SILDOLJE- OG SILDEMELINDUSTRIENS FORSKNINGSINSTITUTT NORWEGIAN HERRING OIL AND MEAL INDUSTRY RESEARCH INSTITUTE Kjerreidviken 16, N-5141 Fyllingsdalen, Norway Telephone +47 55 50 12 00 - Fax +47 55 50 12 99 E-mail:office@ssf.no

Improved methods for analysis of fatty acid

isomers

Svein Are Mjøs & Jan Pettersen

April 2001

This project was financed by:

IFOMA (International Fishmeal and Oil Manufacturer's Association) London, UK (IFOMA project C13E)

Denofa, Fredrikstad, Norway

Margarinfabrikken Norge, Oslo, Norway

Norwegian Research Council, Oslo, Norway (NFR project 117234/112) Norsildmel, Bergen, Norway

Contents 1) Introduction

2) Nomenclature

3) Catalytic hydrogenation

4) Analytical methods, fundamental concepts

4.1) Capillary GC of fatty acid derivatives

4.2) Mass spectrometric detection

4.2.1) Mass spectrometry (MS)

4.2.2) MS of fatty acid methyl esters

4.2.3) MS of picolinyl and DMOX derivatives

4.2.4) Derivatisation of double bonds

4.2.5) Geometric isomerism

4.3) Infrared detection

4.3.1) Calculating chromatograms from spectra

4.3.2) Instrumental design and properties

4.4) Silver Ion Chromatography

4.5) Multivariate methods

4.5.1) The nature and representation of multivariate data

4.5.2) Matrixes and vectors

4.5.3) Principal component analysis (PCA)

4.5.4) Multivariate regression techniques

4.5.5) Variable pre-treatment

4.5.6) Noise and noise reduction techniques

5) Method development

5.1) HPLC separation

5.1.1) System description

5.1.2) Mobile phase selection

5.1.3) Temperature dependence

5.1.4) Elution profiles of hydrogenated fats

5.1.5) Control of split stability

5.2) Gas chromatography, choice of columns.

5.2.1) Introduction

5.2.2) Elution profiles

5.2.3) Chromatographic overlap, HPLC fractions

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5.2.4) Column efficiency

5.2.5) Summary

5.3) Derivatives for GC-MS

5.3.1) Introduction

5.3.2) Picolinyl derivatives

5.3.3) DMOX derivatives

5.3.4) Fatty acid methyl esters (FAME)

5.4) Infrared detection

5.4.1) Studies on saturated FAME standards, CH signals

5.4.2) Number of double bonds

5.4.3) Prediction of cis-trans ratio in mixed peaks

5.4.5) Variations in several parameters, PLS regression

5.4.6) Separation of cis and trans isomers, linearity and limits of detection

5.4.7) Optimal detection parameters, optical resolution and coadd factor

5.4.8) The IR-spectra of non-methylene interrupted fatty acids

5.4.9) Chromatographic response and response factors

5.5) Rapid GC-IR method - Validation

5.5.1) Chromatographic quantification

5.5.2) Spectroscopic parameters

5.5.3) Calculation and validation

5.6) The Mass spectra of FAME

5.6.1) Detection of trans isomerism by mass spectra of FAME

5.6.2) Double bond position in monoenes

5.6.3) Double bond positions in dienes

5.6.4) Summary

5.7) Identification by changes in ECL values

5.7.1) Introduction

5.7.2) Application of the method

5.7.3) Trans fatty acids

5.7.4) Application on hydrogenated fats

5.7.5) Application on conjugated isomers, CLA

5.7.6) Application on SP-2560

5.7.7) Summary and possible improvements

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6) Results section

6.1) Quantitative results achieved by rapid GC-IR

6.1.1) Analytical conditions and quality of the results

6.1.2) Description of samples, product overview

6.1.3) Difference between the products, overview

6.1.4) The influence of the raw oil composition

6.1.5) The difference between samples from factory A and factory B

6.2) Detailed studies

6.2.1) Identification and quantification

6.2.2) The composition of sample C1

6.2.3) The composition of sample C2

6.2.4) The composition of sample A31'

6.2.5) The composition of sample A39'

6.2.6) The composition of sample A51

6.2.7) Trends in the monoene distribution by increasing hydrogenation

6.3) Comparison of the two methods

7) Summary and concluding remarks

8) Appendices:

8.1) Silver ion HPLC fractionation method

8.2) FAME derivatisation method

8.3) Picolinyl derivatisation method

8.4) DMOX derivatisation method

8.5) Cis to trans isomerisation by PTSA

8.6) Column parameters applied in section 5.2

8.7) GC-IR Instrument parameters.

8.8) GC-MS Instrument parameters, method applied in section 6.2

8.9) GC-MS Instrument parameters, section 5.7

8.10) List of ECL values of analysed isomers on BPX-70

8.11) List of ECL values of analysed isomers on SP-2560

8.12) PHFO tables, section 6.1

8.13) List of abbreviations

9) Reference list

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1) Introduction The project work started in December1996 and was terminated in May 2000. During this period of time several publications have appeared about trans-isomeric fatty acids dealing with nutritional and analytical problems.

Recently, Katan published a review concerning dietary effects of trans fatty acids on plasma lipoproteins. The conclusion was that yet there is much work to do in the case of health, nutrition and trans fatty acids (Katan 2000). Thus, there is still controversy on the nutritional effect of trans fatty acids and hydrogenated fats in general (Stanley 1999; Hayakawa et al. 2000). However, the general attitude and nutritional policy in several countries are that due to negative health effects; the consumption of trans fatty acids should be restricted. It has been argued that trans fatty acids always have been a part of the diet due to the fact that the fats of ruminant meat and milk products contain up to 5% trans fatty acids (Ackman 1997); that only special isomers are of concern on the effect of lipoprotein assembly and metabolism (Matthan & Jones 1999); and that we cannot conclude that the intake of trans fatty acids is a risk factor for coronary heart disease (ASCN/AIN Task Force on Trans Fatty Acids 1996). On the other hand it has been argued that the incidences of breast cancer increase among women with high dietary intake of trans fatty acids and low intake of PUFA (Jones 1997).

Determination of the composition of trans fatty acid isomers in human milk lipids is a convenient mean to estimate trans consumption and the dietary sources of the trans-isomers of the corresponding population (Wolff et al. 1998; Bouré et al. 2000; Chen et al. 1997). Also the analysis of trans-isomers of 18:1 in human blood serum seemed to reflect reliably the dietary intake, although it was shown that a moderate intake resulted in decreased conversion of linoleic acid to its more unsaturated and long-chain metabolites (Vidgren et al. 1998).

Some publications concern partially hydrogenated fish oil (PHFO). Thus Molkentin & Precht (1997) concluded that the appearance of trans-16:1 in human adipose tissue and plasma reflects the intake of PHFO, although they focused the uncertainties that may arise if the analysis was done without Ag+-HPLC pre-fractionation. In an experiment young men were fed PHFO, partially hydrogenated soybean oil (PHSBO) and butter, respectively. The results showed that the haemostatic variables were more favourable for the men receiving PHFO than that of the PHSBO and butter groups (Almendingen et al. 1996). However, in another experiment it was shown that replacing PHFO with PHSBO gave beneficial effects on the composition of the plasma lipids (Müller et al. 1998).

In animal experiments; it was shown that the degree of hydrogenation of PHFO influenced enzymatic activities in rat liver microsomes (Morgado et al. 1998); that trans monoenes of partially hydrogenated vegetable oil (PHVO) did not affect the prostaglandin synthesis in rats (Mahfouz & Kummerow 1999); that moderate amounts of trans fatty acids reduced the formation of PUFA in rat organs; that trans fatty acids affected the essential fatty acid concentration of rat milk (Larquè et al. 2000); that trans-isomers of 18:2 and, in particular trans 18:3-isomers is desaturated and elongated in rat liver samples (Chardigny et al. 1997; Bretillon et al. 1998); and that fully hydrogenated soybean oil had poor digestibility and produced lean rats (Kaplan & Greenwood 1998).

Dietary effects of conjugated linoleic acid (CLA) have been focused during the last decade. Nutritionally, the most interesting isomers of CLA contain one trans and one cis double bond in conjugation in the molecular C-chain. Several studies with animals have shown beneficial nutritional effects of CLA. In experiment with mice fed CLA, human prostatic cancer cells produced smaller regional tumours compared to mice fed the control diet (Cesano et al.

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1998). Mammary carcinogenesis seemed to be modified by CLA and reduced mammary cancer risk (Ip & Scimeca, 1997; Thompson et al. 1997). Moreover, it has been demonstrated in animal experiments that dietary CLA causes effects on the body composition; i.e. produced lean mice (Park et al. 1997) and pigs (Dugan et al. 1997). This has also been shown in experiments with humans (Blankson et al. 2000), although in another experiment no effect on the body composition was shown (Zambell et al. 2000). The reason for this controversy might be differences in the level of CLA and types/compositions of CLA-isomers that were ingested. Furthermore, dietary trans fatty acids has been shown to increase the level of CLA in human serum (Salminen et al. 1998).

The general suspiciousness or/and uncertainty about nutritional health effects of trans fatty acids have caused authorities of some countries to make laws to restrict the content of trans fatty acids in nutrients (e.g. EU). Consequently, there is a demand to perform analyses for trans fatty acids in the different kind of foods that contain fat components. Although the lack of a complete database for trans fatty acid level in foods the intake of trans-isomers has been calculated. In USA in 1997 it was published that estimated intake of trans fatty acids per capita per day ranged between 2,6 and 12,8 grams. High-range estimates were drawn from availability or disappearance data, low range-range estimates from analysis of self-selected diets (Lichtenstein 1997). In Germany the average intake of trans fatty acids was analysed by capillary gas chromatography (GC) and estimated to be 1,9 grams and 2,3 grams per day and capita for women and men, respectively (Fritsche & Steinhart 1997).

The content of trans fatty acids in the German margarines (analysed by capillary GC) varied between 0,2% and 4,9% and since 1994 the margarine trans-content has decreased significantly (Fritsche & Steinhart 1997b). In Bulgary the content of trans isomers in margarines was analysed by GC on capillary and packed columns, and the contents of trans 18:1 and 18:2 trans-isomers were within 1,9 – 8,0% and 0,4 – 1,4%; respectively (Tsanev et al. 1998). In Denmark the analysed content (capillary GC) of trans 18:1 in margarines and shortenings varied between 0,4% and 9,0%; while margarines and shortenings with high content of long-chain fatty acids, i.e. appearing from PHFO, had about 20% total trans monoenoic of which close to 50% were made up of trans long-chain fatty acids (e.g. trans 20:1 and trans 22:1) (Ovesen et al. 1998). Both fat-foods and frying fats contain trans fatty acids and the content were in the range of 16 – 25% in Denmark (Ovesen et al. 1998), while that of fried foods in UK, analysed by infra-red spectroscopy (IR), were within the rage of 2 – 34% of the fat (Church, 1997). Trans fatty acid content of Danish beef, veal and lamb fat also contained trans 16:1 ranging within 0,1-0,8%, and trans 18:1 ranging between 1-5% of the fat. The analyses were done by capillary GC and argentation-thin-layer chromatography (Ag+-TLC) (Leth et al. 1998). The total content of trans isomers of 18:1, 18:2 and 18:3 in Spanish margarines was in the range of 0,2-20%; 0,2-1% and 0-0,5%; respectively. The analyses were performed by two different gas chromatography procedures, i.e.; one procedure included pre-fractionation by Ag+-TLC (Alonso et al. 2000).

Also unhydrogenated food oils contain trans fatty acids. Geometrical isomers are formed at elevated temperatures, e.g. during the deodorization of oils at temperatures above 190°C. In average, 17% of α-linolenic acid (18:3n-3) found in commercial oils have been shown converted to trans-isomers (Wolff 1997; Martin et al. 1998). Thus, in Mexico seventy-two percent of vegetable oil samples obtained from 18 different oil refining factories contained more than 1% trans fatty acids. The analyses were performed by capillary GC (Medina-Juárez et al. 2000).

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In USA there is an ongoing process to make rules that regulate for labelling the content of trans fatty acids in nutrients. The new rules that are proposed require labelling for trans fatty acids if the content is more than 0,5 grams per serving (Katan 2000).

The regulations and declaration of the trans fatty acid content in nutrients have caused a request for a database on trans fatty content in foods consumed in different countries. Thus the analytical problems that arise performing trans fatty acid analyses have been focused in several publications. IR-spectroscopy for detection of double-bond trans-geometry, and GC-analyses for quantification of the respective trans-fatty acid isomers is the most commend analytical methods used. The IR-spectroscopic method has been tested and the sensitivity improved by attenuated total reflection (FTIR) (Sedman et al. 1997) and optimised (Mossoba et al. 1996; Ratanayake & Pelletier 1996). Due to the improved sensitivity the FTIR-spectroscopy can be used to determine the trans-fatty acid content in unhydrogenated, refined oils (De Greyt et al. 1998), although the lower limit of quantification is set 1% (Adam et al. 2000). The iodine-value (IV), i.e. the total amount of double bonds of fats and oils, were determined by FTIR-spectroscopic analysis (performing analyses for both cis- and trans- geometry) and the by capillary GC. The results showed good correlation for IV-analyses, although the trans contents determined by GC were significantly higher than that of FTIR-analyses (Sedman et al. 1998). In another work the differences of the trans content determined by FTIR and GC were inconsistent, i.e. the GC-values were higher and lower that of the respective FTIR-results (Adam et al. 1999).

Some articles show improved quantitative analysis of the different isomers formed during the hydrogenation of oils. Thus, Kitayama et al. (1998) identified, by well-resolved peaks on GC-analysis, ten species of 18:1 isomers (positional and geometrical) with a new coating on the capillary column, and Mossoba et al. (1997) found and identified nine 18:1 isomers (positional and geometrical) by silver-ion high performance liquid chromatography (Ag+-HPLC) accompanied by gas chromatography based on mass-spectrometry (MS) and FTIR-detection. Also Aro et al. (1998) improved the analyses of 18:1 isomers by making a combined analyses of DMOX (4,4-dimethyloxazoline-) and methyl esters fatty acid derivatives. Moreover, DMOX-derivatives were formed for analysing the content of 16:1 cis/trans isomers that appear in PHFO. Frequently, 16:1 isomers have been overestimated due to overlap with C17 fatty acids (Precht & Molkentin 2000). Separation and quantification of different trans-monoenoic isomers found in PHFO have been shown by a method based on silver nitrate thin layer chromatography, doing analyses for 20:1 and 22:1 trans isomers in margarine and in human adipose tissue (Wilson et al. 2000).

In general, this overview of publications that appeared during the last 3-4 years concerning trans-isomeric fatty acids demonstrates the needs for an improved quantitative and qualitative analysis to determine the fatty acid isomers that are formed during processing; e.g. hydrogenation and/or deodorization. In particular, dealing with partially hydrogenated fish oil (PHFO) the multiplicity and complexity of fatty acid isomers formed are huge. This work focus on identification and quantitative determination of geometrical isomers appearing in PHFO, but also analyses of fatty acid isomers formed in partially hydrogenated vegetable oils have been done.

Three different methodologies have been applied. In principle they are based on, respectively; pre-separation and fractionation by liquid chromatography (Ag+-HPLC) followed by GC-analyses with MS- and FTIR- detection; direct GC-analysis by FTIR-detection and mathematical treatment of the data; and direct GC-analysis based on MS-detection and multivariate data treatment of the retention times.

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2) Terms and nomenclature applied Several types of fatty acid nomenclature are used in the literature. Nomenclature used in this report will be explained using the structures given in Figure 2-1 as examples.

Figure 2-1a shows the nomenclature and structure of arachidonic acid. All double bonds are of cis geometry and separated by one methylene group (or two single bonds). The nomenclature describes the number of carbons, the number of double bonds and the position of the first carbon in the first double bond counted from the methyl end of the carbon chain, denoted by "n-6". In the literature the n-6 position is often referred to as "omega-6" or "ω6" position. When this nomenclature is used on di or poly unsaturated fatty acid isomers, e.g. "n-6 fatty acids", it is assumed that all double bonds are separated by one methylene group. If the geometry of the double bonds is not given, all bonds are of cis geometry. When the reference is to specific double bond positions, or groups of isomers, e.g. "n-9 double bonds", no assumptions are made about position or geometry of the rest of the double bonds in the molecule. The isomer shown in Figure 2-1b thus has an n-6 double bond, even though it is separated from the next bond by more than one methylene group.

Alternatively double bond positions may be counted from the carbonyl carbon and is then often denoted by "∆" or "d". When this nomenclature is used, all positions in the molecule are given and the double bond geometry is denoted by letter "c" or "t" after the position, as shown in Figure 2-1d. In the cases where all double bonds have the same geometry, the terms all-cis or all-trans may be used. If the geometry of the double bonds is not given, the fatty acid is the all-cis isomer.

In this report, references are to ∆-positions when the positions are written in front of the chain length and number of double bonds, e.g. "9,12,15-18:3" means "18:3 ∆9,12,15" or "18:3n-3". Note that the "∆" letter is not shown, this will only be used in the text when it is necessary to clarify from which end of the molecule the positions are given.

In the cases of methylene-interrupted double bond systems with trans fatty acids, double bond geometry may be given as shown in Figure 2-1e, where the geometry is listed from the carbonyl end to the methyl end of the carbon chain.

When double bonds are separated by three or more single bonds, Figure 2-1b, the term isolated double bonds is used. Alternatively the term non-methylene interrupted (NMI) is used. Figure 2-1d shows a conjugated double bond. Note that the term NMI does not include conjugated double bonds.

Fatty acids are sometimes denoted by their systematic IUPAC names, e.g. 20:4n-6 is referred to as 5,8,11,14-eicosatetraenoic acid, 7c,11t-18:2 may be referred to as c7,t11-octadienoic acid. Common names as arachidonic acid (20:4n-6) or abbreviations for IUPAC names, e.g. EPA for 20:5n-3 or DHA for 22:6n-3 are sometimes used. When the letter "C" is used to identify the number of carbons in the carbon chain, e.g. "C16" or "C18", it refers to the group of fatty acids with the given number of carbons in the chain - irrespective of the number of double bonds.

Some attention should also be paid to the term isomer. Isomers refer to molecules with the same molecular formula. Thus the 18:2 fatty acids pictured in Figure 2-1 b, c and d are isomers with the molecular formula C18H32O2, however, the 18:3 fatty acid shown in Figure 2-1e is not an isomer of these, since the corresponding formula is C18H30O2.

Isomerism can be divided into structural isomerism and stereo isomerism. Structural isomers differ from each other in the arrangement, or connectivity, of their atoms. Thus 18:1n-9 and 18:1n-7 are structural isomers. In the case of unsaturated fatty acids, this form of isomerism is

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commonly referred to as positional isomerism, because the two molecules differ only in the position of the double bond. Other forms of structural isomerism arise when branches or rings are introduced in the molecule. Thus n-15:0 and iso-15:0 are also structural isomers.

Stereoisomerism occurs when the molecules have the same structural isomerism but differ in the arrangement of the atoms in space. Cis-trans isomerism, is a type of stereoisomerism that is commonly referred to as geometric isomerism. Thus cis 18:1n-9 and trans 18:1n-9 are geometric isomers (and stereoisomers).

Other types of stereoisomerism arise with the introduction of chiral centers, which are carbon atoms that are connected to four different groups. Enantiomers, which have at least one chiral center, are mirror images of one another. Enantiomers have similar physical properties and differ only in the rotation of polarised light. In fatty acids, this form of isomerism is uncommon and can only occur if branches, rings, hydroxy groups or other groups are introduced in the fatty acid chain.

The term homologues should not be confused with isomerism. Homologues are molecules that have similar structure, but differ by the number of methyl groups in the carbon chain. Thus 16:0, 17:0, 18:0, 19:0 and 20:0 are homologues in a homologue series. Homologue series are important in the identification of fatty acids by spectra or by chromatographic retention times.

Figure 2-1)

Overview over fatty acid structure and nomenclature

CO

∆12c∆7c

HO

CO

∆14c∆11c∆8c∆5c

HO n-6

OHO

CO

cistranstrans

HO n-3

a) 20:4n-6

b) 7,12-18:2 (18:2 ∆7,12)

c) 7,9-18:2(18:2 ∆7,9)

d) 7c,11t -18:2(18:2 ∆7c,11t)

e) 18:3n-3 ttc(9t,12t,15c-18:3)

C∆9c∆7c

HO

CO

∆11t∆7c

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3) The hydrogenation process Catalytic hydrogenation is an industrial process that has found widespread use in chemical manufacturing. This usually involves saturation of double bonds in alifatic or aromatic systems, but also the reduction of functional groups, e.g. aldehydes to alcohols, nitriles to amines or acids to alcohols. Catalysts used are usually noble metals, (Pt, Pd, Rh, Ru) or base metals (Ni, Co, Cu) (Murthy 1999). Only nickel has found widespread use in catalytic hydrogenation of edible oils (Coenen 1976).

3.1) Process design Hydrogenation of edible oils is usually a batch process, taking place in a closed reactor. Catalyst and oil is heated to the necessary reaction temperature, and hydrogen is added. Hydrogen is suspended in the oil by vortexing. The final product is a result of several parameters where the most important are: Initial properties of the oil to be hydrogenated, type and amount of catalyst, reaction temperature, hydrogen pressure, degree of vortexing, reaction time and reactor design. An overview over limiting conditions in the various reaction steps is found in Murthy (1999).

3.2) Hydrogenation reactions Theories suggest that there are two types of hydrogenation reactions taking place in the catalytic hydrogenation of double bonds. Monoenoic or isolated double bonds are hydrogenated via the so-called half hydrogenated state (HHS) described by Horiuti & Polanyi (1934) and often referred to as the Horiuti-Polanyi mechanism.

Methylene interrupted polyenes are also hydrogenated by the π-allylic mechanism (PAM). This is a two step reaction involving conjugation of the double bonds as the first step and hydrogenation of the conjugated isomer as the second step (Rooney et al.1960). Kinetics for the two mechanisms depends on type of catalyst and other reaction conditions, but the reaction rates for the PAM is usually significantly higher than the reactions via the HHS. In a hydrogenation process dominated by the PAM this would lead to high consumption of methylene-interrupted polyenes and accumulation of monoenes and polyenes with isolated (non-conjugatable) double bonds (Koritala et al. 1973) Rate of formation of 18:1 from 18:2 is typically 5-100 times larger than rate of formation of 18:0 from 18:1 (Bailey & Fisher 1946, Coenen 1976).

3.3) Isomerisation and formation of new double bonds Both the HHS and PAM involve initial steps where the double bonds are adsorbed by the catalysts. These are reversible reactions, and desorbtion without hydrogenation occur and leads to new double bonds. These new double bonds are not necessarily formed with the same geometric configuration as the original bonds, which leads to the formation of trans double bonds. The new double bonds may also be formed in positions adjacent to the original double bonds, leading to positional isomerisation.

Thermodynamically a trans double bond is favoured over a cis double bond, which could lead to accumulation of trans double bonds above 50% in the products. Several model studies has indicated that a kind of equilibrium between cis and trans double bonds is reached as the cis/trans ratio approaches 2:1, but other values has also been reported (Albright and Wisniak, 1962). By isomerisation with p-toluenesulfinic acid in dioxane equilibrium was found to be close to 80% trans, 20% cis (see appendix 8.5). However, these reaction conditions were

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very different than those found in catalytic hydrogenation. The hydrogenation of edible oils is irreversible and does not proceed to equilibrium, but is controlled by kinetics i.e. reaction time, temperature, etc.

The amount of trans double bonds formed are determined by several factors where the most important are:

• The degree of hydrogenation over isomerisation. A high degree of hydrogenation over isomerisation may give products with the desired iodine value and physical properties before the cis/trans equilibrium is reached, thus giving products with low trans values.

• Selectivity: Both the rate of destruction (hydrogenation) of double bonds and the rate of formation of new double bonds (isomerisation) may be different for cis and trans double bonds. This is discussed below.

Regarding positional isomerisation, it has been shown that the double bonds migrate at about equal speed in both directions along the carbon chain, steric effects may alter this pattern when the double bonds are in positions close to the carbonyl group (Sebedio et al. 1975)

In the π-allyl mechanism geometric isomerisation does not take place without simultaneous positional isomerisation. Thus we can not expect high levels of isomers with trans bonds in the original double bond positions of the polyenoic fatty acids. Sebedio and Ackman (1983b) have also confirmed this in experimental studies on fish oil.

3.4) The rate of hydrogenation over isomerisation. As discussed above the rate of hydrogenation over isomerisation is important for the outcome of the process. Several factors may influence the hydrogenation/isomerisation ratio. The most important parameters is the availability of dissolved hydrogen at the catalyst surface, and in general, the following rules can be applied:

• Increasing hydrogen pressure leads to increased hydrogen concentration over the catalyst, hydrogenation is favoured.

• Increasing vortexing (to a certain degree) reduces the mass transfer resistance and increases the amount of dissolved hydrogen, hydrogenation is favoured.

• Increasing the amount of catalyst decrease the amount of available hydrogen per catalyst surface area, thus isomerisation is favoured.

• Increasing the temperature leads to higher reaction rates and higher consumption of hydrogen, thus the hydrogen concentration at the catalyst surface is decreased and isomerisation is favoured.

The above rules for isomerisation versus hydrogenation all depends on the availability of hydrogen after the double bond has reacted with the catalyst in the initial hydrogenation steps. It is important to note that in most batch hydrogenation processes the hydrogen pressure is not constant. Often hydrogen is added in the start of the process and the pressure is gradually reduced as hydrogen is consumed, thus leading to high selectivity conditions at the end of the process. At the end of the process when hydrogen pressure is reduced to zero, isomerisation of double bonds may continue as long as the catalyst is still present in the oil and reaction temperature is sufficient. This may influence the end product (Wells and Wilson 1967).

The nature of the catalyst is also of importance for the outcome of the process. Poisoning of the catalyst surface by e.g. sulphur compounds will alter the catalyst properties in favour of isomerisation (Drozdowski and Szukalska 2000).

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3.5) Reaction rates and Selectivity The hydrogenation reactions generally follow first order reaction rates, but these reaction rates are rarely equal for all fatty acid isomers (Albright and Wisniak 1962). This difference in reactivity is usually referred to as selectivity. Several different kinds of selectivity exist (Dijkstraa, 1997):

• Selectivity for PAM over the HHS mechanism

• Cis/trans selectivity

• Selectivity for double bonds in certain positions in the fatty acid chain - may usually be neglected (Allen, 1964) and will not be discussed further.

• Positional selectivity. The rate of hydrogenation of an unsaturated fatty acid may depend on its position in the triglyceride molecule

• Triglyceride selectivity. The rate of hydrogenation of an unsaturated fatty acid may depend on the identity of the other fatty acids in the molecule.

High selectivity in a process is in general favoured by the same reaction conditions that favour isomerisation over hydrogenation (Bailey 1949), low availability of hydrogen at the catalyst surface leads to selective hydrogenation.

3.6) Selectivity for PAM over HHS This selectivity is highly dependent on type of catalyst, but also on reaction conditions. Koritala (1973) compared several metals with nickel catalyst at various conditions in a mixture of 18:2 and 18:1. Copper-chromite was found to be nearly 100% selective for methylene interrupted dienes, no monoenes was hydrogenated until all dienes had been consumed. Platinum catalyst showed nearly no selectivity while nickel and palladium were intermediates between the two extremes. At high reaction temperature and low pressure (195°C/8psi) the catalyst showed higher selectivity for dienes than at reduced temperature and increased pressure (100°C/30psi). There are several types of nickel catalysts used and the selectivity of these differs.

In a recent experiment, List et al. (2000) found that the hydrogen pressure had large effects on the selectivity when soybean oil was hydrogenated over nickel catalyst. Hydrogenation to approximately the same iodine values at 50 and 500 psi gave different products indicating reactions with high selectivity at 50 psi and low selectivity at 500 psi.

The selectivity for PAM over HHS is critical for the outcome of the process. With high selectivity, reaction rates for polyenes will depend not only on the number of double bonds, but also on the availability of the double bonds to form conjugated intermediates. Dienes with isolated double bonds was found to have reaction rates only slightly higher than monoenes (Koritala et al. 1973). It is important to note that not only hydrogenation of monoenes is suppressed in the presence of conjugatable polyenes, but also the rate of isomerisation.

In a hydrogenation process for fish oil with selectivity for PAM one should thus expect the following:

• All polyenes and dienes originally present have methylene-interrupted double bonds and are thus hydrogenated at high rate. The amounts of these isomers in the end products will therefore be very low.

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• Among the products formed, isomers with isolated double bonds will have lower reaction rates than isomers with methylene interrupted isomers. Thus polyenes with isolated double bonds will accumulate.

• The cis monoenes originally present show low affinity for the catalyst and will therefore be protected from both hydrogenation and isomerisation as long as the PAM dominates. Thus original cis isomers should be present in high amounts in less hydrogenated products.

• Although difficult to prove by experiments, the trans monoenes formed are probably products from the hydrogenation of dienes and polyenes and not from isomerisation of cis monoenes.

A study on hydrogenated fish oil (Sebedio et al., 1981) showed increasing levels of none methylene interrupted dienes (NMID) and trienes (NMIT) as the iodine value was reduced from 119 to 79. The original polyunsaturated fatty acids were completely vanished at IV 79. The amount of NMIT did not decline until IV 88 was reached.

It should be emphasised that the higher reaction rates for polyenes compared to monoenes can also be explained by HHS theory only, without involving the theories of PAM (Dijkstraa, 1997)

3.7) Cis/trans selectivity: Cis/trans selectivity is difficult to investigate because it involves two different processes. Both the consumption of double bonds by hydrogenation and the formation of new double bonds may have elements of selectivity for cis or trans and it is difficult to distinguish between the two. During the hydrogenation process the relative amount of trans to cis isomers increases until the cis/trans equilibrium is reached. Once this equilibrium is reached cis and trans double bonds are apparently consumed in the same rate by hydrogenation, but it is important to note that the equilibrium is both a function of the hydrogenation and the simultaneous isomerisation from cis to trans, and from trans to cis.

The trans/cis equilibrium is usually around 60-70% trans, but results with large variations have been reported (Albright and Wisniak, 1962, Dijkstraa 1997). Van der Planck and van Oosten (1975) found that cis double bonds showed slightly more affinity for the catalyst surface than trans double bonds. This kind of catalyst selectivity may therefore lead to some accumulation of trans isomers. Trans isomers are also thermodynamically more stable than cis isomers.

3.8) Positional and triglyceride selectivity Most studies mentioned above are neglecting the possible effects of the triglyceride molecule when the theories of hydrogenation are outlined. A review of published results is given in Dijkstraa (1997) who concludes that positional selectivity (the fatty acid's position in the triglyceride molecule) may be neglected for practical purposes while the triglyceride selectivity may be more important.

Unsaturated fatty acids with short-chain fatty acids in adjacent positions in the triglyceride molecule tend to be hydrogenated at a higher rate than unsaturated fatty acids adjacent to larger molecules. The choice of catalyst seems to be of importance, catalysts with small pores tend to favour triglyceride selectivity more than catalysts with large pores.

- 13 -

Most studies of triglyceride selectivity have been carried out on designed triglycerides or on vegetable oils with a simple triglyceride composition. How triglyceride selectivity may effect the outcome of fish oil hydrogenation is not known.

3.9) Side reactions The formation of intermolecular reactions can not be ruled out. Difference between Wijs iodine value and that calculated by GC has been reported. This is possibly caused by double bonds in fatty acid dimers that will contribute to the Wijs value, but not to GC-value (Ackman and Eaton 1966).

3.10) New techniques The above rules for trans-formation generally refer to the traditional nickel catalysed batch process used in hydrogenation of edible oils. Some new techniques are emerging which may lead to products with significantly lower trans-content.

Electrocatalytic hydrogenation (Yusem and Pintauro 1992, Hong et al. 1999, Warner et al. 2000) is one possible solution. In electrocatalytic hydrogenation the oil is mixed with a proton containing solvent (e.g. water). The nickel catalyst is the electrode in the electrochemical reaction. Atomic hydrogen is produced directly on the catalyst surface and reacts readily with the unsaturated double bonds in the fatty acids. Thus the rate limitations caused by low solubility of gaseous hydrogen in the oil is circumvented.

The rate limitations caused by hydrogen solubility have also been solved by hydrogenation in supercritical propane. Both hydrogen and the oil are highly soluble in the propane phase. PHVO with very low trans values has been achieved by this method (Macher et al. 1999)

An alternative to partially hydrogenated fats with high trans content is blending or interesterification of highly hydrogenated fats with unhydrogenated edible oils (List et al. 1995)

- 14 -

4) Analytical methods - Fundamental concepts

4.1) Capillary GC of fatty acid derivatives Retention of fatty acid derivatives on polar GC-columns are generally determined by the molecules boiling point and the number and position of double bonds in the carbon chain. In isothermal GC-runs the logarithm of the retention time of the saturated FAMEs and the number of carbons in their carbon chain show a linear relationship (James 1959). Retention of FAME are often described by ECL-values (equivalent chain length).

In the original articles by Miwa et al. (1960) and Woodford and van Gent (1960) isothermal GC programs are used and the logarithms of the retention times are converted to ECL values by setting the ECL values of the saturated fatty acids equal the number of carbons in the fatty acid carbon chain, thus retention for other isomers are given on an ECL scale rather than time scale. ECL values for the different fatty acids mainly depend on the stationary phase used, and are therefore used to compare retention data where the other chromatographic parameters (temperature program, column length, column flow) vary. In this work linear temperature programs are used and ECL values are found by direct non-linear calibration of the retention times, - no logarithmic transformation.

FCL (fractional chain length) is the difference in ECL value between an unsaturated isomer and the corresponding saturated fatty acid (Jamieson 1975). FCL values for fatty acids in a homologue series, e.g. 16:1n-9, 18:1n-9, 20:1n-9 has been shown to have similar values (Ackman and Burgher 1963). FCL values of NMI dienes has also been shown to equal the sums of the FCL values for the two corresponding monoenes (Sebedio and Ackman 1982). ECL values of the natural fatty acids are frequently reported in the literature, numerous more and less successful methods has been used to calculate FCL/ECL-values for isomers that are not available as standards (Ackman and Hooper 1973, Grahl-Nielsen 1996, Mjøs 1996, Stransky et al. 1997)

Generally retention times increase in positional isomers at the double bond is located further apart from the carbonyl group, both in monoenes (Gunstone et al. 1967, Barve et al. 1972, Christie 1988, Thompson 1997b) and in polyenes (Christie 1968, Christie 1988), with some deviations when the double bond is located close to the carbonyl group, i.e. ∆2-4. The n-2 and ∆3/2 positions deviates from the regular pattern by having significantly higher ECL values than isomers with double bonds in nearby positions. Investigations on dienes have mainly been carried out on the methylene-interrupted isomers (Christie 1989). Less work has been done on the large number of NMI dienes, where the retention times will not only be determined by the position and stereochemistry of the double bonds, but where the distance between the double bonds will be an additional factor (Christie 1988). A simple model for the structure-retention relationship for unsaturated fatty acids is proposed by Beumelle and Vial (1987)

It should be emphasised that the very polar GC-columns used today (CP-Sil 88, SP-2560, BPX-70) with cyanopropyl groups may have different properties than the stationary phases in the rather old publications cited above.

FCL values for methylene interrupted dienes are generally higher than the sum of the FCL values for the two monoenes with double bonds in the same position. In Christie (1988 and 1989) this is explained by homo-conjugation or interaction between the stationary phase and the diallyl methylene group. For dienes with isolated double bonds this difference has been found to be small (Sebedio and Ackman 1982).

- 15 -

Some examples of good resolution between positional isomers of C18 and C16 monoenes are found in the literature (Kitayama 1988, Ratanayake et al. 1990, Ratanayake and Pelletier 1992, Molkentin and Precht 1995, Duchateau 1996, Mossoba 1997, Thompson 1997, Aro 1998, Wolff 1999). However these resolutions are obtained with low temperature isothermal programs, or near isothermal programs in temperature ranges 150-160°C. Such low temperatures would cause problems with the elution of the long chain isomers, which in hydrogenated fish oil is more important than short chain isomers. Thus the temperature programs are always a trade-off between resolution at different chain lengths, and maximum elution time. Theoretically each sample could be analysed several times using temperature programs optimised for each chain length – However, the marginal improvements in resolution possibly obtained with this strategy might not be worth the additional costs. In general it is more difficult to achieve good separation of C20 and C22 compared to the shorter fatty acids. The higher molecular weight gives slightly worse chromatographic properties: In addition, the number of isomers increases with increasing chain length.

The picolinyl and dimethyloxazoline (DMOX) derivatives used for mass spectrometric determination of double bond positions generally show the same relative elution patterns as FAMEs. However picolinyl esters are more difficult to separate because of the higher molecular weight (Christie and Stefanof 1987). The higher elution temperatures, approximately 50°C higher than FAME may also give problems with the maximum temperature for some columns (Dobson and Christie 1996)

Several GC-columns are used in this study. All are of the fused silica type. Polyethyleneglycol (PEG) and polysiloxane with various polar substituents are used as stationary phase. More details about columns are given in section 5.2.

4.2) Mass spectrometric detection

4.2.1) Mass spectrometry In mass spectrometry with electron impact ionisation the molecules in gas phase are bombarded with high-energy electrons and form radical cations. The radical cations are usually unstable and will decompose in the detector; the rate of fragmentation usually depends on the molecule's ability to stabilise the positive charge. The resulting fragments are separated by their mass to charge ratio (m/z) in an electric field. The charge is usually +1, and thus their masses can be known. The molecular weight can be told from the molecular ion, and the fragmentation pattern can give important information about isomerism.

4.2.2) Mass spectrometry of fatty acid methyl esters The number of carbons and number of double bonds can easily be determined from high quality mass spectra of FAMEs, at least for isomers with zero to three methylene-interrupted double bonds. The molecular ion is usually seen, and in addition the lower masses show characteristic patterns. Examples of mass spectra of common C18-fatty acids are given in Figure 4.2-1 a to e. The most important ions are listed in Table 4.2-1.

In saturated fatty acids, fragmentation is usually dominated by the McLafferty rearrangement (Table 4.2-1) giving the base peak at m/z 74. The peaks at m/z 87, 143, 199, 255 (5-8 in the table) arise from loss of the neutral aliphatic radicals with chain lengths C3-C15 (Odham and Stenhagen 1972). The molecular ion is seen at m/z 298 in Figure 4.2-1a. Cleavage at the carbonyl carbon and loss of methoxy radical gives the peak at 267 (3 in table) (Odham and Stenhagen 1972). Any methyl branches are normally easy to locate (Ryhage and Stenhagen 1959)

- 16 -

In linear alkenes and unsaturated fatty acid methyl esters double bonds tend to migrate in the molecular ion prior to fragmentation, thus making the determination of double bond position in unsaturated fatty acids uncertain. There are generally no ions that serve to indicate the position or the stereochemistry of the double bond in monoenes (Odham and Stenhagen 1972, Christie 1989). However this does not mean that the spectra of positional isomers of unsaturated FAMEs and other simple esters are exactly identical. For instance Brakstad (1993) showed that the double bond position in monoenoic ethyl esters could be determined by PLS calibration on their mass spectra. Thus, there is also a system in the difference that can be related to the double bond positions. The fragmentation pattern in monoenes is dominated by unsaturated aliphatic radicals with masses 55 (base peak), 69, 83, and 97 (11-14 in Table 4.2-1). The Molecular ion is seen at m/z 296. The more abundant peaks at 264 and to 265, corresponding to loss of methanol and methoxy radical are always abundant and serve as indicators for molecular weight in low quality spectra (Christie 1989). For the sake of simplicity this text refers to fragments as aliphatic and straight chain. The fragments are usually stabilised by formation of cyclic compounds, especially when double bonds are present.

In methylene interrupted dienes a series of diunsaturated aliphatic radicals with masses 67, 81, 95 and 109 dominates the spectrum at low masses. The monoenoic series are also abundant at masses 55 and 69. The molecular ion (m/z 294) is more abundant than in monoenes. Loss of methoxy radical (m/z 263) is also seen. Most of the dienoic spectra found in this study showed molecular ions at m/z 55 and generally look more like monoenes in the lower mass region (see section 5.6.3). This can be explained by suppression of the reaction leading to m/z 67 in non-methylene interrupted dienes. The m/z 67 ion is illustrated in Figure 4.2-2. When the two double bonds are separated by more than two double bonds, the ion can not be formed without double bond migration prior to fragmentation. A mass spectrum of the NMI fatty acid 5,9-18:2 is presented in Christie (2000b). Although m/z 67 still is of high abundance in this spectrum it is significantly suppressed compared to spectra of methylene interrupted fatty acids presented in this source, both m/z 81 and 55 are of higher abundance. The methylene-interrupted spectra of 2,5-, 10,13-, 5,8-, and 13,16-18:2 are presented in Christie and Holman (1967). Among the spectra the 10,13 and 5,8-isomers have m/z 67 as their base peak while the 13,16-isomer (which is methylene interrupted) has a suppressed 67 ion and 55 followed by 74 as the most abundant ions. This may indicate that double bond positions close to the methyl end may give 55 as base peak also in methylene interrupted systems. The spectrum of the 2,5-isomer showed large deviations from the general pattern because of the double bond in the 2-position.

In methylene interrupted trienes a series of trienoic aliphatic fragments dominates the pattern with abundant fragments at 79 (base peak), 93, 107 and 121 (32-35 in Table 4.2-1). The dienoic series are also abundant with fragments at 67, 81, 95 and 109 (36-39 in table). Monoenoic fragments are seen at m/z 55 and 69. In methylene-interrupted trienes (and tetraenes to hexaenes) rules exist for predicting the position of the double bond system in the chain. Both the distances from the methyl end and from the carbonyl group can be predicted from diagnostic ions arising from the cleavage mechanism indicated in Figure 4.2-3. Thus the fragments 108 and 236 in 18:3n-3 and 150 and 194 in 18:3n-6 (table 4.2-1. 41-44) are indicators of the double bond positions (Fellenberg 1987). Investigations on cis/trans isomerised trienes and tetraenes (see section 5.6.1) also revealed that this mechanism (surprisingly) gives important information on cis/trans geometry of double bonds in methylene interrupted polyenes. The same results indicate, although not proved, that one can not expect the spectra of NMI trienes and tetraenes to look like the methylene interrupted analogues. A mass spectrum of a NMI triene (5,9,12-18:3) is presented in Christie (2000b).

- 17 -

This spectrum indeed shows a different pattern in the lower mass region than seen for the methylene interrupted trienes. M/z 79 (usually the base peak) is suppressed and both 67 and 81 is of higher abundance.

Fig. 4.2-1a)

18:0, saturated fatty acid methyl ester

40 60 80 100 120 140 160 180 200 220 240 260 2800

5000

10000

15000

20000

25000

30000

35000

40000

45000

50000

55000

60000

65000

70000

75000

80000

85000

90000

m/z-->

AbundanceAverage of 16.640 to 16.726 min.: N990818A.D

53

55

74

87

97115

129143

157171185 199213 241

255267271

298

Fig. 4.2-1b)

18:1n-9, monounsaturated fatty acid methyl ester

40 60 80 100 120 140 160 180 200 220 240 260 2800

5000

10000

15000

20000

25000

30000

35000

40000

m/z-->

AbundanceAverage of 17.126 to 17.194 min.: N990818A.D

53

55

74

83

97

111

137141 166180

194207

222

235246

264

278296

69

Fig. 4.2-1c) 18:2n-6, diunsaturated fatty acid methyl ester

40 60 80 100 120 140 160 180 200 220 240 260 280 3000

5000

10000

15000

20000

25000

30000

35000

m/z-->

AbundanceAverage of 17.960 to 18.029 min.: N990818A.D

53

67

81

95

109

123135 150 164 178187 205 220 234 251

263

278

294

Fig. 4.2-1d) 18:3n-6, triunsaturated fatty acid methyl ester

40 60 80 100 120 140 160 180 200 220 240 260 280 3000

5000

10000

15000

20000

25000

m/z-->

AbundanceAverage of 18.521 to 18.601 min.: N990818A.D

53

67 79

91

107

121135

150163175 194

207221235243 261 279292

- 18 -

Fig. 4.2-1e) 18:3n-3, triunsaturated fatty acid methyl ester

40 60 80 100 120 140 160 180 200 220 240 260 2800

2000

4000

6000

8000

10000

12000

14000

16000

18000

20000

22000

24000

m/z-->

AbundanceAverage of 19.007 to 19.115 min.: N990818A.D

53

67

79

95

108

121135

149

163173 191 210223236

249261

277292

Fig.4.2-2)

m/z 67 ion, stabilised by ring formation.

++

67 67

Fig. 4.2-3)

Formation of diagnostic ions for double bond position in polyenes.

COOMe+*

COOMe +*

1 2 3

+*

1 2

2 3

Position of double bonds from methyl end:n3: m/z 108, n4: m/z 122n6: m/z 150, n9: m/z 192

Position of double bonds from carbonyl group:d6: m/z 194, d7: m/z 208, d8 m/z 222d9: m/z 236, d10: m/z 250, d11: m/z 264

Cyclic?

Cyclic?

18:3n3

- 19 -

Tab.4.2-1) Important fragmentation reactions in FAME, masses given refer to C18-isomers (fig. 4-2.1)

No observed m/z Neutral Loss Reaction Saturated 1 298 [R-COOCH3] + 0 Molecular ion, M+ 2 74 [CH2C(OH)OCH3] + 224

CH3(CH2)13CHCH2 Cleavage β to carbonyl group, Mc Lafferty rearrangement. Base peak in saturated FAME

3 267 [R-C=O]+ 31 [CH3O] Cleavage α to ester carbonyl, loss of methoxy radical 4 59 237 Cleavage α to ester carbonyl, loss of R-group 5 87 [(CH2)2COOCH3]+ 211 [C15H31] 6 143 [(CH2)6COOCH3]+ 155 [C11H23] 7 199 [(CH2)10COOCH3]+ 99 [C7H15] loss of heptyl radical 8 255 [(CH2)14COOCH3]+ 43 [C3H7] loss of propyl radical 9 57 [C4H9]+ 241

[(CH2)13COOCH3] loss of butyl ion

Monoenes 10 296 [R-COOCH3] + 0 Molecular ion, M+ 11 55 [C4H7]+ 241 (monoenoic series) 12 69 [C5H9]+ 227 (monoenoic series) 13 83 [C6H11]+ 213 (monoenoic series) 14 97 [C7H13]+ 199 (monoenoic series) 15 74 [CH2C(OH)OCH3] + 224

CH3(CH2)13CHCH2 Cleavage β to carbonyl group, Mc Lafferty rearrangement.

16 59 237 Cleavage α to ester carbonyl, loss of R-group 17 87 [(CH2)2COOCH3]+ 209 [C15H29] 18 264 32 CH3OH Loss of methanol, rearrangement 19 265 31 [CH3O] Cleavage α to ester carbonyl, loss of methoxy radical 20 222 74 McLafferty, charge on R-fragment 21 180 116 ?? Dienes M+ 294 22 294 [R-COOCH3] + 0 Molecular ion, M+ 23 67 [C5H7]+ 227 Usually base peak in methylene interrupted dienes.

Dienoic series 24 81 [C6H9]+ 213 Dienoic series 25 95 [C7H11]+ 199 Dienoic series 26 109 [C9H13]+ 185 Dienoic series 27 55 [C4H7]+ 239 Monoenoic series 28 69 [C5H9]+ 225 Monoenoic series 29 59 235 Cleavage α to ester carbonyl, loss of R-group 30 263 31 [CH3O] Cleavage α to ester carbonyl, loss of methoxy radical Trienes M+ 292 31 292 [R-COOCH3] + 0 Molecular ion, M+ 32 79 [C6H7]+ 213 Trienoic series.

Usually base peak in methylene interrupted cis trienes.33 93 [C7H9]+ 199 Trienoic series. 34 107 [C8H11]+ 185 Trienoic series. 35 121 [C9H13]+ 171 Trienoic series. 36 67 [C5H7]+ 225 Dienoic series 37 81 [C6H9]+ 211 Dienoic series 38 95 [C7H11]+ 197 Dienoic series 39 109 [C9H13]+ 183 Dienoic series 40 55 [C4H7]+ 237 Monoenoic series 41 108 (in n-3) [C8H12] + 184 Internal cleavage and rearrangement of double bond

system. Indicator for n-3 polyenes 42 150 (in n-6) [C11H18] + 142 Internal cleavage and rearrangement of double bond

system. Indicator for n-6 polyenes 43 236 (in n-3)

[C13H21COOCH3] + 56 Internal cleavage and rearrangement of double bond

system. Indicator for ∆9 polyenes 44 194 (in n-6)

[C10H15COOCH3] + 98 Internal cleavage and rearrangement of double bond

system. Indicator for ∆6 polyenes

- 20 -

4.2.3) Mass spectrometry of picolinyl and DMOX-derivatives Although FAMEs give some information about fatty acid structure, information about positional isomerism is usually better achieved by other derivatives. By introducing a Nitrogen containing ring in the molecule, the molecular ion is stabilised and double bond migration is avoided. Two such derivatives have been used in this study.

Both the picolinyl esters (Harvey 1982, 1984) and the DMOX-derivatives (Zhang et al. 1988, Richili and Fay 1991) are widely used in fatty acid analysis. Simple rules exist for the interpretation of these spectra (Zhang et al. 1988, Christie 1988, Harvey 1992, Hamilton and Christie 2000). However, numerous exceptions occur and diagnostic ions are usually of low abundance. Mass spectra of all possible C18 monoenes have been published both for picolinyl (Christie et al. 1987) and for DMOX (Christie 2000a). Less work has been done on isomers with longer chain length and more double bonds, where the published spectra are usually those found in natural lipids with regular methylene interrupted double bonds; a structure which can not be expected in hydrogenated fats. Christie et al. (1987b) has investigated the picolinyl spectra of some NMI dienes and concluded that conclusive identifications were difficult to achieve without reference spectra of the actual compounds for comparison.

Spectra from pure peaks of some size are usually necessary to make reliable identifications. These derivatives are usually good alternatives to confirm or reject the presence of certain isomers. But in hydrogenated oils with extensively chromatographic overlap and a very large number of possible isomers, these derivatives might be of minor importance except in the analysis of monoenes where the number of isomers is low and a certain degree of chromatographic resolution can be obtained. An example of DMOX-spectrum (20:1n-9) is given in Figure 4.2-4.

4.2.4) Derivatisation of double bonds To give spectra with more distinctive fragmentation patterns, double bonds are often derivatised by some functional group. The most common procedures are hydrogenation with deuterium, preparation of dimethyl disulfide adducts (Schribe et al. 1988), oxidation to vicinal diols followed by silylation, and preparation of methoxy or methoxyhalogeno-derivatives (Christie 1989). However, most of these methods are not suitable for complex mixtures. Some of the methods substantially increase the weight of the isomers, a property that will cause problems when analysing C20 and C22 fatty acids. Thorough reviews of these and other methods for double bond locations are given by Schmitz and Klein (1986) and Minnikin (1978)

4.2.5) Geometric isomerism It has been generally believed that mass spectrometry gives no useful information about cis/trans-isomerism in unsaturated fatty acids. However, on simpler molecules, i.e. short-chain alkenes, differences exist between mass spectra of cis and trans isomers. This is explained both by steric and by thermodynamic effects (Natalis 1965). Trans isomers are thermodynamically more stable than cis isomers, the higher internal energy retained in the molecular ions of cis isomers might thus lead to small differences in the fragmentation pattern. Stearic effects are less likely to be of any significance in long chain fatty acids except when the double bond is close to the carbonyl group. Stearic effects may also be of significance in the stability of cyclic fragments formed from the molecular ion.

In this project, analysis of isomerised methylene interrupted PUFA revealed that trans isomerism have tremendous impact on the mass spectra of certain fatty acids (see section

- 21 -

5.6.1). Leth (1997) also found small differences between trans-trans and cis-cis 18:2n-6 when analysing the mass spectra by PCA.

Fig. 4.2-4)

Mass spectrum of DMOX-derivative of 11-20:1.

Gap of 12 amu instead of 14 amu indicates double bond position

60 80 100 120 140 160 180 200 220 240 260 280 300 320 340 360

55

69

98

113

126

168

196

210

236

264 278

292 320348 363

224182 250 306 334

M+

O

N

12 Amu

12 Amu

224

236

4.3) Infrared detection Infrared spectroscopy is widely used in the analysis of lipids, recent reviews of the topic is given by Guillén and Cabo (1997) and van de Voort (1994). Trans double bonds in oils and fats can be quantified by infrared spectroscopy by measuring the absorption at approximately 970 cm-1 (e.g. AOCS Official Method Cd 14-95). Different methods are applied for quantification, both the area and the height of the absorption peak are used, and different criteria are used for drawing the baseline (Madison et al. 1982, Lanser and Emken 1988, Ulberth and Haider 1992, van de Voort 1995). The infrared spectra also contain information about the chain length and the number of cis double bonds. The most important bonds for the determination of chain length and cis and trans unsaturation are listed in Sinclair et al. (1952a, 1952b), Strøm (1994), Guillén and Cabo (1997). The infrared spectrum of an unsaturated FAME is shown in Figure 4.3-1 and the most important signals are summarised in Table 4.3-1. Cis double bonds and carbon chain length can not be estimated directly from any strong absorption band in the same way as the amount of trans double bonds. This information is therefore extracted by means of multivariate calibration e.g. PLS. Some examples of this technique used on spectra of mixtures are shown by van de Voort et al. (1992).

4.3.1) Calculating chromatograms from spectra Chromatograms may be calculated from the spectra in several ways. Normally the signals at all the wavelengths are combined to a Gram-Schmidt chromatogram. This has the advantage of large signal strength because the absorbance at all wavelengths is used to construct the chromatographic trace. One drawback with this method is that noise are also extracted from all wavelengths which may lead to unnecessary large accumulation of noise if the desired signals are only present in parts of the spectrum.

A solution to this is to calculate selected wavelength chromatograms (SWC), where the average signal, or maximum, at a narrow range of wavelengths is used as chromatographic response. SWC may also be performed on spectra that has been derived to reduce spectroscopic baseline drift or the influence from unwanted interfering compounds.

- 22 -

0

0.5

1

1.5

2

2.5

3

2800285029002950300030503100

20:2tt

20:2cc3025trans

3010cis

2955

2926

2855

0

0.1

0.2

0.3

0.4

0.5

0.6

0.7

5006007008009001000110012001300140015001600170018001900

20:2tt

20:2cc

1746

1654/1648cis

1465

1418cis

1377

1238

1163

1033

968trans

914cis

723cis

Fig. 4.3-1) 20:2n-6 and the corresponding trans-trans isomer. Important absorption bands for the characterisation of fatty acids. After Strøm (1994) Guillèn and Cabo (1997). Only the areas 3100-2800 (upper trace) and 1900-550 (lower trace) cm-1 is shown. Y-axis is absorbance normalised to 1 at maximum absorbance for the carbonyl peak.

Table 4.3-1) The most important infrared signals in FAME.

Frequency (cm-1) Group Mode of vibration 3025 =C-H (trans) Stretching (very weak) 3010 =C-H (cis) Stretching 2955 -C-H (CH3) Stretching, asym 2926 -C-H (CH2) Stretching, asym 2855 -C-H (CH2) Stretching, sym 1746 -C=O Stretching 1654/1658 -C=C- (cis) Stretching (very weak) 1465 -C-H (CH2, CH3) Bending, scissoring 1418 =C-H (cis) Bending, rocking 1377 -C-H (CH3) Bending, sym 1238 -C-O, -CH2- Stretching, bending 1163 -C-O, -CH2- Stretching, bending 1033 -C-O Stretching 968 -HC=CH- (trans) Bending out of plane 914 -HC=CH- (cis) Bending out of plane (very weak) 723 -(CH2)n-, -HC=CH- (cis) Bending, rocking

- 23 -

4.3.2) Instrumental design and properties: Generally, three ways to link a FT-IR spectrophotometer to a gas chromatograph are used today; the lightpipe (LP) interface, the direct deposition (DD) interface (Bourne et al. 1990) and the matrix isolation (MI) interface. In both the direct deposition interface and the matrix isolation interface the eluent from the gas chromatograph is trapped on a metal surface and the surface can then be scanned by the FT-IR instrument after the chromatographic run. In the matrix isolation interface the analyte is trapped and isolated in an inert matrix, usually argon, such that there is no interactions between the molecules of the analyte (Reedy et al. 1985). This gives spectra similar to that of the analyte in gas phase. The direct deposition interface gives spectra similar to normal low temperature condensed phase spectra. Because the analytes is trapped and can be scanned several times after the chromatographic run the MI and DD has superior sensitivity compared to the LP interface, which use online detection.

In this work a lightpipe is used as interface between the GC and the FTIR detector (Figure 4.3-2). The lightpipe design is a compromise between sensitivity and chromatographic resolution. Longer lightpipes give increased sensitivity but lower chromatographic resolution (Griffiths 1978). Purging with a makeup gas has the same effect as reducing the length of the lightpipe. Most modern designs therefore have a fixed sized lightpipe, which is purged with helium or nitrogen.

Unlike most other GC detectors, e.g. FID and MS, where the amount of analyte is proportional to the area of the chromatographic peak, the response is proportional to the peak height in a lightpipe GC-FTIR. Using the area as a measure of the analyte amount, which is necessary in non-resolved mixtures, may therefore introduce an error in the quantification.

Figure 4.3-2)

Lightpipe design in the HP-IRD Makeup Gas

Capillary outletCapillary inlet

EluentInfrared source

Infrared detection

4.4) Silver ion chromatography High performance chromatography (HPLC) is widely used in the field of lipid analysis. A short review of the topic is given by Christie (1997).

Silver ion chromatography, both in the TLC mode and in HPLC mode, is used for the separation of both triglycerides and fatty acid derivatives. Both cis and trans double bonds form complexes with transition metals, and in particular silver ions. Thus it is possible to achieve chromatographic separation of complex mixtures in fractions after their number of double bonds. The silver ion complexes with cis double bonds are stronger than the complexes formed with trans double bonds, thus it is also possible to separate the fatty acids into fractions containing different number of cis and trans double bonds, a feature which makes silver ion chromatography a versatile tool for analyses of trans content in different fats (Christie and McG. Breckenridge 1989).

- 24 -

Usually silver ions is added to the stationary phase where it is stabilised on a cation exchange column (Christie 1987), but silver ions has also been added to the mobile phase in an ordinary reversed phase separation (Schomburg & Zegarski 1975). Today the preferred systems are usually the modified cation exchange columns and apolar mobile phases with small amounts of polar modificators, i.e. acetonitrile (Anon. 1996, Christie 1989, Juanèda and Sebedio 1994, Elfmann-Börjesson et al. 1997)

The mechanisms of silver ion complexing and retention are described in detail by Nikolova-Damyanova et al. (1996) and by Christie (1998b). In general the cis complexes are about twice as strong as the trans complexes. In mixtures of fatty acids this will lead to close eluting or overlapping bands of the following isomers: cis monoenes and trans, trans dienes – trans-cis dienes and trans-trans-trans trienes – cis-cis dienes and cis-trans-trans trienes. The possibility of overlapping fractions is even larger when tetraenes etc. is present in the mixture. Complete resolution of the fractions after their number of cis/trans double bonds is therefore difficult (impossible) to achieve in most samples of hydrogenated fats as long as polyenes are present.

The distance between the double bonds in dienes and polyenes is also important for the strength of a silver-ion complex, with a maximum complex strength when the double bonds are separated by five carbon atoms (Christie 1998b). As this distance vary in the isomers of hydrogenated fats (but not in natural fats) this is an important source of band broadening making it difficult to achieve good separations. The position of the double bond is of importance also in the monoenes. When the distance between the double bond and carbonyl group is eight carbons or more the elution order is opposite that of gas chromatography. The retention time will decrease as the double bond move further apart from the carbonyl group (Christie and McG. Breckenridge 1989; Adlof et al. 1995; Nikolova-Damyanova et al. 1992). Between ∆3 and ∆8 retention behaviour is less predictable because of interactions between the carbonyl group and the silver ion-double bond complex (Christie 1998b)

4.5) Multiavariate methods For several reasons, a large portion of the results are based on the use of multivariate mathematical methods such as principal component analysis (PCA) or partial least squares regression (PLS). A short description of the methods is given below.

4.5.1) The nature and representation of multivariate data In data sets with a large number of variables it is usually difficult to achieve a good picture of the nature of the data and the correlation between the variables. One or two variables can be illustrated on a single surface (e.g. a paper or the PC-screen), usually in the form of bar plots or two-dimensional xy-scatterplots. A third variable can be plotted in three-dimensional xyz-scatterplots, which is usually a projection in the three dimensional space projected onto a two dimensional surface.

The material world has three dimensions, and when the number of variables increase beyond three, most people will have problems with “seeing” the structure in the data. However, most mathematical rules that are valid in two or three dimensions are also valid in higher order dimensions, often called m-space, e.g. Phytagora’s rule can be applied for measuring distances in m-space: d2 = x1

2 + x22 + x3

2 + …. + xn2. A number of methods exist that aim at

reducing the dimensionality in systems with many variables. Most methods are using the correlation between the original variables to reduce the dimensionality. The original m-space may be projected onto so called latent variables. The concept of latent variables is explained by two examples below.

- 25 -

In most organisms, changes in temperature in the environment will have an effect of the fatty acid composition. If the levels of 20 fatty acids were measured in two populations living at different temperatures (all other factors being equal), we would have a data set where the m-space had a dimensionality of 20. A difference in several, maybe all, of the fatty acids would probably be observed. However, the change in the 20 variables is induced by one single parameter only, the temperature. In a case like this, the temperature is often referred to as an underlying factor or a latent variable. The true degrees of freedom is only one, thus the original 20 variables can be projected onto one latent variable (plus noise).

A more relevant example for this project is the application of infrared spectra from the IRD. The spectral regions of 1030-900 cm-1 and 3100-2800 cm-1 are the regions most relevant for describing the fatty acid molecules. With a spectral resolution of 8 cm-1 approximately 50 variables are necessary to describe these regions, thus the m-space has 50 dimensions. However the patterns in the spectra are caused by three factors only (a little simplified), the chain length of the molecule and the number of cis and trans double bonds. Thus there are only three degrees of freedom, and the original 50 dimensions can be projected onto three latent variables.

4.5.2) Matrixes and vectors Scientific data, e.g. analytical results, are often given in tables where the values for several samples or cases are listed for several measured variables. Such a table is illustrated in Figure 4.5-1 where eight variables have been measured in 10 cases/samples. In multivariate terms a table like this is commonly referred to as a matrix. Each number in the matrix is referred to as an element, each variable as a column vector and each sample as a row vector.

Figure 4.5-1)

10 x 8 matrix, X, with column vector t, row vector pT and element, e. By definition, vectors are column vectors, the superscript, T, on pT means transposed and denotes that pT is a row vector.

v1 v2 v3 v4 v5 v6 v7 v8o1

o3o4o5o6o7o8o9o10

o2

e6,4

row vector (pT)

colu

mn

vect

or (t

)

In linear algebra, column vectors and row vectors can be multiplied, the products is a matrix with dimensions corresponding to the number of elements in the column vector and the row vector. The multiplication of two vectors is illustrated in Figure 4.5-2.

A brief review of the matrix and vector computations most used in chemometrics is given in Grung (1996). For further reading textbooks in linear algebra are recommended, e.g. Anton (1991).

Figure 4.5-2)

Vector multiplication,

tpT = X

colu

mn

vect

or (t

)

x = Matrix(X)

Matrix(X)

row vector (pT)

- 26 -

4.5.3) Principal component analysis (PCA) Principal component analysis is a method to extract latent variables, in PCA called principal components, and to reduce the dimensionality of m-space. M denotes the original matrix. As illustrated in Figure 4.5-2 multiplication of a column vector and a score vector gives a matrix; thus, matrixes can be explained by sets of column and row vectors. When extracting the first principal component the goal is to find the column vector and the row vector that gives the best representation of the variation in M. The products of these two vectors, which is called score vector and loading vector, is the first principal component, PC1. With real data PC1 will never give a perfect description of M, thus there will be a residual matrix E1 which is found by subtracting each element in PC1 from the corresponding element in M, E1 = M - PC1 = M - tpT. Another way to describe the extraction of PC1 is that the algorithm seeks for the set of score and loading vectors that will minimise the total variation in E. This is done by an iterative computational procedure.

When E1 is computed, this matrix is used as input variable when PC2 is extracted. From E1 and PC1, the residual matrix E2 is computed: E2 = E1 - PC2. The procedure can be continued until the number of principal components equals the least of the number of variables or number of objects.

When the correlations in the variables are large the first principal components will explain a large portion of the total variation in M. In this example one might say that PC1 + PC2 explain 80% of the original variance in M. After extraction of principal components, the score vectors contain the information about the objects while the loading vectors contain information about the original variables.

Scor

e ve

ctor

2Sc

ore

vect

or 2

Scor

e ve

ctor

3Sc

ore

vect

or 3

Loading vector 3Loading vector 3

MM PC1matrixPC1

matrix

E1E1 PC2matrixPC2

matrix

E2E2 PC3matrixPC3

matrix

Loading vector 1Loading vector 1

Scor

e ve

ctor

1Sc

ore

vect

or 1

Loading vector 2Loading vector 2

E1 = M - PC1E1 = M - PC1

E2 = M1 - PC2E2 = M1 - PC2

Figure 4.5-3) Brief description of PCA

- 27 -

Thus the original objects and variables can be investigated by plots of score vectors and loading vectors respectively. Such plots are referred to as score plots and loading plots in the text. An example is illustrated in Figure 4.5-4 where the score vector 1 is plotted against score vector 2 and loading vector 1 are plotted against loading vector 2 in ordinary xy-scatterplots.

Figure 4.5-4)

Score plot and loading plot

Scor

e ve

ctor

2Sc

ore

vect

or 2

Score vector 1Score vector 1 Loading vector 1Loading vector 1

Load

ing

vect

or 2

Load

ing

vect

or 2

loading plotloading plot

Score plotScore plot

Since 80 percent of the variation in this example is maintained by the two first PCs, 80 percent of the original eight variables in M can therefore be expressed in two-dimensional plots of the first principal components. Some experience is usually needed to interpret PC-plots, but a few simple rules can generally be applied.

Assuming that the principal components displayed represent a large portion of the variance, objects that are close in score plots are also close in the original m-space. Thus, the distance between the objects in the score plots is a measure of the similarity between objects. In the score plot in Figure 4.5-4 there is two classes of objects, five objects in the second quadrant that are equal, and three objects in the fourth quadrant that are different from these. The single sample in quadrant three is not related to any of the groups.

In the loading plots, variables that lies in the same direction from origo tend to be positively correlated, variables that lies in opposite directions are negatively correlated, while variables that are located 90° to each other are uncorrelated. In the loading plot there are three groups of variables that are closely related. The variables in the second quadrant is negatively correlated to the variables in the fourth quadrant, while the two variables in the third quadrant are uncorrelated to these. The single variable in the first quadrant is positioned close to origo. This means that the variable is badly explained by the two loading vectors (assuming that the variables had equal variation in the original data matrix).

Usually the two first principal components are interpreted this way. When interpreting PC plots it is always important to take the explained variance by the two principal components into consideration. Important information may also be present in the other principal components. Validation methods indicate if the principal components represent data structure or white noise.

In the cases where the variables in the matrix are spectra, e.g. where each variable is a wavelength in an UV or IR spectrum or a mass in a mass spectrum, the loading and score vectors are usually presented as bar plots or line plots. In such cases the spectral representation can tell which functional groups that give rise to a single absorption signal in the spectra.

A comprehensive review of PCA is given by Wold et al. (1987)

- 28 -

4.5.4) Multivariate regression techniques Multivariate regression techniques is applied when a response variable (often denoted y) is to be explained from a number of x-variables (x1, x2, …, xn). From algebra it is known that when the number of samples (equations) equals the number of variables, exact solutions may be found. In matrix notations this case can be written as y = Xb where y is the response variable and b is the vector holding the unknown predictors (the vector that describes the response vector y as a function of the matrix X). B is found by inverting the X matrix and multiplying by y: b = X-1y.

When the number of samples is lower than the number of variables the set of equations has an infinitesimal number of solutions, the matrix X is not quadratic and not invertable. When there is more samples than variables the best estimate for b has to be found by multivariate regression techniques. In common multiple linear regression, MLR, b is found by the following equation: b = (XTX)-1 XTy.

MLR has certain limitations, especially when the number of variables is large, or when the amount of correlation between the variables or between the samples is large. When spectra are used as x-variables, the number of variables is usually very large (often hundreds). In those cases the number of samples is usually lower than the number of variables and thus MLR gives no solution. Colinearity between the variables or objects may lead to rank deficiency in the matrix (XTX) which may not be invertable (the matrix is singular). In the cases when IR spectra of fatty acid methyl esters are used as x-variables the amount of colinearity is extreme. Several hundred wavelengths may be measured, while the number of degrees of freedom is only three, the fatty acid chain length, number of cis double bonds and number of trans double bonds.

The above mentioned drawbacks of MLR may be solved by applying latent variable techniques. In principal component regression, PCR, the X-matrix is first subjected to a PCA decomposition, which reduce the original number of variables to a much lower number of latent variables. A regular MLR is then done with the score vectors from the PCA as input variables. In the case of IR spectra of the fatty acids, the MLR is performed on the three latent variables instead of the large number of spectral wavelengths. Thus a solution may be found as long as the number of samples is three or larger.

Another important aspect of the PCR is illustrated in Figure 4.5-3. After the first principal component is extracted, PC1 is subtracted from the data matrix before PC2 is extracted. Thus PC2 can not contain the same information as PC1; PC3 can not contain information explained by PC2, etc. The colinearity between principal components is zero (orthogonality); thus correlation between the variables is no problem when the PCs are used as variables in MLR.

Partial least squares regression is a similar technique to PCR, however there is one important difference in the extraction of the latent variables. In principal component regression, the extraction algorithm extracts latent variables that explain as much as possible of the variation in the X-matrix. In PLS regression the algorithm extracts LVs that explains as much as possible of the common variance between he X-matrix and the y-vector. Thus PLS is a more powerful regression technique, fewer PLS components than PC-components are usually needed to give good estimates for b, and the number of computations is reduced. PLS score plots are often more easy to interpret than PCA score plots. For these reasons PLS are usually preferred over PCR, especially when the number of variables is large. A comprehensive review of PLS is given by Manne (1987), Martens and Næs (1991), Kvalheim and Karstang (1989)

- 29 -

4.5.5) Variable pre-treatment The pre-treatment of variables is important in LV methods. When LVs are extracted the variables with largest absolute variance will have the largest influence in the models. When the difference between the variables is large, a few variables will dominate the loadings, while others are not explained. These problems can be solved by different pre-treatment of the variables.

The most common solution is standardisation. Each variable is divided by its own standard deviation, thus each variable will get standard deviation (and variance) equal to 1. Standardisation should be used with care. Variables that has a low initial variance often has a low signal to noise ratio, e.g. parts of the IR spectra where there is no absorbance and only baseline noise is present. Because of the low absolute variance, standardisation means multiplication by a large number. Thus the influence of variables with low signal to noise ratios may get increased influence on the cost of variables with large signal to noise ratios.

Other pre-treatment procedures include division by the variable mean or taking the logarithm or various roots of each variable. Usually subtraction of the mean (centring) of each variable is performed before PCA or other LV techniques.

4.5.6) Noise and noise reduction techniques Noise in modern hyphenated chromatography is a rather complex issue. The topic of analytical noise is discussed in this chapter because the issue is strongly related to data treatment and multivariate techniques.

Spectral noise / Chromatographic noise

There is no clear distinction between spectral noise and chromatographic noise, any signal in the spectrum will give a contribution to the chromatographic trace and any “ghost peak” or visible chromatographic interferent will have a spectrum. However the noise might only be relevant in either the chromatographic direction or the spectroscopic direction. The noise may also be removed in one of the two directions, e.g. by differentiation.

Correlated / uncorrelated noise Both correlated and uncorrelated noise might be handled by LV techniques, but in different ways. By uncorrelated noise, often called white noise, there is no pattern or correlation between the variables. Since LV techniques such as PCA extracts the systematic variation in a matrix, uncorrelated noise is left in the residual matrix after the systematic variation has been extracted. Thus the LV used for interpretation or regression may be essentially free of white noise. This quality of PCA can also be used as a filtering technique in hyphenated chromatography. PCA is performed on the CST matrix, containing the products of chromatograms and spectra. The systematic variation is explained by the principal components found significant by PCA. The CST matrix may then be reconstructed by multiplying only the significant components, leaving the white noise in the other components.

In correlated noise there is a correlation pattern in the variables, e.g. when variable x1 is high so is variable x2. Examples of correlated noise are the presence of background of water and CO2 in IR spectra or the contributions from column bleed in the MS spectra. The correlated noise is handled by the fact that it, opposed to white noise, is explained by the significant components in LV methods. The latent variable that explains the noise pattern(s) may be excluded in interpretations or regressions, or the matrix may be constructed without these components. Note that if the noise pattern does not correlate with the Y-variable(s) in a PLS

- 30 -

regression, the principal component will not be significant, or the noise pattern might not be explained. This is because PLS regression extracts components that are relevant for the prediction of Y.

Overlap/interferences The presence of interfering compounds in chromatograms or spectra is a common problem in both spectroscopy and chromatography. Interferences in spectroscopy may be present as correlated noise, which is handled by LV methods as described above. Other ways to handle these problems is by application of multivariate curve resolution techniques (Liang and Kvalheim 1993, Liang et al. 1993, Cuesta Sánches et al. 1997) or by derivating the CST matrix in chromatographic direction to obtain pure spectra (Liang and Kvalheim, 1993b). If the interferent is present at the baseline before or after the chromatographic peak it may also be reduced or completely removed by simple baseline subtraction.

Baseline drift Baseline drift is another type of noise that may be present in the spectra, especially the infrared spectra. This may be corrected by linear or non-linear regression techniques. When the spectra are used as x-variables in multivariate regression, the baseline drift is often corrected by using the first or second derivative of the spectra in the regression models.

Homoscedastic / heteroscedastic noise The difference between homoscedastic and heteroscedastic noise is also important. Homoscedastic noise is independent of the signal strength, while heteroscedastic noise is increasing with increasing signal strength. Usually both the two types of noise are present. In modern instrumentation with electronic amplification of the signals heteroscedastic noise may represent a large portion of the noise. The difference between these two types of noise is important with respect to signal to noise ratio. When the noise is heteroscedastic an increase in the signal (e.g. when increasing concentrations) may not give the desired increase in signal to noise ratio because the noise increases proportionally.

The noise is often measured from the baseline in areas where there are no signals. This method should only be applied in systems where homoscedastic noise dominates. Where heteroscedastic noise dominates, the estimated error should be given as percent of the signal and not as a fixed number.

- 31 -

5) Method development

5.1) HPLC separation

5.1.1) System description HPLC was used both for pre-fractionation of partially hydrogenated fats and for the purification of reference fatty acids. A silver nitrate loaded ion exchange column, Chromspher 5 Lipids (Chrompack, Middelburg, The Netherlands) was applied with a non-polar mobile phase.

The HPLC system used in most of the study is described in Figure 5.1-1. The column effluent is splited between the evaporative light scattering detector (ELSD) and a fraction collector. The split ratio was usually set to approximately 1:4, i.e. 25% of the effluent was directed to the detector; the rest was directed to the fraction collector. Different amounts of internal standard (15:0 FAME) was added to the fractions, which was diluted or concentrated under a stream of nitrogen before GC analysis. As long as the split ratio were held constant during the HPLC run, the relative composition of the unfractionated sample could be calculated by comparing the absolute amounts found in each of the fractions.

Figure 5.1-1)

Sketch of HPLC system

mixer

A B

Injector

Pump

Fraction collector

Ag+ -

colu

mn

Ligh

t sca

tterin

gde

tect

or

Splitt 75/25

5.1.2) Mobile phase selection Several mobile phase combinations have been applied with silver ion columns of this type. Most common are chlorinated solvents like dichloromethane and dichloroetane with small mounts of acetonitrile as polar modifier. The combination of hexane and acetonitrile are also widely used. A third solution is the combination of hexane, toluene and ethyl acetate.

The addition of acetonitrile as polar modifier in the solvent system proved to give increased baseline noise on the ELSD detector compared to other solvents. The cause of this increased detector noise was not found. The hexane/toluene/ethyl acetate combination was therefore used as mobile phase in initial studies. A gradient elution of FAME from PHFO analysed with this system is shown in Figure 5.1-2.

The three first groups of peaks are well separated and contain principally one type of isomers. The first peak contains the saturated fatty acids; the second contain trans monoenes ant the third peak contains the cis monoenes. The fourth peak contains mainly trans-trans dienes, but other geometric isomers are also present. In the case of dienes and polyenes the positions and

- 32 -

the distance between the double bonds will have significant influence on the retention times. There will also be overlap between dienes and more unsaturated polyenes. Thus peaks containing e.g. only cis-trans dienes or cis-cis dienes could not be achieved.

PUFA

SFA

tran

s M

UFA

cis

MU

FA

Figure 5.1-2) PHFO with mp appr..31°C analysed on hexane based solvent system with toluene/ethyl acetate as polar modifier. Solvent A: 100% hexane. Solvent B: 50% hexane, 25% toluene, and 25% ethyl acetate. Injection at 5% solvent B, hold for 5 min. Increase to 50% solvent B at 80 min. Hold for 20 min. Flow: 1.0ml/min. Column temperature: Approx. 20°C.

The use of toluene/ethyl acetate as polar modifier has certain limitations. First of all, it is a weak modifier and problems with elution of the most unsaturated compounds may arise. Toluene and ethyl acetate also have a rather high viscosity compared to hexane. Thus, the viscosity of the mobile phase will change during a gradient run. This may influence the split ratio between the detector and the fraction collector and cause quantification problems.

Several other polar organic solvents were tested as modifiers. Of these, acetone proved to be the most successful. Acetone was found to be slightly stronger as polar modifier compared to toluene/ethyl acetate. Its viscosity is practically equal to the viscosity of hexane, and its low boiling point makes concentration of the fractions possible at low temperatures, reducing the risk of loss of FAME in this step. The elution profiles with acetone as modifier was found to be similar to the toluene/ethylacetate combination. Some samples are shown in Figure 5.1-5 a-e

The elution times for several FAME standards are plotted against percent acetone in the mobile phase in Figure 5.1-3. The standards were injected separately. Note that cis 18:1n-9 and all-trans 18:2n-6 elute with the same retention times. However, when cis monoenes and trans-trans dienes are co-injected, which is the case when real samples of PHFO or PHVO are analysed, the cis monoenes elute before trans-trans dienes. The degree of overlap between the two fractions is usually close to zero. Thus a displacement effect may be present.

5.1.3) Temperature dependence At 0°C, increasing the portion of acetone in the mobile phase beyond 50% had no effect on the retention times of the most polar compounds. The elution times decrease with increasing temperature. EPA and DHA could be eluted at elevated temperatures. However, heating of the column proved to give significant reduction of the column lifetime. Plot of elution times at different temperatures for 20:4, 20:5 and 22:6 is given in Figure 5.1.4

- 33 -

0

5

10

15

20

25

30

35

40

45

50

0 2 4 6 8 10 12 14 16 18 20 22 % acetone

16:0 18:1 t 18:1 c 18:2 tt 18:2 cc

min.

Figure 5.1-3) Retention times with different amounts of acetone in the mobile phase. Isocratic elutions. Flow 2ml/min. Room temperature (approx. 20°C)

0

10

20

30

40

50

60

50 55 60 65 70 75 80 85 90 95 °C

22:6n-3

20:5n-3

20:4n-6

min.

Figure 5.1-4) PUFA Retention times at different temperatures. Mobile phase: 50% hexane, 50% acetone (vol./vol.). Flow: 2ml/min

5.1.4) Elution profiles of hydrogenated fats For routine separation of hydrogenated fats and fatty acid standards, the program similar to that illustrated in Table 5.1-1 was used. Small adjustments in the programs sometimes had to be applied to correct for changes in column characteristics, different sample types, amounts injected, etc.

The elution profiles for several types of fat are shown in Figure 5.1-5. For the vegetable oils, where the number of isomers seem to be much lower than with the PHFO the HPLC resolution may be good enough to achieve complete identification of the isomers if sufficient number of fractions are collected by HPLC, see for instance the chromatohrams in Figure 5.1-5 d and e. In the chromatograms of PHFO samples (Figure 5.1-4 b and c), distinct peaks are not seen because of the large number of isomers. The sharp peaks at approximately 25 and 35 minutes found in the PHFO samples are probably not fatty acid isomers. No single large peaks were observed in the GC-MS chromatograms of these fractions.

- 34 -

Table 5.1-1) Typical Ag-HPLC program applied.

Time (min) Percent Solvent B

0 (start) 3

5 5

20 15

27 50

30 100

40 100

40 0

50 0

0 20 40 60 80 1000

10

20

30

40

50

% Solv. B

Time(min)

Solvent A 100% Hexane Solvent B 50% hexane, 50% acetone

Flow 2 ml/min Temperature Approx. 20°C

Column Chromspher 5 Lipids, 250 x 4.6 mm

Chrompack, Middelburg, The Netherlands

Aquisition

Equilibr.

Figure 5.1-5a)

HPLC elution profile, FAME reference mixture GLC-67, Nu-Chek Prep. Elysian, MN. Injection at 3% solvent B, increase to 5% at 5min., Increase to 15% at 20min. Increase to 50% at 27min. Increase to 100% at 30min. Hold for 10 min. SF

A +

18:

1n-9

t

18:2

n-6

cc

18:2

n-6

tt

18:1

n-9

c

18:3

n-3

ccc

Figure 5.1-5b)

HPLC elution profile, PHFO with mp approx. 26°C made from monoene rich raw oil. Parameters as described in Table 5.1-1.

PUFA

SFA

tran

s M

UFA

cis

MU

FA

- 35 -

Figure 5.1-5c)

HPLC elution profile, PHFO with mp approx. 31°C. Parameters as described in Table 5.1-1.

PUFA

SFA

tran

s M

UFA

cis

MU

FA

Figure 5.1-5d)

HPLC elution profile, PHSBO with mp approx. 32°C. Injection at 5% solvent B, hold for 5min., Increase to 20% at 30min. Increase to 50% at 45min. Increase to 100% at 50min. Hold for 5 min.

PUFASFA

tran

s M

UFA

cis

MU

FA

Figure 5.1-5e)

HPLC elution profile, PH coconut oil. Low degree of hydrogenation. HPLC parameters as in Figure d. PUFA

SFAcis

MUFAtrans MUFA

5.1.5) Control of split stability The stability of the split between the fraction collector and light scattering detector is critical for correct quantification. The split stability was tested by fractionation of the reference mixture GLC-67 (Nu-Chek Prep, Elysian, MN). Each fraction was collected and the amount quantified by addition of equal amounts of 19:0 to all fractions. The calculated weight percent of the FAMEs in the sum of the fractions were compared with the percents in an unfractionated sample. The results are given in Table 5.1-2.

The change in the percent composition, expressed as loss factor, between the two methods of quantification is generally small. A loss of 22 percent is seen for 20:0, a large loss is also seen for 22:0. This loss is probably caused by the GC-MS quantification method, and not by changes in split ratio. 20:0 and 22:0 elutes together with 14:0, 16:0 and 18:0, for which no

- 36 -

loss were observed. 20:0 and 22:0 are very small peaks compared to the internal standard (19:0), which may lead to underestimation of these peaks, especially if the MSD threshold value is high.

By comparing the sum of areas of the unfractionated sample, with the sum of areas in the fractionated sample, the split ratio was estimated to 0.55, (55% of the effluent were directed to the fraction collector). The discrepancy between weight percent (column A) in Table 5.1-2 and area percents (column B and C) is caused by differences in the GC-MS detector response and is not related to the HPLC split ratio. Response factors were not applied.

Table 5.1-2) Control of split ratio by fractionation of GLC-67. Column B is percent composition by direct GC-MS analysis of unfractionated reference mixture. Column C is results achieved by fractionation on HPLC, addition of internal standard (19:0), GC-MS analysis of fractions, and calculation of total composition.

A B C D Weight

percent Percent

composition by GC

Percent after fractionation

Loss factor (C/B)

14:0 1.0 0.97 0.99 1.02 16:0 10.0 10.65 11.07 1.04 18:0 6.0 6.24 6.35 1.00

trans-18:1 20.0 20.27 20.59 1.02 cis-18:1 25.0 25.89 24.98 0.96

trans, trans-18:2 2.0 1.69 1.50 0.88 cis, cis-18:2 34.0 32.72 33.12 0.98

20:0 0.5 0.45 0.35 0.78 18:3 1.0 0.77 0.72 0.94 22:0 0.5 0.36 0.32 0.89

5.2) Gas chromatography, choice of columns

5.2.1) Introduction A large variety of capillary CG columns are available, differing in polarity, functional groups in the stationary phase, column length, internal diameter and the thickness of the stationary phase.

The choice of a column is usually a compromise between different wanted properties. The two detectors in the GC system, the IRD and the MSD shall solve different tasks, and require very different chromatographic conditions to be operated at maximum performance. It is possible to run the two detectors in parallel or in series on the same column, but then both detectors would be operated at sub-optimal conditions.

The MSD has a relatively good sensitivity, and can therefore be used to identify and quantify individual isomers. To utilise this property the column should have a high selectivity for

- 37 -

positional isomerism and large efficiency (large plate number). When the column is used for the analysis of HPLC fraction from silver ion chromatography, the selectivity for geometric isomerism is of less importance. When used with an MSD the column bleed should also be low. The column flow should also be kept low. This requires relatively long columns with a narrow internal diameter, a low film thickness and a stationary phase with medium to high polarity.

The IRD has different requirements. The sensitivity of the detector is relatively low; thus large amounts need to be injected. Separation of individual isomers will be low when large amounts are injected, and the IR-spectra give no information about positional isomerism either. The column of choice for the IR-detector should therefore have large sample capacity and the ability to separate the fatty acids after total number of double bonds. There should be no or limited overlap between the retention times of fatty acids with different chain length. The last demand excludes the most polar phases. Thus, a phase with low to medium polarity should be applied.

The retention characteristics of several different phases were tested with a FAME reference mixture and samples from hydrogenated fats. The results are summarised below.

5.2.2) Elution profiles The elution profiles for the C20 fatty acids are shown in Figure 5.2-1. The fatty acids of other chain lengths show similar patterns. For the least polar stationary phases 100% methylpolysiloxane, DB-5, CP-Sil 13 and DB-1701 the polarity was too low to give the wanted separation after the total amount of double bonds in the molecule. The methylpolysiloxane, DB-5 and CP-Sil 13 showed the usual elution pattern for low polarity columns where the unsaturated fatty acids elute before the saturated fatty acids. However the double bond positions has large influence in all three cases, leading to higher retention times for the n-3 unsaturated fatty acids compared to other isomers in the reference mixture. In all three columns, the region of trienes overlap with the region of dienes, thus none of these stationary phases are suitable for use with the IRD detector.

In the case of DB-1701, the elution pattern is less predictable than with the other columns. The C20 fatty acids in the hydrogenated sample elute as a rather narrow group with complete overlap between fatty acids with different number of double bonds.

The HP-Innowax has a stationary phase that differs significantly in composition from the other stationary phases. This is a 100 percent polyethylene glycol (PEG) phase while the other phases are polysiloxanes that is modified with phenyl or cyano groups to increase polarity. The fatty acids on the PEG column elutes according to increasing number of double bonds. 20:4n-6 elutes before 20:3n-3 which illustrates that the double bond position also is of large influence. All 20:1 isomers are well separated from 20:0 and there seem to be limited chromatographic overlap between monoenes and polyenes.

BPX-70 and SP-2560 are both very polar columns with a large load of cyano groups in the phase. The elution patterns are similar to that found for the PEG column, but the increase in polarity leads to better separation between the saturated fatty acids and the monoenes. The monoenes are also better separated into cis and trans. There is some overlap between cis and trans monoenes and between cis monoenes and PUFA. The polarity of these stationary phases is so high that certain trienes in PHFO may overlap with the saturated fatty acid with two more carbons in the molecule. Thus, under certain conditions, these columns may not be suitable for analysis of unfractionated PHFO.

- 38 -

C20 region, GLC-461 C20 region, PHFO mp 31

20:4n620:5n3 20:3n6 20:2n6

20:3n320:1n9 20:0

32.5 36.0 min

20:320:2

20:1/20:2

20:1

20:0

31.5 37.0 min Figure 5.2-1a) 100 percent methyl substituted polysiloxanes

20:4n620:5n3

20:3n6 20:2n6

20:1n9/20:3n3 20:0

32.5 35.5 min

20:2

20:1/20:220:1/20:2

20:0

32.5 36.0 min Figure 5.2-1b) DB-5, J&W (5% phenyl, 95% methyl polysiloxane

20:3n6/20:5n3

20:4n620:1n9/20:2n6

20:3n3

20:0

39.0 42.0 min

20:1/20:2

20:1/20:2

20:1/20:220:0

40.0 42.0 min Figure 5.2-1c) CP-Sil 13, Chrompack (14% phenyl, 86% methylpolysiloxane)

20:5n320:3n6

20:4n6/20:1

20:220:020:3n3

37.0 40.5 min

20:2/20:3

20:1

20:2

20:0

40.0 41.5 min Figure 5.2-1d) DB-1701, J&W (6% cyanopropylfenyl-, 94% methylpolysiloxane)

20:5n320:3n3

20:4n620:3n6

20:220:120:0

30.0 36.0 min 30.0 35.0 min

20:2 / 20:320:1/20:2

20:1

20:0

Figure 5.2-1e) HP Innowax, Hewlett Packard (100% polyethylene glycol)

- 39 -

C20 region, GLC-461 C20 region, PHFO mp 31

20:0

20:5n322:1

20:3n3

20:4n620:3n620:2

20:1n9

18:3n3

22:0

24.0 29.0 min

Unfractionated PHFO not analysed. Elution

profiles of PHFO on this column may be found in section 6.

Figure 5.2-1f) BPX-70, SGE (70% cyanopropyl polyphenylenepolysiloxane).

29.0 38.0 min

20:524:0

22:2

20:4n6

20:3n322:1

20:3n622:0

20:218:3n3

20:118:3n6

20:0

20:1

20:120:1

20:0

28.0 32.5 min

20:2 / 20:3

Figure 5.2-1g) SP-2560 (100% cyanopropylpolysiloxane)

5.2.3) Chromatographic overlap, HPLC fractions Fractions from Ag-HPLC of PHFO were analysed on the three most polar columns. The first two fractions were the trans and cis monoenes respectively, while the other six fractions contained dienes and polyenes. Note that all fractions are scaled to the highest peak in the chromatograms, thus the polyenes are considerably expanded on the y-axis compared to the monoenes.

The PEG column (HP-Innowax) showed poor separation between cis and trans monoenes. There was some overlap between the monoenes with the double bond close to the methyl end and early eluting dienes. Very small amounts of late eluting C20-PUFA seem to elute into the C22 region. Note that 20:5n-3 elutes before 22:0 on this column. This means that some of the polyenes created in the hydrogenation process have elution times larger than EPA, even though the number of double bonds in the new isomers probably are less then five.

On the BPX-70 there seemed to be no overlap between late eluting C20 PUFA and C22 isomers. There was a good separation between cis and trans monoenes. The overlap between cis monoenes and early eluting dienes seems to be of approximately the same size as on PEG columns.

The SP-2560 had too large polarity to be useful for analysis on unfractionated fats. There was extensive overlap between C20 and C22 regions in the chromatograms. C18 fatty acids also eluted into the C20 region. Note that the saturated fraction is not shown. The elution times for the saturated fractions can be found in Figure 5.2-1. The separation between trans and cis monoenes is approximately as on BPX-70.

- 40 -

Figure 5.2-2a)

GC-MS trace. HP Innowax (Agilent). Elution profiles for the C20 regions of various HPLC fractions of PHFO with mp. 31°C.

Trans monoenes: red

Cis monoenes: blue

PUFA: green

Chromatograms are normalised to peak maximum.

30.0 31.0 32.0 33.0 34.0 35.0 36.0 37.0 min

C20C22

Figure 5.2-2b)

GC-MS trace. BPX-70 (SGE), other parameters as in Figure a

24.0 24.5 25.0 25.5 26.0 26.5 27.0 27.5 28.0 min

C20

Figure 5.2-2c)

GC-MS trace. SP-2560 (Supelco), other parameters as in Figure a

29.0 30.0 31.0 32.0 33.0 34.0 35.0 min

C20 C22

- 41 -

5.2.4) Column efficiency When temperature programming are used in GC, the column efficiency can be described by the separation number, or Trennzahl (Ettre 1975). This is defined as the distance between two peaks in a homologue series (usually n-alkanes) divided by the sum of widths at half height. Mathematically, this can be described as follows:

TZ = [tR(z+1) - tR(z)] / [W½h(z) + W½h(z+1)] - 1

The separation numbers calculated by comparing 18:0 with 20:0, and 20:0 with 22:0 are given in Table 5.2-1 below. The columns differ in length and other dimensions, and the chromatographic parameters used are not directly comparable (parameters are given in appendix 8.6).

Still some trends can be seen. Even though the most polar columns are much longer than the less polar columns, the separation number decreases with increasing polarity. The SP-2560 is four times as long as the 100 percent methyl substituted polysiloxane column; the separation numbers are slightly lower. The separation numbers of CP-Sil 13 are nearly twice that of SP-2560.

It should be mentioned that skewing and peak broadening are often seen on peaks of long chain saturated fatty acids when very polar columns are used. This is caused by low solubility of the saturated fatty acid both in the mobile phase (low volatility) and stationary phase (too low polarity). Thus, the separation numbers for monoenes and polyenes may be higher than the figures calculated from the saturated series on these columns.

Table 5.2-1) Calculated separation numbers

Column length

(m)

Rt18:0 Rt20:0 Rt22:0 W18:0 W20:0 W22:0 TZ18-20 TZ20-22

100% methyl 25 28.45 36.05 43.52 0.073 0.081 0.098 23.23 19.37DB-5 30 27.76 35.27 42.69 0.074 0.076 0.071 23.50 23.61CP-Sil 13 50 33.39 41.31 48.98 0.067 0.057 0.056 30.62 32.44DB-1701 60 30.53 39.60 48.92 0.087 0.100 0.105 22.79 21.29HP-Innowax 60 23.38 29.98 36.85 0.078 0.081 0.089 19.27 18.81BPX-70 60 19.48 23.86 28.71 0.046 0.054 0.059 20.40 20.03SP-2560 100 24.21 28.35 33.25 0.051 0.059 0.074 17.31 16.88

5.2.5) Summary In summary, PEG columns and BPX-70 seem to be suitable for the analysis of unfractionated samples on the IRD. The overlap between cis and trans monoenes seen on PEG can be solved by quantification based on the IR-spectra.

For analysis of cis and trans monoenes on unfractionated fats, BPX-70 or SP-2560 should be applied. These give good separation between cis and trans isomers. PEG columns should not be used for this purpose. In samples where significant amounts of PUFA are present, overlap of late eluting PUFA and monoenes may be a problem on SP-2560. EPA may also cause problems on BPX-70, however the retention times of EPA can be significantly shifted compared to the retention times of 22:1-isomers by applying different analytical conditions (see section 5.7).

- 42 -

For the analysis of Ag-HPLC fractions, where the cis-trans separation is already carried out by the HPLC, the ability to separate positional isomers is of interest. All three columns seem to have approximately the same ability to separate positional isomers of monoenes. The PEG column (which is shorter than the two other columns) showed remarkable good separation of positional monoenes. Thus PEG columns and other less polar columns might be a valuable supplement to the more polar columns also in the analysis of trans isomers.

The choice of a GC column is not only a question about relative elution times. Parameters such as temperature limits, column bleed, column efficiency (measured as plate numbers or Trennzahl numbers) should be taken into consideration. The column efficiency (plates per meter) tend to decrease significantly as the polarity of the stationary phase increase. Thus the gain in selectivity sometimes achieved by increasing column polarity may be lost because of decreased column efficiency. The column bleed, which should be minimised when MS detectors are applied, also tends to increase as polarity increases. In general, the least polar column that can be used to solve a specific resolution problem should be applied.

A 30 m PEG column, CP-Wax 52 (Chrompack, Middelburg, The Netherlands) was selected for the GC-IR analyses. The BPX-70 may be a good alternative, but this was not investigated further. A 60 m BPX-70 column was selected for the GC-MS analyses; this can be applied both on unfractionated fats and HPLC-fractions. This was also the only polar column with temperature limits high enough to be used with picolinyl and DMOX derivatives of long chain fatty acids. The use of a long PEG column, which will have better column efficiency than more polar cyanopropyl phases of the same length, seem to be a promising alternative for the analysis of HPLC fractions; this was not investigated further.

5.3) Derivatives for GC-MS

5.3.1) Introduction Three kinds of GC derivatives of fatty acids have been applied in the study; fatty acid methyl esters (FAME), Picolinyl esters, and dimethyloxazoline derivatives (DMOX). The identification of fatty acid isomers from spectra of the two latter is described in section 4.2. Identification based on FAME is briefly described in section 4.2 and outlined in section 5.6.

Application of DMOX and picolinyl derivatives proved to be of limited importance in the identification of positional isomerism in the hydrogenated samples. In the case of monoenes, DMOX were used to confirm the identification based on retention times of FAME isomers.

5.3.2) Picolinyl derivatives The derivatisation procedure for picolinyl derivatives is described in appendix 8.3. The method described by Harvey (1992) was applied. Weaker concentration of thionylchloride compared to the original procedure was used because the original concentration proved to attack double bonds in methylene interrupted dienes.

Problems with artefacts or unreacted FAME or FFA were noticed in some samples. Thus quantification on picolinyl esters should be applied with care.

Picolinyl esters elute at temperatures that are approximately 50°C higher than the corresponding FAME elution temperatures. Thus BPX-70 was the only polar column that could give reasonable elution times with the C22 picolinyl esters. The chromatographic resolution was significantly worse than resolution achieved with both FAME and DMOX.

- 43 -

The picolinyl esters proved to be useful for identification of isolated compounds. However, because of the poor chromatographic properties, the procedure had only limited value for identification of unknowns in complex mixtures of isomers in hydrogenated fats.

5.3.3) DMOX derivatives In the case of DMOX derivatives the method described by Berdeaux et al. (1997) was applied with some modifications. The complete procedure is described in appendix 8.4. Derivatisation is done by heating with large excess of 2-amino-2-methylpropanol (AMP) at 180/190°C for approximately 8 hours. One potential problem with this procedure is the risk of isomerisation of double bonds caused by the large temperature and prolonged reaction times. The AMP solution is dissolved in DCM and washed twice with water before a final cleanup step on a short reversed phase HPLC column.

The problem with possible isomerisation was tested by heating for 12 hours. Little or no trans isomerisation was observed. However, other types of artefacts was present, most of these are removed by the last HPLC cleanup step.

Similar to the picolinyl esters, DMOX derivatives are useful alternatives for the identification of isolated isomers. However, pure spectra of a certain quality must usually be available to achieve a reliable identification. This limits its use on fractions from hydrogenated fats. Multivariate curve resolution techniques (explained in 4.5.6) can be an alternative to achieve pure spectra.

The chromatographic properties of DMOX derivatives are better than the properties of picolinyl esters but slightly poorer than the properties of FAME. The application of picolinyl esters was used to confirm the identity of certain monoenes in HPLC fractions of hydrogenated fats.

5.3.4) Fatty acid methyl esters (FAME) The most common derivatives are fatty acid methyl esters. FAME are usually prepared by alkaline or acidic trans-esterification in large excess of methanol. The complete derivatisation and extraction procedure, which is based on AOCS Ce 1b-89, is described in appendix 8.2.

The AOCS method combines an alkaline (methanolic NaOH) and an acidic, (methanolic BF3) trans-derivatisation. Studies on raw fish oil proved that even short derivatisation times was able to change the fatty acid composition of the sample. The most unsaturated compounds (EPA/DHA) were reduced, and some earlier eluting artefacts also appeared with the BF3 step.

Refined fish oils and hydrogenated fats are pure triglycerides which react rather rapidly to give FAME. Studies indicated that 10 minutes heating at 100°C in methanolic NaOH followed by 10 minutes heating in methanolic BF3 was sufficient to give complete derivatisation to FAME.

5.4) Infrared detection The most important infrared signals from FAME are described in Figure 4.3-1 and Table 4.3-1. A series of initial experiments were performed on various reference compounds to test the system performance, limits of detection, linearity, etc. The studies were performed on the 30m CP-Wax capillary column described in appendix 8.6.

- 44 -

5.4.1) Studies on saturated FAME standards, C-H signals. The behaviour of the C-H signals was investigated by comparing the saturated FAME isomers from C12 to C24. GLC-409 (Nu-Chek Prep, Elysian, MN) was used as a reference mixture. To avoid influence from chromatographic conditions all spectra were normalised after the maximum of the carbonyl peak (i.e. in all spectra, the maximum of the carbonyl peak has absorbance value 1.00).

In Figure 5.4.1 the number of CH bonds are plotted against the absorbance maximum for the CH signal at 3200-2800 cm-1. There is a relationship between the number of CH bonds in the FAME molecules and the absorbance maximum for this signal, which is close to linearity. However, by extrapolating one sees that the linear regression line is crossing the x-axis far from origo. Since negative absorbance can not be achieved, the regression must follow a non-linear trend as indicated by the dotted line.

Figure 5.4-1)

Number of CH bonds in saturated FAME molecules plotted against the absorbance maximum for the CH region, extrapolation crosses far from origo.

0

1

2

3

4

5

6

0 10 20 30 40 50

y=0.0168 x1.4867 y=0.1434 x - 1.6607

Number of CH bonds in FAME molecule

Max

C-H

abs

/ m

ax C

=O a

bs

This non-linearity may be explained by a small difference in the absorption maxima for CH signals from CH2 and CH3. For straight chain FAME isomers the number of CH3 is always two. Thus the CH2 to CH3 ratio increases with increasing chain length of the molecule. The influence on the spectra is illustrated in Figure 5.4-2, which is showing the C-H region of saturated FAME isomers with chain lengths from C12 to C24. At lower chain lengths a larger portion of the signal is in the shoulder around 2930 cm-1 and is therefore not contributing to the signal at the peak maximum.

Direct linear regression should be used with care when the chain length of FAME molecules are estimated from IR-spectra, especially when short chain lengths are considered. A multivariate model based on the wavelengths from 3050-2800 cm-1 will probably be able to handle short chain fatty acids with high degree of accuracy. Prediction of the chain length from the infrared spectra have little practical importance because these parameters are already known from the GC retention times, however, it is important to know how these spectral variations may influence the prediction of other parameters, e.g. the number of cis and trans double bonds.

- 45 -

Figure 5.4-2)

The CH signal for saturated FAME molecules normalised to the peak maximum.

0

0,1

0,2

0,3

0,4

0,5

0,6

0,7

0,8

0,9

1,0

27502800285029002950300030503100

12:0

16:0

20:0

24:0

cm-1

Nor

mal

ised

abs

orba

nce

Figure 5.4-3)

Number of CH bonds in saturated FAME molecules plotted against the total peak area for the CH region, extrapolation crosses near origo y = 1,5858x - 3,6358

0

10

20

30

40

50

60

70

80

0 10 20 30 40 50

cm-1

Area under 3100-2750cm-1(CH-region)Area under 1530-900(fingerprint region)

Number of CH in FAME molecule

Are

a un

der t

he p

eaks

The signal strength from the hydrogens in CH3 and CH2 groups is similar. This can be controlled by plotting the area of the CH2/CH3-peaks (normalised against the carbonyl maximum) versus the number of double bonds. This is shown in Figure 5.4-3 where the extrapolated regression line is crossing the x-axis near origo.

The fingerprint region from 1600 to 900 cm-1 is the other important region of the spectra. The fingerprint region of the C12 to C22 saturated FAME isomers are shown in Figure 5.4-4. There are only two peaks where the spectra are influenced by the chain length: 1441 and 1357 cm-1. The peak at 1441 cm-1 consists of at least two different signals and the proportion of these two signals varies as the chain length increases. The lack of variation in the rest of the region indicates that the fingerprint region is basically signals from the carbonyl group. This can also be confirmed by comparison with the spectra of alkanes where there is little absorbance in the fingerprint region.

- 46 -

Figure 5.4-4)

The fingerprint area of C12 to C22 FAME. Spectra are normalised after the carbonyl peak maximum.

0,1

0,2

0,3

0,4

0,5

0,6

0,7

100011001200130014001500

12:0 14:0 16:0 18:0 20:0 22:0

Nor

mal

ised

abs

orba

nce

cm-1

1441cm-1

1357cm-1

5.4.2) Number of double bonds The effect of introducing cis double bonds can bee seen in Figure 5.4-5a showing the spectra of 18:0 and all-cis 18:1, 18:2 and 18:3. The effect of introducing trans double bonds is seen in Figure 5.4-5b showing the spectra of 18:0 and all-trans 18:1, 18:2 and 18:3.

The difference between cis and trans double bonds can also be seen in Figure 4.3-1, which is comparing the spectra of 20:2 tt and 20:2 cc. Note that in addition to the obvious difference in the regions of the trans peak at 970 and the cis peaks at 3020 cm-1 and 700 cm-1, there is also a small difference in the region of the CH-peak.

It is clear that the number of trans double bonds can be deduced from the trans peak at 970 cm-1 alone. As long as the number of trans double bonds and the chain length is constant, the number of cis double bonds may also be estimated from several regions in the spectrum.

5.4.3) Prediction of cis-trans ratio in mixed peaks In real samples the chromatographic separation between cis and trans isomers is limited (also on very polar columns). Thus, to be able to quantify the cis and trans content in samples of unhydrogenated fats one must be able to quantify the average number of cis and trans double bonds in peaks that are mixtures of several cis and trans isomers.

In a preliminary experiment this was tested on mixtures of 18:1 cis / 18:1 trans and of 18:2 cis / 18:2 trans. The mixtures varied in cis to trans ratio from 100 percent cis to 100 percent trans. Large amounts were injected on the column; consequently the presence of white noise in the spectra can be neglected.

As long as the cis to trans ratio is to be estimated in peaks where the total number of double bonds and the chain length is constant the degrees of freedom in the system is 1, and mono-variate regressions may therefore be applied. The cis peak at 3051 to 2975 cm-1 and trans peak at 999 to 938 cm-1 were integrated and plotted against the average number of cis and trans double bonds in the peaks. The plots are shown in Figure 5.4-6 a-d. The spectra of the pure 18:1 and 18:2 isomers can be seen in Figure 5.4-5.

- 47 -

2800285029002950300030503100

18:0 18:1c 18:2cc 18:3ccc2.5

3.0

3.5

4.0

2.0

1.5

1.0

0.5cm-1

0,1

0,2

0,3

0,4

0,5

0,6

0,7

5006007008009001000110012001300140015001600

cm-1

Figure 5.4-5a) Parts of the IR spectra of 18:0, and all-cis 18:1, 18:2 and 18:3. Spectra are normalised to the carbonyl maximum

0,5

1,0

1,5

2,0

2,5

3,0

3,5

4,0

2800285029002950300030503100

18:0 18:1t 18:2tt 18:3ttt

cm-1

0,1

0,2

0,3

0,4

0,5

0,6

0,7

5006007008009001000110012001300140015001600

cm-1

Figure 5.4-5b) Parts of the IR spectra of 18:0, and all-trans 18:1, 18:2 and 18:3. Spectra are normalised to the carbonyl maximum

The plots in Figure 5.4-6 reveal linear correlations between the areas of the peaks and the amounts of cis and trans double bonds. Thus mono-variate regression can be applied to estimate the average cis and trans content in confounded peaks when the number of double bonds are known. As long as the total number of double bonds are known the degrees of freedom in the system is 1 and consequently the amount of cis double bonds can be calculated by subtracting the amount of trans double bonds, and vice versa.

If the total number of double bonds is not known, quantification of the cis content is more complicated. Figure 5.4-6e and f shows the areas under the peaks plotted against the cis and trans content with the 18:1 and 18:2 isomers in the same plots. There is still a good relationship between the area of the trans peak and the content of trans double bonds in the mixture. However, the relationship between the cis double bonds and the cis peaks is disturbed by weak absorption of trans double bonds in the region of the cis peak. Thus mono-variate regression applied to the cis peak will fail. As long as there is variation in the total number of double bonds the amounts of cis double bonds can not be calculated by subtracting the amounts of trans double bonds. In this case the total number of double bonds is either 1 or 2. In real mixtures this number is usually not an integer.

To achieve a correct estimate for the cis content, multivariate regression techniques must be applied. The predicted vs. Measured values for a two component PLS regression is shown in Figure 5.4-7. The result show that PLS is able to give a good prediction of the cis content in the peaks, even under the influence of varying trans content. Attempts to build general regression models (no limitations in the degrees of freedom) by PLS regression are discussed in the next section.

- 48 -

Cis trans

y = 0.7544x + 4.9025R2 = 0.9641

4.8

5.0

5.2

5.4

5.6

5.8cis area(cm-1)

0.0 0.1 0.2 0.3 0.4 0.5 0.6 0.7 0.8 0.9 1.0

Average cis

trans0.10.20.30.40.50.60.70.80.91.0

y = 1.6305x + 0.2778R2 = 0.9963

0.00.20.40.60.81.01.21.41.61.82.0

trans area(cm-1)

Average trans

0.0 0.1 0.2 0.3 0.4 0.5 0.6 0.7 0.8 0.9 1.0cis0.10.20.30.40.50.60.70.80.91.0

Figure 5.4-6a) The average cis content against the area of the cis peak (spectroscopic) in 18:1

Figure 5.4-6b) The average trans content against the area of the trans peak (spectroscopic) in 18:1

y = 0.6859x + 5.7315R2 = 0.9953

5.6

5.8

6.0

6.2

6.4

6.6

6.8

7.0

7.2

0.0 0.2 0.4 0.6 0.8 1.0 1.2 1.4 1.6 1.8 2.0

cis area(cm-1)

Average cis

trans0.20.40.60.81.01.21.41.61.82.0

y = 1.5723x + 0.5279R2 = 0.9947

0.00.40.81.21.62.02.42.83.23.64.0

0.0 0.2 0.4 0.6 0.8 1.0 1.2 1.4 1.6 1.8 2.0

trans area(cm-1)

Average trans

cis0.20.40.60.81.01.21.41.61.82.0

Figure 5.4-6c) The average cis content against the area of the cis peak (spectroscopic) in 18:2

Figure 5.4-6d) The average trans content against the area of the trans peak (spectroscopic) in 18:2

y = 1.044x + 5.0226R2 = 0.7875

4.6

5.0

5.4

5.8

6.2

6.6

7.0

7.4

0.0 0.2 0.4 0.6 0.8 1.0 1.2 1.4 1.6 1.8 2.0

cis area(cm-1)

Average cis

y = 1.6469x + 0.3694R2 = 0.9824

0.00.40.81.21.62.02.42.83.23.64.0

0.0 0.2 0.4 0.6 0.8 1.0 1.2 1.4 1.6 1.8 2.0

trans area(cm-1)

Average trans

Figure 5.4-6e) The average cis content against the area of the cis peak (spectroscopic) in 18:1 and 18:2.

Figure 5.4-6f) The average trans content against the area of the trans peak (spectroscopic) in 18:1 and 18:2

- 49 -

Figure 5.4-7)

Predicted vs. Measured when the average cis content is estimated by two component PLS regression.

0.5 1.0 1.5 2.0Predicted avg. cis

0.5

1.0

1.5

2.0

Elements:Slope:Offset:Correlation:RMSEP:SEP:Bias:

181.001701

-0.0048620.9953640.0590370.0606490.003369

Avg. cis

5.4.4) Variation in several parameters, PLS regression In the above mentioned experiments usually only one parameter was varied at time (in statistical terms: there is only one degree of freedom). In real samples one must be able to handle spectra where the three parameters: chain length, the number of cis double bonds and the number of trans double bonds vary (three degrees of freedom).

As seen in Figure 4.3-1 and Table 4.3-1 the estimation of the number of trans double bonds should still not be difficult. The chain length or number of double bonds does not influence the peak at 970cm-1; thus the trans content may be estimated from this peak alone. The number of carbons in the molecule is not a problem either. In GC-IR this parameter can simply be estimated from the chromatographic retention time.

The cis isomers cause larger problems. There are no regions in the infrared spectrum where there are strong signals from cis double without influence from the two other parameters, trans double bonds and number of carbons. Thus the estimation of the number of cis double bonds from the spectra is not a simple task.

This problem must be solved by combining the information found in several variables (wavelengths in IR spectra). Thus multivariate regression must be applied. Because of high colinearity in the spectra multiple linear regression (MLR) applied directly on the spectra may fail, thus partial least squares (PLS) regression is therefore used.

Both the average numbers of cis and trans double bonds need to be estimated in mixed peaks. The chain length is not important to predict, but when using models built on fatty acids with different chain lengths, more spectra will be available and thus the robustness of the regression models will increase. There is also less work involved in applying one common model for all chain lengths than using several different models. Thus the cis and trans double bonds must be estimated in a system with three degrees of freedom, the chain length, contents of cis double bonds and contents of trans double bonds vary.

Isomers of the most common fatty acids methyl esters with one to four double bonds was made from pure standards (Nu-Chek Prep, Elysian, MN) by heating in dioxane with p-toluenesulfinic acid as described in appendix 8.5. The isomers was subsequently purified and separated by the number of cis and trans double bonds on silver ion HPLC as described in appendix 8.6. In this way four fractions could for example be achieved from 18:3n-3, the first

- 50 -

fraction contained the all-trans 18:3n-3 isomer. The second fraction contains the three isomers with one cis double bond, the third fraction the three isomers with two cis isomers, and the fourth fraction contained all-cis 18:3n-3. Since the last fraction always contained the pure cis isomer (the starting material) this fraction was not collected.

The various fractions were injected in the GC-IR and the spectra were collected. In general it was not possible to separate isomers with the same amounts of cis and trans double bonds on the GC, i.e. 9c,12c,15t-18:3 could not be separated from the 9t,12c,15c and 9c,12t,15c isomer. The spectral difference between these compounds can be neglected (if any) and the average spectrum is therefore representative for all isomers.

A database of the spectra achieved from the various cis and trans isomers and the saturated fatty acids were built. The isomers used in the database are summarised in Table 5.4-1. The absorbance spectra were normalised to the carbonyl maximum and applied in the PLS calibration models. Some outliers, possible caused by too low amounts injected or some baseline drift in the detector during the chromatographic run, where removed from the dataset prior to calibration.

The PLS calibrations can be applied on the whole spectrum or parts of the spectrum. In some cases the calibrations may be improved by using the first derivatives or the second derivatives of the spectra as x-variables. Several combinations were tried and the results are summarised in Table 5.4-1.

For the prediction of the average number of cis and trans double bonds the first derivative of the spectra gave the most accurate predictions in PLS. This indicates that there might have been some baseline drift in the spectra. Although the differentiation of the spectra removes baseline drift it also reduces the ratio of signal to white noise in the spectra. Thus derivatisation of spectra should be applied with care. When spectra of poor quality (large amounts of white noise) were predicted by the models, the underivated spectra gave the best results.

Table 5.4-1)The results from several PLS regressions, variations in the pre-treatment of spectra and the selection of spectral regions in the models

Spectra pre-treatment Prediction: Average deviation (abs)

None Number of cis 0.09 None Number of trans 0.10 None Number of dbb 0.10 None Chain length 0.24

First derivative Number of cis 0.06* First derivative Number of trans 0.06 First derivative Number of dbb 0.08 First derivative Chain length 0.32

Second derivative Number of cis 0.09 Second derivative Number of trans 0.07 Second derivative Number of dbb 0.10 Second derivative Chain length 0.27

None, only 900-1030 cm-1

Number of trans 0.08

First derivative, only 900-1030 cm-1

Number of trans 0.05*

* Predicted vs. Measured shown in Figure 5.4-9 and 5.4-10

- 51 -

For the prediction of trans content, models based on the trans peak (1030-900 cm-1) gave better predictions than models based on the entire spectrum. The first derivative of selected spectra (compare with Figure 5.4-5) are shown in Figure 5.4-8 while predicted vs measured for the PLS calibrations of the number of cis and number of trans double bonds are given in Figure 5.4-9 and 5.4-10.

Figure 5.4-8a)

Derivatives of CH region, 18:1 and 18:2 cis and trans isomers

Cm-128502900295030003050-0,5-0,4-0,3-0,2-0,10,00,10,20,30,40,5

Cis peak≈ 3020cm-1

CH-region

Figure 5.4-8b)

Derivatives of trans peak region, 18:1 and 18:2 cis and trans isomers

cm-192094096098010001020-0,10-0,08-0,06-0,04-0,020,000,020,040,060,080,10

18:1t18:2tt18:1c18:2cc

Trans peak967cm-1

Figure 5.4-9)

Predicted vs measured for the PLS regression of number of cis double bonds based on the first derivative spectra of the regions 3200-2800cm-1 and 1850-550cm-1.

- 52 -

Figure 5.4-10)

Predicted vs measured for the PLS regression of number of cis double bonds based on the first derivative spectra of the region 1030-900cm-1.

5.4.5) Separation of cis from trans isomers, linearity and limits of detection. The signals from cis and trans double bonds are illustrated in Figures 4.3-1 and 5.4-5. The trans signal at 970 cm-1 is not influenced by other functional groups. There is also a trans signal below the large C-H peak at approximately 2920 cm-1. There is a strong cis signal at approximately 3020 cm-1 and a broader signal below 750 cm-1, which was outside the range of the detector (the detector was later changed to a wide band detector, so this region is included in later studies).

To test the detection limits and ability to distinguish between isomers with different amounts of cis and trans unsaturation, mixtures of cis and trans 18:1n-9 and cis-cis and trans-trans 18:2n-6 were injected at different amounts varying from approximately 5 ng to 6000 ng on the column.

The spectra were normalised to the maximum of the carbonyl peak and the wavelengths of 3500 to 2800 cm-1 and 1030-910 cm-1 were used as variables in PCA. Even though the number of variables is large, there are only two degrees of freedom in the dataset, the number of trans double bonds and the number of cis double bonds. Fatty acid chain length is constant since all isomers are C18.

The PCA score plot is shown in Figure 5.4-11. Each of the four fatty acid isomers are grouped in each of the quadrants of the score plot. The identification of the isomers tends to fail, i.e. the sample ends up in or near wrong quadrant, at approximately 10 ng injected. At approximately 20 ng injected the isomers end up well inside the right quadrant, but deviates significantly from the remainder of the groups. Thus spectra of comparative qualities are good enough for identification of pure isomers but may not be good enough for estimation of cis-trans contents in mixed peaks, a task which normally need higher quality spectra. The spectra from 40 ng and above show only minor deviations from the remainder of the groups.

Supervised classification methods (e.g. PLS or SIMCA) are usually more powerful than the unsupervised methods (PCA, KNN, etc.). By the use of classification with PLS with the classes as dependent variables only one spectrum (5.2 ng 18:2 tt) had too low quality to be correctly identified by the models.

Selected spectra are shown in Figure 5.4-12 a-d. A comparison of these spectra with the score plot illustrates the benefit achieved by using all wavelengths of selected spectral regions and combining them into LV models. The trans peak at 970 cm-1 is hardly visible in the spectra

- 53 -

obtained at 5-20 ng. Still the 5 and 10 ng spectra of highest quality are located in the right quadrant because information of other parts of the spectrum is also used.

-1.0

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RESULT6, X-expl: 61%,19%

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18:1 t

18:1 c

PC1

PC2 Scores5.1ng

20.8ng

5.2ng 15.0ng

7.5ng 10.4ng9.3ng

18.5ng 18.5ng

4.7ng29.9ng

20.2ng

10.1ng

Figure 5.4-11) PCA of 18:1 t, 18:1 c, 18:2 tt and 18:2 cc with different amounts injected on the GC column. Samples in the outer ellipse deviate from the rest of the samples because of low spectral quality, but are still located in the correct quadrant.

Another advantage of the LV methods is the possible reduction of noise. Because LV methods tend to explain the systematic variation in the dataset, uncorrelated noise (white noise) is not explained and hence left in the residual matrix. Correlated noise can also be removed because it is often explained by different LV than the signals. This is explained in section 4.5.6. In Figure 5.4-11 the two first principal components will most likely explain all systematic variation, because the dataset has only two degrees of freedom, thus the 20% of the variance that is not explained by the two first principal components is assumed to be basically white noise.

Another result, which can be seen in Figure 5.4-11 is that, except from the change in the relative content of white noise and signal to noise ratio, the spectra are not influenced by the amounts injected. Thus there is excellent linearity and equal response at all wavelengths, and no correction for the amounts injected need to be applied.

It is important to note that the limits of identification indicated above are not valid in general. These limits are dependent on the similarity of the spectra to be identified and the specific absorption for the molecule. The quality of the spectra is also very dependent on the chromatographic conditions. Sharper peaks, which gives spectra of higher quality may be achieved by using higher temperatures and less polar columns, but this will usually be on the cost of chromatographic separation.

It is also important to distinguish between the limits of detection, quantification and identification. There are several definitions of these parameters. The limits of detection and quantification usually refer to the limits where a compound in the chromatograms can be detected or quantified with a certain degree of reliability. The limit of identification refers to

- 54 -

the spectra and is usually very dependent on the compounds to be identified and possible interferents.

Figure 5.4-12a)

Percent transmittance spectrum of 20.2ng 18:1n-9

99,7

99,75

99,8

99,85

99,9

99,95

100

70010001300160019002200250028003100340037004000

%T

1/cm

Figure 5.4-12b)

Percent transmittance spectrum of 10.1ng 18:1n-9

99,87

99,89

99,91

99,93

99,95

99,97

99,99

100,01

70010001300160019002200250028003100340037004000

%T

1/cm

Figure 5.4-12c)

Percent transmittance spectrum of 5.1ng 18:1n-9

99,9299,9399,9499,9599,96

99,9799,9899,99

100100,01

70010001300160019002200250028003100340037004000

%T

1/cm

In the experiment mentioned above the peaks were not visible in the chromatograms at 5 and 10 ng injected. Still the compounds could be identified from their spectra (The retention times were given by the MSD, which was run in parallel with the IRD). It may seem like a paradox that compounds that are below the detection limit can still be identified. This can be explained by the following: The Gram-Schmidt chromatogram is constructed from the signals from all wavelengths in the IR spectrum. In large regions of this spectrum there are no signals of interest, for instance in the regions from 4000 to 3100 cm-1 and from 2800 to 1850 cm-1. But still large amounts of noise are accumulated from this regions, leading to low signal to noise ratios. In the PCA model only the region of interest are included, in addition PCA has the ability to separate signals from noise.

- 55 -

5.4.6) Optimal detector parameters: Optical resolution, coadd factor Two parameters regarding the sampling of infrared spectra must be optimised, the optical resolution and the coadd factor. The optical resolution can be set to 4, 8 or 16 cm-1. The coadd factor describes the number of spectra which is added into one spectrum before the data are saved during sampling, 4, 8 or 16 spectra are averaged. These parameters also effect the sampling speed, i.e. the number of spectra that are sampled from each chromatographic peak.

By increasing the spectral resolution more detailed information about the peaks in the spectra is achieved. However, the sampling time will also increase, leading to poorer description of the chromatographic peaks. Sampling at a larger number of wavelengths may also increase the amount of noise sampled, leading to a reduction in signal to noise ratios.

By increasing the coadd factor, fewer samples are stored in the computer, thus leading to decreased datafile sizes. However the number of spectra sampled from each chromatographic peak is also reduced, ideally at least 10-12 spectra should be sampled to get an accurate estimate of the peak area. Theoretically the effect of the coadd factor on the quality on average spectrum of the peak can normally be neglected; averaging of spectra before or after data storage should make no difference.

To investigate the effect of these two parameters 5-50 ng trans 18:1, all-cis 18:2 and all-cis 18:3 were injected on a 30 m CP-Wax 52 column. With these low amounts injected, the spectra will contain large amounts of noise. PCA were done on the spectral region from 3100 to 2800 cm-1, which are the CH-signals. The spectral resolution and the coadd factor was varied according to the arrangement given in Table 5.4-2

Table 5.4-2) Experimental design for measurement of the effects of optical resolution and the coadd factor

Series Optical resolution Coadd factor 0 4 4 1 8 4 2 16 4 3 8 4 4 8 8 5 8 16

Series 0-2 investigate the effect of varying the optical resolution at constant coadd factor, while the coadd factor are varied in series 3-5. Series 1 and 3 are replicates. The default instrument parameters are optical resolution 8 and coadd factor 4.

The PCA cross validations indicated that two components were necessary to describe the systematic variation in the spectra, this also corresponds with the number of degrees of freedom, which is two. The score plots, and the amount of variance that is explained by the two first principal components are thus used as a measure of the spectral qualities. The results are summarised in Table 5.4-3 and selected score plots are given in Figure 5.4-13.

The matrix variance is the overall variance in the dataset, divided by the number of samples the average variance per sample is found. There is a small variation in the number of samples, because severe outliers were deleted from the dataset. The explained variance denotes the percent of the matrix variance that is explained by the two first principal components. The residual variance after two principal components is assumed to be primarily white noise. In

- 56 -

addition to the PCA with all three isomers, a PCA was also performed on a dataset with only 18:2 and 18:3, which was the isomers that was most difficult to separate when all isomers were included.

Regarding the optical resolution two trends are visible, both in the datasets with three isomers and in the dataset with two isomers. The variance per sample is reduced as the resolution (and thus the number of variables) is decreased. At 16 cm-1 resolution the variance is half that of 4cm-1. It is important to know how the reduced sample variance due to the reduction of variables effect the signal to noise ratio. This can be investigated by looking at the variance explained by the two first principal components. There is an increase in the explained variance when moving from 4 cm-1 resolution to 8 and 16 cm-1 resolution. There is no improvement when moving from 8 to 16 cm-1. In other words, the signal to noise ratio is lower at 4 cm-1 than at the other two resolutions. The quality of the data is also indicated in the score plots. Figure 5.4-13 a-c shows the score plots achieved with all three isomers at the different resolutions. The plots indicate a better separation of the samples at 16 cm-1 than at the other two resolutions. Table 5.4-3) Effect of optical resolution: Total matrix variance and percent explained variance by the two first principal components for the experiments described in Table 5.4-2.

Series 0 18:1, 18:2, 18:3

Series 1 18:1, 18:2, 18:3

Series 2 18:1, 18:2, 18:3

Optical Resolution 4 8 16 Coadd factor 4 4 4 No samples 53 50 51 Matrix variance 0.138 0.118 0.070 Variance per sample 0.0026 0.0024 0.0014 Explained variance PC1 38 64 62 Explained variance PC2 18 20 22 Explained variance PC1+PC2 56 84 84 Series 0

18:1, 18:2 Series 1

18:1, 18:2 Series 2

18:1, 18:2 Optical Resolution 4 8 16 Coadd factor 4 4 4 No samples 34 33 33 Matrix variance 0.083 0.063 0.043 Variance per sample 0.0024 0.0019 0.0013 Explained variance PC1 24 56 39 Explained variance PC2 20 16 31 Explained variance PC1+PC2 44 72 70

This leads to the conclusion that resolution at 4 cm-1 should be avoided. A resolution of 16 cm-1 seems to give the spectra of highest quality. However, the gain in spectral quality when moving from 8 to 16 cm-1 is limited. The PCA was only performed on the CH-region and not on the whole spectrum. High spectral resolution may be beneficial in some cases, especially when investigating absorbtions in the fingerprint area. Thus 8 cm-1 was selected as a standard in the remaining method development.

- 57 -

Regarding the coadd factor, there is no clear trends seen in Table 5.4-4. The average variance per sample is approximately equal in the three series. Nor is there any trend seen in the percent explained variance. Thus the coadd factor should be set as low as possible (i.e. 4), to achieve the best chromatographic resolution possible.

Figure 5.4-12a)

Score plot of the separation of 18:1t (red), 18:2cc (green) and 18:3ccc (blue) resolution 4cm-1.

Numbers indicate nanograms injected

The CH region 3050-2800cm-1 were used as variables, variable weights = 1

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5

-6 -4 -2 0 2 4 6

2550

50

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2535

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4040

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3045

45

PC1

PC2

PC1 38%, PC2 18%Opt 4, Coadd 4

Figure 5.4-12b)

Resolution 8cm-1, other parameters as in fig. 5.4-12a

20

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PC1

PC2

PC1 64%, PC2 20%Opt 8, Coadd 4

Figure 5.4-12c)

Resolution 16cm-1, other parameters as in fig. 5.4-12a

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- 58 -

Table 5.4-4) Effect of coadd factor: Total matrix variance and percent explained variance by the two first principal components for the experiments described in Table 5.4-2.

Series 3 18:1, 18:2, 18:3

Series 4 18:1, 18:2, 18:3

Series 5 18:1, 18:2, 18:3

Optical Resolution 8 8 8 Coadd factor 4 8 16 No samples 54 47 51 Matrix variance 0.123 0.090 0.122 Variance per sample 0.0023 0.0019 0.0024 Explained variance PC1 61 58 57 Explained variance PC2 20 18 27 Explained variance PC1+PC2 81 76 84 Series 3

18:1, 18:2 Series 4

18:1, 18:2 Series 5

18:1, 18:2 Optical Resolution 8 8 8 Coadd factor 4 8 16 No samples 36 32 35 Matrix variance 0.071 0.070 0.079 Variance per sample 0.0020 0.0022 0.0023 Explained variance PC1 56 48 60 Explained variance PC2 13 19 15 Explained variance PC1+PC2 69 67 75

5.4.7) The IR spectra of non-methylene interrupted fatty acids Most fatty acid isomers in hydrogenated fats are of the NMI type. Since pure standards of NMI fatty acid isomers are difficult to prepare and separate all polyunsaturated fatty acid isomers used in the calibrations are of the regular methylene interrupted type. Spectral differences between the two types of isomers may reduce the accuracy of the PLS predictions.

To investigate possible differences spectra of all-trans 20:2n-6 and 22:2n-6 were compared to the spectra of the first trans diene fraction from a hydrogenated fat. This fraction contains a mixture of several NMI isomers, and may also contain small amounts of methylene interrupted isomers. Selected areas of the spectra are compared in Figure 5.4-13a and b.

The differences are apparent in certain regions of the fingerprint region, but there seem to be no difference in the trans peak region. There is also a very small difference in the CH region where the NMI spectrum has slightly stronger absorbance at the CH2 maximum at 2925 cm-1 and seems to have slightly lower absorbance at the CH3 shoulder at 2970 cm-1. However this small difference might be within normal instrument variation. Results from Strøm (1994) prove that also double bond positions can have some influence on the spectra in his region.

The differences seen in Figure 5.4-13 were also seen for the C22 isomers. Ideally, a similar comparison should be made also for the cis isomers. However, it was not possible to isolate pure NMI cis isomers from hydrogenated fats, because of the presence of low amounts of trienes in these fractions. Pure NMI cis dienes must be made by hydrazine reduction (Ratanayake 1990) or other synthetic procedures.

In conclusion, the prediction of trans content based on the peak at 970 cm-1 alone is probably not influenced by positional isomerism. Positional isomerism might have some influence on

- 59 -

the prediction of cis isomers, since these models must be built on larger proportions of the spectra. The fingerprint region should be excluded from the cis calibrations.

Figure 5.4-13a)

The CH region of all-trans 20:2n-6 and all-trans 20:2 NMI fraction from PHFO

cm-1285029002950300030503100

NMI 20:2 tt 20:2n6 tt

0,5

1,0

1,5

2,0

2,5

3,0

0,0

Figure 5.4-13b) The fingerprint region of 20:2tt and 20:2 NMI fraction from PHFO

cm-180090010001100120013001400150016000,0

0,1

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5.4.8) Chromatographic response and response factors The chromatographic response for various methyl esters has been investigated by analysis of a reference mixture (GLC-461, Nuchek Pep. Elysian, MN) of FAMEs with variations in chain length and the number of double bonds.

The detector responses of the Gram-Schmidt (GS) chromatogram are given in Figure 5.4-14. The majority of the GS-signal is caused by C-H signals. As double bonds are introduced the number of C-H bonds are significantly reduced while there is only a minor decrease in the molecular weight. This can be illustrated by introducing a double bond in 18:0. The number of C-H bonds drops from 38 to 36, a reduction of 5.2%, while the molecular weight drops from 298 to 296, a reduction of 0.67%. This decrease in the ratio of C-H bonds to the molecular weight leads to decreased detector response as the number of double bonds increase. Figure 5.4-14 illustrates that the number of double bonds has severe impact on the detector response while the influence of the chain length can be neglected. The detector response drops to 50% of the values achieved for saturated fatty acids when three double bonds are introduced.

- 60 -

0,3

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ctor

Double bonds6

c14 c16c18 c20c22

0 1 2 3 4 5

14 0,98 0,78

16 0,98 0,76

18 1,00 0,77 0,63 0,50

20 1,00 0,80 0,66 0,50 0,44 0,41

22 1,00 0,85 0,68 0,38 0,34

Double bonds

Cha

in le

ngth

Figure 5.4-14) Detector response relative to 18:0. Gram-Schmidt chromatogram based on the whole spectrum

Application of selected wavelength chromatograms (SWC) of the carbonyl peak at 1790-1710 cm-1 may circumvent the reduced detector response for unsaturated fatty acids. On a molar basis the carbonyl signal is essentially constant for all FAME isomers. However if the results are to be expressed on a weight basis, the chromatographic areas must be corrected for different carbonyl content relative to the molecular weight. The detector response relative to the molecular weight for SWC chromatograms of the carbonyl peak is given in Figure 5.4-15.

Res

pons

e fa

ctor

Double bonds

0,7

0,8

0,9

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c14 c16c18 c20c22

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18 1.00 0.94 0.98 0.95

20 0.90 0.89 0.88 0.88 0.88 0.85

22 0.81 0.84 0,84 0.77 0.80

Double bonds

Cha

in le

ngth

Figure 5.4-15) Detector response relative to 18:0. SWC chromatogram based on the carbonyl maximum

If correction factors for different weight to carbonyl ratios are not applied, the results are expressed on a molar basis. Even though trans content are normally expressed on a weight basis, which is also the case in this study, it may be more relevant to express the results on a molar basis. This can be illustrated by comparing elaidic acid (18:1 trans) with brassidic acid (22:1 trans). Both fatty acids have one trans double bond, but when the trans content is expressed on a weight basis brassidic acid has only 83% the trans content of elaidic acid.

This difference is important when the trans content in marine fats are discussed, because most of the trans content is in C20 and C22 fatty acids. It is of less importance for trans values in vegetable fats, because the majority of the trans double bonds are in the C18 fatty acids, which is also used as standards when the trans content is measured by IR.

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5.5) Rapid GC-IR method - Validation Based on the results from chapter 5.2 and 5.4 a GC-IR method based on a single GC run was developed. The aim of the method was to be able to quantify the amounts of SFA, trans MUFA, cis MUFA, total PUFA and the cis and trans double bond content in PUFA for each chain length. The results are validated by calculation of the total trans content and comparison with total trans analysed by AOCS method Cd 14-95.

5.5.1) Chromatographic quantification On the basis of the results in 5.2 CP-Wax 52cb were selected as the GC column. A complete description of the gas chromatographic parameters is given in appendix 8.7. SWC of the carbonyl peak of the second derivative spectra were used as chromatographic trace. By using the carbonyl peak as the base for the chromatographic quantification, it is not necessary to correct for the number of double bonds in the FAME molecules. However corrections for different chain lengths may be necessary (see 5.4.8).

A GC-IR chromatogram is shown in Figure 5.5-1. For each chain length three peaks are quantified, as illustrated by the expanded C20 area. By taking the average spectra of the peaks, the average cis and trans content of the MUFA and PUFA peaks are calculated by means of the PLS regression model described in section 5.4.4. Thus the amounts of trans monoenes, cis monoenes and average amount of cis and trans double bonds in PUFA are found.

5 7 9 11 13 15

min

10,7 10,9 11,1 11,3 11,5 11,7 11,9

Figure 5.5-1) GC-IR trace of PHFO sample 3 in Table 5.3-1.

C14

C16

C18

C20 C22

20:0

20:1

C20 PUFA

5.5.2) Spectroscopic parameters Each spectrum was purified by background subtraction and corrected for drifting baseline by linear regression. The first derivatives of the spectra where used in the PLS-models. For the prediction of trans content, only the region 1030-900 cm-1 was used. The total number of double bonds can be estimated either by summing the cis and trans content, or by direct PLS calibration for total double bonds. For the direct prediction of total double bond content, the areas 3200-2800 cm-1 and 1030-900 cm-1 were used in the PLS model, first derivative spectra.

- 62 -

5.5.3) Calculations and validation The method was validated by analysis of 14 different samples for iodine number and AOCS total trans (infrared method). These values were compared to the values obtained by summing all values for the GC-IR method. Data for the 14 samples are given in Table 5.5-1. The calculations of the sum trans and sum double bonds from the GC-IR method is illustrated in Figure 5.5-2.

APeak

Number12::n

CAverage

TransT1T2::

Tn

DTrans

AmountA%1 T1A%2 T2

::

A%n Tn

EAverage

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FDbb

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BArea

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::

A%n

Σ = 100Figure 5.5-2) The calculation of total trans and total double bonds. Column B is the area percents from the chromatogram. Column C is the average trans content from the PLS calibration of the average spectra from the peaks. Column D is the trans content in each peak and is found by multiplying Column C and D. The sum of the elements in column D is the total trans value from GC-IR listed in Table 5.3-1. The same calculations are done for the total double bonds (cis+trans) in columns E and F. The sum of column F is the total double bond value from GC-IR listed in Table 5.3-1.

In Table 5.5-1 and Figure 5.5-3 the sum trans analysed by AOCS Cd 14-95 is compared to the sum trans calculated from the GC-IR results. There is a difference by definition in the way the two results are reported. The GC-IR method gives the results as number of double bonds per number of fatty acid molecules, i.e. mole percent trans double bonds.

By definition the AOCS method reports the results as percent trans given as percent elaidic acid, thus pure elaidic acid FAME should give a value of 100%. The initial sample amounts are measured by weight and the trans peak signal is not normalised to the carbonyl peak maximum, which leads to the following problems:

Pure brassidic acid (22:1 trans) has a molecular weight that is 18.7 percent higher than elaidic acid. Thus equal amounts by weight of the two fatty acids will contain 84.2 percent as many brassidic acid molecules as elaidic acid molecules. Since the number of trans double bonds in the two molecules are equal, 100 percent 22:1 trans would give a trans value of only 84.2 when analysed by AOCS Cd 14-95. Similarly samples with average chain length shorter than C18 will be overestimated by the AOCS method, pure 16:1 trans and 14:1 trans would be reported as 110 and 123 percent respectively.

Since the average chain length of the samples is known, correction factors can be applied that corrects the AOCS value to mole percent trans. However, as seen in Table 5.5-1 the average molecular weight of most samples is close to 298 g/mole, which is the molecular weight of 18:0. Thus the correction factors for the soybean and fish oil samples will be in the range 0.99 to 1.01, and may therefore be ignored. For palm oil the correction factor is approximately 0.96, which will give an error of only 4%. For the two pure palm oil samples factors of 0.78

- 63 -

and 0.85 would have to be applied, however both these samples has values of both trans and iodine value close to zero, thus the accuracy in these values are probably low irrespective of the discrepancy discussed above.

It was chosen not to correct the AOCS trans value to molar percent, however, when samples with very short chains are considered, e.g. milk fat or certain vegetable oils, the two values are not directly comparable.

Table 5.5-1) Fats used in the validation experiment. Abbreviations: FH = fully hydrogenated, PH = partially hydrogenated, UH = unhydrogenated, SBO = soybean oil, CO = coconut oil, PO = palm oil, FO = fish oil, IV iodine value

No Sample type Avg. Mol. Wt(g/mol)

Σ trans AOCS

Cd 14-95

Σ Trans (GC/IR mole

percent)

Iodine Value Σ Dbb (GC/IR mole

percent)

1 FH SBO 295 1,3 2,4 1,60 0,00 2 PH PO 285 1,4 2,5 57,0 55,5 3 PH FO Mp 31 293 57,6 62,5 79,0 81,9 4 FH SBO + FH CO 252 0,0 2,9 2,20 0,2 5 PH FO Mp 51 294 21,1 18,4 32,0 21,2 6 PH SBO mp 32 295 41,3 55,7 78,0 87,0 7 PH SBO mp 41 295 43,4 52,2 66,0 70,5 8 FH SBO + FH CO

+UH VO 270 0,2 2,9 46,0 52,2

9 PH FO mp 27 302 27,5 30,4 78,0 73,4 10 PH CO (slightly

hydrogenated) 231 1,5 4,8 5,6 6,0

11 PH FO mp 39 294 37,9 36,9 50,0 44,3 12 PH PO mp 51 286 24,0 28,4 37,0 35,9 13 PH PO mp 41 284 15,3 18,9 46,0 44,7 14 PH SBO IV≈100 295 27,2 32,3 94,0 104,5

Regarding the sum of double bonds these values are not directly comparable to the iodine value. The iodine value is per definition grams iodine absorbed by 100 g of fat sample. For pure trioleine (18:1 TAG) which has a molecular weight of 884 g/mol six mole iodine atoms are absorbed per mole trioleine, this gives a iodine value of 86, while the sum of double bonds analysed by the GC-IR method should be 100.0. (See also AOCS recommended practice Cd 1c-85).

Since most FO and SBO samples has an average molecular weight close to 298 the values for trioleine can be used to deduce an approximate correction factor between the two ways of measuring double bond content, which is 100 / 86 = 1.16. Thus the formula for the regression line in Figure 5.5-4 should be y = 1.16x. The lower estimate for the regression line indicates that the number of double bonds might be slightly underestimated by the GC-IR method.

In the samples with lower average chain length than C18 the factor changes. Pure 16:1 TAG would give an iodine value of 95 and pure 14:1 TAG would give a value of 104.7, thus in samples with shorter chains the iodine value may be higher than the GC-IR value.

- 64 -

In Figure 5.5-3 and 5.5-4 the GC-IR values are compared with the AOCS Cd 14-95 and the iodine value. The rather good correlation with standard methods when the GC-IR results are summed to one value indicates that the results from GC-IR make sense.

There is a very large variation in the samples selected for validation. The method has not been tested for the ability to distinguish between very similar samples. Nor have there been established limits of detection, limits of quantification or accuracy figures. The differences between various types fats, hydrogenated or unhydrogenated, is very large. Optimisation and testing of the method towards each type of fat will probably give much better accuracy than achieved in the general method for all samples applied here.

Figure 5.5-3)

AOCS total trans value against total trans calculated from GC-IR. Red circles: PHFO, green: PH/FH SBO, blue: PH/FH PO/CO

y = 1,0962x + 1,6051R2 = 0,969

0

10

20

30

40

50

60

70

0 10 20 30 40 50 60 7

AOCS

GC-IR

0 Figure 5.5-4)

Iodine value against total number of double bonds calculated from GC-IR. Red circles: PHFO, green: PH/FH SBO, blue: PH/FH PO/CO

y = 1,0926x - 4,1138R2 = 0,9763

0

20

40

60

80

100

0 20 40 60 80 100

GC-IR

IV

5.6) The mass spectra of FAME The fundamental concepts of mass spectrometric detection and the mass spectra of FAME are described in section 4.2. Some studies were conducted on trans isomers will be summarised in this section.

5.6.1) Detection of trans isomerism by mass spectra of FAME The literature generally suggest that trans isomerism is not important for the mass spectra of FAME. However, results from the analysis of picolinyl esters of dienes (Leth 1997) indicate

- 65 -

that there might be small differences in the mass spectra that can be detected by application of PCA.

Common FAME reference standards (Nu-Chek Prep, Elysian, MN) was subjected to isomerisation by PTSA in dioxane and purified on Ag-HPLC as described in appendix 8.5. This is the same isomers that were used in the PLS calibration for the number of cis and trans double bonds by IR-spectra. On the polar BPX-70 column, isomers with the same amount of cis and trans double bonds, e.g. 9c,12c,15t-18:3, 9c,12t,15c-18:3 and 9t,12c,15c-18:3 will be partially or completely separated, thus the mass spectrum for each of the isomers may be collected.

The mass spectra were subjected to PCA, an overview score plot with all isomers is seen in Figure 5.6-1. As may be expected from the theories summarised in section 4.2, the plot shows a separation into groups according to the number of double bonds. As the number of double bonds and possible conformations increase, the variations in the spectra also increase, which is illustrated by the larger groups for trienes and tetraenes than for the other groups.

Figure 5.6-1) Overview score plot based on mass spectra with m/z 50-170, Unweighted variables.

Monoenes

Saturated

Dienes

Trienes

Tetraenes

Monoenes The mass spectra of the cis and trans isomers of the most common monoenes were studied. No differences in the spectra that could be related to the double bond geometry were seen. It should be emphasised that the monoenes used in the study had the double bonds in positions far from the carbonyl group (e.g. ∆9 position). When the double bonds move closer to the carbonyl carbon there might be interactions between the two groups that can be effected by cis-trans isomerism.

Dienes

Figure 5.6-2 shows score plot of the mass spectra of dienes. There is a clear separation between the conjugated linolenic acid (CLA) isomers and the other dienes. The methylene-interrupted fatty acids are separated into groups after chain length. Separation into cis and trans isomers was not visible inside these groups. However, this effect may be masked by variation in chain length and double bond position in the two first principal components.

By applying PCA on each of these groups separately a rather diffuse difference between cis and trans isomers is seen. The score plots of the 18:2n-6, 19:2n-6, 20:2n-6 and 22:2n-6

- 66 -

isomers are shown in Figure 5.6-3 a-d. In all plots there is a separation between the all-cis and all-trans isomers. With the exception of the 20:2-isomers, the group with one trans bond is also separated from the groups with zero or two trans double bonds. The two groups with one trans double bond can not be separated, i.e. the position of the trans double bond seems to have no influence on the mass spectra.

The loading plots (not shown) indicate that m/z 68, 54, 55 and 81 are typically higher in the all-cis isomers, than in the all-trans isomers. However, this difference is very small. The mass spectra of 18:2n-6 tt and 18:2n-6 cc are shown in Figure 5.6-3 a and b. The difference between the spectra is approximately at the same level as found on picolinyl derivatives by Leth (1997). Even if there is a partial separation between the groups in PC plots, the difference is probably too small to be of any practical importance in identification of unknown spectra or quantification based on confounded spectra.

Figure 5.6-2)

Score plot of CLA and n-6 methylene-interrupted dienes. Variables as described in Figure 5.6-1.

CLA

18:219:2

20:2

22:2

Trienes

In trienes the introduction of a cis double bond was found to have large influence on the spectra, a result that is of diagnostic importance. Figure 5.6-5 shows the score plot of all trienoic isomers applied in the experiment. The spectra are clearly separated into four groups in the PC plot. The effect of positional isomerism on the mass spectra of polyenes is well known from the literature (Fellenberg 1987, Burkow and Aspenes 1996), explaining the separation between the 9,12,15-18:3 and 6,9,12-18:3 isomers. However, there are only two kinds of positional isomers in the dataset, and the two groups visible for each of the positional isomers is therefore caused by geometrical isomerism. Further inspections of the groups revealed that the geometry of the central double bond in the isomers is of major importance for the mass spectra.

- 67 -

9c,12c-18:2

9t,12t-18:2

9c,12t-18:2

9t,12c-18:2

10c,13c-19:2

19:2ct

10t,13t-19:2

10c,13t-19:2

10t,13c-19:2

Figure 5.6-3a) Score plot of 18:2n-6 geometrical isomers

Figure 5.6-3b) Score plot of 19:2n-6 geometrical isomers

11c,13c-20:2

11c,13t-20:2

11t,13c-20:2

11t,13t-20:213c,16c-22:2

13c,16t-22:2

13t,16c-22:2

13t,16t-22:2

Figure 5.6-3c) Score plot of 20:2n-6 geometrical isomers

Figure 5.6-3c) Score plot of 22:2n-6 geometrical isomers

Figure 5.6-4a) The mass spectrum of tt 18:2n-6

60 80 100 120 140 160 180 200 220 240 260 280 300

55

67

81

95

109

150 263

Figure 5.6-4b)

The mass spectrum of all-cis 18:2n-6

60 80 100 120 140 160 180 200 220 240 260 280 300

55

67

81

95

109

150 263

294

294

- 68 -

Figure 5.6-5)

Score plot of trienes

n-6 isomerscentral dbb trans

n-6 isomerscentral dbb cis

n-3 isomerscentral dbb cis

n-3 isomerscentral dbb trans

The score plots when the geometrical isomers of 18:3n-6, 18:3n-3, 20:3n-3 and 22:3n-3 were subjected to PCA are presented in Figure 5.6-6 a-d. In all plots, the geometry of the central double bond gives a very clear separation along PC1, which explains 33 to 65 percent of the total variation in the data. There is also a clear trend of separation along PC2 caused by the geometry of the double bond closest to the methyl end of the isomers. In the cases of C20 and C22 isomers, the influence of the geometry of the double bond closest to the carboxyl group is large enough to give separate groups for each isomer.

-100

-50

0

50

100

-300 -200 -100 0 100 200 300 400 RESULT4, X-expl: 89%,3%

PC1

PC2 Scores

6t,9t,12t-18:3 (red) and 6c,9t,12t-18:3 (blue)

6t,9t,12c-18:3 (red) and 6c,9t,12c-18:3 (blue)

6t,9c,12c-18:3 (red) and 6c,9c,12c-18:3 (blue)

6t,9c,12t-18:3 (red) and 6c,9c,12t-18:3 (blue)

-300

-200

-100

0

100

200

300

-400 -300 -200 -100 0 100 200 300 400 RESULT7, X-expl: 77%,17%

PC1

PC2 Scores

9t,12c,15t-18:3 (red) and 9c,12c,15t-18:3 (blue)

9t,12c,15c-18:3 (red) and 9c,12c,15c-18:3 (blue)

9t,12t,15c-18:3 (red) and 9c,12t,15c-18:3 (blue)

9t,12t,15t-18:3 (red) and 9c,12t,15t-18:3 (blue)

Figure 5.6-6a) Score plot of 18:3n-6 isomers Figure 5.6-6b) Score plot of 18:3n-3 isomers

-400

-300

-200

-100

0

100

200

300

-400 -300 -200 -100 0 100 200 300 400 RESULT8, X-expl: 71%,23%

PC1

PC2 Scores

11t,14t,17t-20:3

12c,15t,18t-20:3

11t,14t,17c-20:3

11c,14t,17c-20:3

11t,14c,17c-20:3

11t,14c,17t-20:3

11c,14c,17t-20:3

All cis

-400

-300

-200

-100

0

100

200

300

-400 -300 -200 -100 0 100 200 300 400 RESULT10, X-expl: 73%,21%

PC1

PC2 Scores

13t,16t,19c-22:3

13c,16t,19c-22:3

13t,16t,19t-22:3

13c,16t,19t-22:3

13c,16c,19t-22:3

13t,16c,19t-22:3

13t,16c,19c-22:3

All cis

Figure 5.6-6c) Score plot of 20:3n-3 isomers Figure 5.6-6c) Score plot of 22:3n-3 isomers

- 69 -

The mass spectra of four 18:3 isomers are shown in Figure 5.6-7 a-d. The influence of double bond position is seen by comparing all-cis 18:3n-3 (Figure a) with all-cis 18:3n-6 (Figure b). A difference is clearly seen in the lower masses. In both isomers, m/z 93 is the base peak, but in the n-6 isomer m/z 67 has approximately the same value. Important differences may also be seen in other masses in this area. In each of the spectra two masses may be used to accurately determine the positions of the double bonds. The mechanisms that give rise to these ions are illustrated in Figure 4.2-3. Cleavage between double bond number 3 and 4 in the figure gives the diagnostic ion at m/z 108 in the case of an n-3 fatty acid or m/z 150 in the case of an n-6 fatty acid (Fellenberg 1987). The position of the first double bond from the carbonyl group can be told from the cleavage between double bond number 1 and 2 in the figure, which give rise to ions at m/z 236 in the case of a ∆9 double bond (18:3n-3) or m/z 194 in the case of a ∆6 double bond (18:3n-6) (Burkow and Aspenes 1996). The masses for similar ions of other positional isomers are given in the figure.

The effect of introducing trans geometry in the central double bond is seen in by comparing Figure 5.6-7a (all-cis 18:3n-3) with Figure 5.6-7c (18:3n-3 ctc). There is a clear reduction in abundance of m/z 79, which is no longer base peak, and a corresponding increase in m/z 95 relative to the other masses. The diagnostic ions for positional isomerism, m/z 108 and 236 are also significantly reduced, in the case of m/z 236 to background level. Introducing trans geometry in the central double bond of 18:3n-6 reduces both diagnostic ions to background level.

Introducing trans geometry in position ∆15 in 18:3n-3, the effect visible along PC2 in Figure 5.6-6, has less impact on the mass spectra. Even though the effect may seem clear in the score plots, it is difficult to observe any difference between the spectra in Figure 5.6-7a and d.

Further studies to achieve detailed knowledge of why trans isomerism in the central double bond has severe impact on the mass spectra has not been conducted. Nor have other derivatives been investigated. Both the mechanism leading to base peak at m/z 79 and the ions indicating positional isomerism in methylene-interrupted cis systems is blocked or significantly reduced by geometrical isomerism in the central double bond.

A brief explanation may be found by looking at the decomposition mechanisms illustrated in Figure 4.2-3. The position of the double bond system counted from the methyl end of the carbon chain is found by cleavage between double bond 2 and 3 in the figure, while the position counted from the carboxyl group are found by cleavage between double bond 1 and 2. Both the resulting ions has a even numbered mass, which means that they are not formed by simple cleavage reactions, but are formed through rearrangement reactions, usually involving cyclic intermediates. The central double bond in the original molecule is a part of both these ions. If this is of cis geometry, the rearrangements may be sterically hindered, and thus these fragmentation pathways are blocked.

According to this explanation, the spectra of NMI trienes should also differ from the spectra of ordinary methylene-interrupted cis systems. This is indeed the case in hydrogenated fats where trienes with m/z 79 as base peak are rarely seen. This was also seen in three spectra from unknown trienes (one 18:3 and two 20:3) in an extract of needles of pine (Pinus sylvestre). ∆5 unsaturated NMI trienes are found in significant amounts in most conifers.

By separating the dienes into smaller groups and conducting PCA on each of the groups some more information about the nature of the mass spectra was achieved. In the case of 18:3n-6 m/z 95 was less important than in the n-3 fatty acids. Simultaneously the m/z 137 was seen in the loading plots where m/z 95 were seen in the plots of n-3 fatty acids. The ions m/z 95 and 137 seem to replace the m/z 108 and 150 ions when the central double bond is trans. The m/z

- 70 -

95 ion can be formed by direct cleavage between carbon number seven and eight, counted from the methyl end, while m/z 108 is formed by rearrangement and cleavage between carbon eight and nine. The same mechanisms probably give rise to m/z 137 and 150 in the n-6 isomers (150-13=137, 108-13=95). Thus m/z 137 and 95 may be used as indicators of double bond position when the masses 150 and 108 disappear because of trans geometry. It should be emphasised that m/z 95 is a major peak also in the spectra of n-6 trienes, but is significantly reduced compared to the n-3 trienes.

The influence of the geometry of the double bond closest to the methyl end was also investigated. The trends regarding the n-3 isomers were very clear. The masses 67, 55 and 81 were significantly higher in the isomers with cis geometry in this position while m/z 95 was a good indicator of trans geometry. If the central double bond (n-9) had cis geometry the m/z 108 ion was also significantly higher in the spectra with cis in the n-3 position. If the central double bond had trans geometry then m/z 79 also was an indicator of trans geometry in the n-3 position.

The results for 18:3n-6 were less clear. 137 replaced 95 as the marker for cis geometry in the double bond closest to the methyl end (n-6). The masses 67, 81 and 55 are still correlated with cis geometry in this double bond. This is also true for m/z 79 when the central double bond is cis. However, other ions, e.g. 120, 80, 91 87 and 107 are also of importance.

Figure 5.6-7a)

The mass spectrum of 9c,12c,15c-18:3 (all-cis 18:3n-3)

6 0 8 0 1 0 0 1 2 0 1 4 0 1 6 0 1 8 0 2 0 0 2 2 0 2 4 0 2 6 0 2 8 0 3 0 0

9 5

6 7

5 5 9 c , 1 2 c , 1 5 c - 1 8 : 3

Figure 5.6-7b)

The mass spectrum of 6c,9c,12c-18:3 (all-cis 18:3n-6)

5 5

1 0 7

6 7

9 1 / 9 3

6 c , 9 c , 1 2 c - 1 8 : 3

6 0 8 0 1 0 0 1 2 0 1 4 0 1 6 0 1 8 0 2 0 0 2 2 0 2 4 0 2 6 0 2 8 0 3 0 0

1 0 8

2 9 22 3 6

7 9

7 9

1 5 01 9 4 2 9 2

- 71 -

Figure 5.6-7c)

The mass spectrum of 9c,12t,15c-18:3

6 0 8 0 1 0 0 1 2 0 1 4 0 1 6 0 1 8 0 2 0 0 2 2 0 2 4 0 2 6 0 2 8 0 3 0 0

5 5

1 0 7

9 c , 1 2 t , 1 5 c - 1 8 : 3

6 7

7 9 9 5

Figure 5.6-7d)

The mass spectrum of 9c,12c,15t-18:3

6 0 8 0 1 0 0 1 2 0 1 4 0 1 6 0 1 8 0 2 0 0 2 2 0 2 4 0 2 6 0 2 8 0 3 0 0

9 c , 1 2 c , 1 5 t - 1 8 : 39 5

6 7

5 5

2 9 2

2 9 22 3 6

1 0 8

7 9

Tetraenes The decomposition rules found for trienes seem to be valid also for tetraenes (and possibly also for pentaenes and hexaenes). The decomposition of all-cis 20:4n-6 giving rise to the ions indicating the double bond positions is shown in Figure 5.6-8. In the case of tetraenes, both ions are formed by cleavage between double bond 2 and 3 in the figure. The ions formed will have masses of 150 indicating the n-6 position and m/z 180 indicating the ∆5 position. According to the rules established above m/z 150 should disappear if double bond ∆11 has trans geometry and m/z 180 should disappear if double bond ∆8 has trans geometry.

Figure 5.6-8)

The decomposition of 20:4n-6 leading to diagnostic ions indicating the double bond position

COOMe+*

∆11 ∆8 ∆5∆14

COOMe +*

∆8∆5

∆11

∆14

20:4n6

m/z 180Seen if dbb ∆8 is cis

m/z 150Seen if dbb ∆11 is cis

- 72 -

The application of the rules for identification of the geometrical isomers of 20:4n-6 will be illustrated below. There are 16 possible geometrical isomers of 20:4n-6. The GC/MS chromatograms of five HPLC fractions of isomerised 20:4n-6 are given in Figure 5.6-9 a-d. The score plot of the mass spectra of the peaks are given in Figure 5.6-10. The all-trans isomer (peak 1) is at the extreme left in this plot while the all-cis isomer (peak 11) is positioned to the right.

The identification of the peak in the first HPLC chromatogram is a trivial task. Since this is the first HPLC fraction, all double bonds are of trans geometry. Peak 1 is positioned at the extreme left in the score plot. The mass spectrum of the peak is given in Figure 5.6-11b. Since both central double bonds are trans, neither of the ions m/z 150 or 180 are present. M/z 67 is the base peak.

There are four possible isomers with 1 cis double bond present in HPLC fraction 2. The size of the peaks indicate that peak 2 probably consist of a single isomer while the other three isomers are in peak 3. Peak 2 is found to the left in Figure 5.6-10 and neither m/z 150 nor m/z 180 are present in the mass spectrum. Thus both central double bonds have trans geometry and the cis is in the ∆5 or ∆14 position. Since the geometry of ∆14 is more important for the retention time than the geometry of the other double bonds, cis in ∆14 should give higher retention time than cis in ∆5. Since peak 2 elutes before the other isomers it is identified as 5c,8t,11t,14t-20:4. The other three isomers with one cis double bond must be present in peak 3. Peak 3 is positioned to the left in the plot indicating the high trans to cis ratio.

In HPLC fraction three there is two cis double bonds in each isomer. There are six possible combinations and only four peaks are seen. Since the area of peaks 5 and 6 is approximately twice that of peaks 5 and 7 the two latter are the pure peaks while two isomers are found in each of the peaks 5 and 6. The spectrum of peak 4 is positioned to the left in Figure 5.6-10. M/z 150 is not visible in the mass spectrum, indicating that the ∆11 double bond has trans geometry. M/z 180 is present indicating that the ∆8 double bond has cis geometry. The last cis double bond must then be in either ∆16 or ∆5. The latter of the two possibilities should give the lowest retention time. Thus peak 4 is identified as 5c,8c,11t,16t-20:4.

Peak 7 is identified as 5c,8t,11c,16t-20:4. M/z 150 is not visible, thus ∆11 has trans geometry. M/z180 is present, thus ∆8 has cis geometry. Since peak 4 was identified as being the 5c,8c,11t,16t-20:4, peak 7 must be the 5t,8c,11t,16c isomer. A cis double bond in position ∆16 also corresponds with the large retention time.

Peaks 5 and 6 are confounded peaks, each consisting of two isomers. In Figure 5.6-10 peak 5 is grouped in the right part of the plot. Both m/z 150 and m/z 180 are relatively abundant in the spectrum. It is therefore reasonable to assume that the isomer with cis geometry in the two central double bonds (5t,8c,11c,16t-20:4) is present in this peak together with another isomer. Peak 6 is located far to the right in the score plot indicating that the isomer with both central double bonds in trans geometry (5c,8t,11t,16c-20:4) is present in this peak together with another isomer. The ion m/z 150 was low but visible in this spectrum while m/z 180 was not visible. This indicates that the other isomer present in this peak is the 5t,8c,11c,16t-isomer, thus the last isomer in peak 5 must be the 5c,8t,11c,16t isomer. This also corresponds with expected retention times as the isomers with trans in ∆16 elute before the isomers with cis in ∆16.

In HPLC fraction four, which contain the four isomers with one trans and three cis double bounds, one pure peak, peak 8, and two confounded peaks, peak 9 and 10, are present. Peak 8 is probably clean; peaks 9 and 10 therefore consist of three isomers. Peak 8 must be the 5c,8c,11c,16t isomer. Both m/z 150 and 180 are present in the mass spectrum and 79 is

- 73 -

significantly more abundant than any other peaks in the spectrum. In the score plot peak 8 is also positioned very close to peak 11, which is the all-cis isomer. The possible alternatives are 5c,8c,11c,16t-20:4 and 5t,8c,11c,16c-20:4. Trans geometry in position ∆16 leads to lower retention time, thus 5c,8c,11c,16t-20:4 is the isomer in peak 8.

Peaks 9 and 10 contain the three isomers with cis in ∆16 position. None of the spectra are pure. A relatively low m/z 150 in the spectrum of peak 9 indicates that the isomer with trans in ∆11 (5c,8c,11t,16c-20:4) is found in this peak. An abundant m/z 150 in peak 10 indicates that the two isomers with cis in ∆11 position are found in this peak.

Fraction five contains only the all-cis 20:4n-6, the mass spectrum of this isomer is shown in Figure 5.6-11c. Note that the all-trans isomer is much more similar to all-cis 20:2n-6 than to all-cis 20:4n-6. The risk of identifying trans polyenes as being dienes (cis or trans) is therefore present when abundant molecular ions are not present.

The above mentioned results show that by combining information from the mass spectra with information about retention times from GC and HPLC, identification of individual trans isomers may be achieved. Some problems exist with the confounded peaks, but the fact that the differences between the spectra are relatively large may lead to accurate quantification and identification if multivariate curve resolution techniques are applied.

These results may be of importance for identification of individual trans isomers in unhydrogenated fats and biological systems where methylene interrupted systems dominate. The results are of limited importance for the identification of unknown isomers in hydrogenated fats. They may explain why most PUFA isomers found in hydrogenated fats have mass spectra which differ significantly from the mass spectra of PUFA found in natural fats. Most PUFA found in hydrogenated fats is of the NMI type without the methylene-interrupted system necessary to achieve a base peak of m/z 79 and the diagnostic ions that indicate the double bond position.

1 2 3

6

4 9

9+10 11

8

5

10

7

Figure 5.6-10) Score plot of the mass spectra of the peaks in Figure 5.6-9. Masses 50-170, variable weights = 1. Peaks that are assumed to consist of only one isomer are encircled with green

- 74 -

Figure 5.6-9a)

GC-MS trace of HPLC fraction 1, all-trans 20:4n-6

21.5 22.0 min.21.0 Figure 5.6-9b)

GC-MS trace of HPLC fraction 1, 20:4n-6 with 3 trans 1 cis double bonds

21.5 22.0 min.21.0 Figure 5.6-9c)

GC-MS trace of HPLC fraction 1, 20:4n-6 with 2 trans 2 cis double bonds

21.5 22.0 min.21.0 Figure 5.6-9d)

GC-MS trace of HPLC fraction 1, 20:4n-6 with 1 trans 3 cis double bonds

21.5 22.0 min.21.0 Figure 5.6-9e)

GC-MS trace of HPLC fraction 1, all-cis 20:4n-6.

21.5 22.0 min.21.0

1

2 3

4 5 6 7

8 9 10

11

- 75 -

Figure 5.6-11a)

The mass spectrum of all-cis 20:4n-6

40 60 80 100 120 140 160 180 200 220 240 260 280 300 320

55

67

79

91

105

150

180 203

Figure 5.6-11b)

The mass spectrum of all-trans 20:4n-6

40 60 80 100 120 140 160 180 200 220 240 260 280 300 320

55

67 79

91

105

149177

Figure 5.6-11c) The mass spectrum of all-cis 20:2n-6

67

95

109

291150

8155

40 60 80 100 120 140 160 180 200 220 240 260 280 300 320

318

318

322

5.6.2) Double bond position in monoenes Contrary to picolinyl and DMOX derivatives, no specific ions indicate the double bond position in monoenes. However, Brakstad (1993) proved that minor differences in the abundance of the ions in the mass spectrum could be utilised to predict double bond position in fatty acid ethyl esters (FAEE). The spectra of FAME and FAEE are rather similar and spectra of monoenes with known double bond positions were subjected to PCA and PLS regression as a test for possibilities to predict the double bond positions from the FAME mass spectrum.

- 76 -

The fatty acids varied in chain length from C11 to C24 and double bond position varied from ∆5 to ∆15. The variables were weighted with 1 in the case of PCA and 1/std.dev. (standardisation) in the case of PLS regression.

The score plot based on the mass spectra of standards with chain length 14 to 22 and double bond position ∆5 to ∆11 is shown in Figure 5.6-12. Principal component 1 explains the double bond position relative to the carbonyl carbon, while PC2 explains the chain length. Thus it can be concluded that the major variance (59% explained) in the mass spectra of these monoenes are caused by the double bond position in the molecule, while the chain length is of minor importance (16% percent explained by PC2). The differences are probably caused by interactions between the double bond and the carbonyl group. When the distance between the carbonyl group and the double bond increase, the difference between the spectra decrease. There is partial overlap between the ∆11 and ∆9 groups. By inclusion of isomers with the double bond further apart from the carbonyl group, any systematic difference between the spectra was difficult to observe.

The loading plot (not shown) indicates that the ions 74 (most prominent), 67, 96, 81, 84 and 57 increase as the double bonds approach the carbonyl carbon, while the ions 55, 69 and 56 decrease. In the isomers with the double bond closest to the carbonyl group m/z 74 is the base peak in the spectra, while m/z 55 is usually the base peak in monoenes. The mass spectra of ∆5-20:1 is compared to ∆11-20:1 in Figure 5.6-13.

Figure 5.6-12)

Score plot of monoenes with double bonds in different distance from the carbonyl carbon, m/z 50-115, standardised variables.

-300

-200

-100

0

100

200

300

-400 -200 0 200 400 600 800

X-expl: 59%,16%

14c9

16c9

18c920c11

16c9

18c9

18c11

20c9

20c11

22c11

14c5

14c7

14c9

16c5

16c7

16c9

18c718c9

18c11

20c9

20c11

22c11

18c6

20c5

PC1

PC2

17c10

∆5

∆6

∆7∆11

∆9∆10

324

Figure 5.6-13a)

Mass spectrum of ∆9-20:1

40 60 80 100 120 140 160 180 200 220 240 260 280 300 320

55

6974

97

111123 138 208 250

292

- 77 -

Figure 5.6-13b)

Mass spectrum of ∆5-20:1

40 60 80 100 120 140 160 180 200 220 240 260 280 300 320

55

67

74

96

110123

141208 250 292

324

∆5∆6

∆7

∆9

∆10

∆11

∆12

∆13 ∆15

∆-position

Prediction by PLS The spectra of C11 to C24 fatty acid methyl esters with double bond positions ranging from ∆5 to ∆15 was subjected to a PLS regression with the double bond position as the dependent variable. The predicted vs. Measured of the regression is shown in Figure 5.6-14. The figure show that the prediction is not accurate enough to give the exact double bond position in the molecule when the double bonds are positioned further apart from the carbonyl carbon than the ∆7 position. However, a rough indication may be achieved. A rough indication of the double bond position, combined with the retention time from GC is often enough to tell if the double bond in an unknown isomer has trans or cis geometry.

By picking the ten variables with largest regression coefficients (absolute value), which were the ions 53, 54, 59, 60, 63, 67, 68, 69, 84, and 102, a regression model based on only one PLS component could be achieved without any significant loss in accuracy. The SEP of the one component model was 0.82 while the four component model showed SEP of 0.79.

Figure 5.6-14)

Predicted vs measured of double bond position in monoenes

Four component PLS regression. Grey line is regression line; red line is target line.

Standardised variables.

17

24

14

1416

1618

11 13

18

20

Measured Y

Predicted Y

10 12 14864 16

10

12

14

8

6

4

16

Elements:Slope:Offset:Correlation:RMSEP:SEP:Bias:

330.8545371.3232880.9499600.7781710.785744-0.082856

20142018141616181618

2020222218201218

22201822

Monoenes:

Chain length

- 78 -

5.6.3) Double bond positions in dienes The influence on the mass spectra of double bond position in dienes was not thoroughly investigated. Most natural dienoic isomers are of the n-6 methylene interrupted type. Except the CLA isomers, these are the only pure reference standards commercially available. Small amounts of methylene interrupted dienes other than n-6 are usually present in fish oil (Young 1986). Non-methylene interrupted dienes has also been reported.

A few samples of marine origin were investigated for the presence of other dienes than the common n-6 isomers. Several methylene-interrupted dienes was found in extracts of blue mussels, barnacles and a sea snail. All isomers assumed to be of the MI type had m/z 67 as a base peak and spectra that were very similar to n-6 isomers. In most of these isomers a prominent peak for m/z 150 was seen. This proves that m/z 150 peak is not an indication of n-6 unsaturation in MI dienes. There can only be one n-6 MI diene of each chain length with cis geometry. The chromatographic elution times did not correspond with trans isomers.

A few dienes was found with m/z 55 and spectra similar to monoenes. These are supposed to be NMI dienes. The spectra of dienes found in hydrogenated fats, where most dienes are of NMI type showed similar patterns. Thus it is possible to discriminate between MI and NMI dienes by mass spectrometry. Figure 5.6-2 shows that conjugated isomers (usually not found in hydrogenated fats) can be separated from other isomers by mass spectrometry of FAME.

It is possible that the distance between the double bonds in NMI fatty acids can be told from the mass spectra, several categories of spectra were seen in the diene spectra from hydrogenated fats. This has not been investigated further. Pure spectra of NMI dienes are difficult to achieve from hydrogenated fats because of the complexity of the products. A possible solution is to synthesise NMI dienes from PUFA by hydrazine reduction.

Trans isomerism will probably have little or no influence on the spectra of NMI dienes since it proved to be without significance both in the spectra of monoenes and methylene interrupted dienes.

5.6.4) Summary The mass spectra of FAME proved to give more information than initially expected. Detailed information about the cis-trans geometry of methylene interrupted systems could be achieved. This can be important information for the identification of unknown isomers in unhydrogenated fats.

The application of PCA on the mass spectra proved to be a valuable tool in identification of unknown isomers. Very small differences, which are difficult to observe by traditional inspection of the spectra, could be discovered. PCA also proved to be a superior classification tool compared to traditional methods. This is illustrated by the case of all-trans 20:4n-6, which by visual inspection look very similar to all-cis 20:2n-6 (Figure 5.6-11), but in the score plot (Figure 5.6-1) this is classified as being a polyene.

Limited amount of information about NMI isomers was achieved. The spectra generally differ from the spectra of methylene interrupted isomers. The number of double bonds in a methylene-interrupted series seems to be more important than the total number of double bonds in the molecule.

- 79 -

5.7) Identification by changes in ECL values.

5.7.1) Introduction In addition to the mass spectra and the infrared spectra, the chromatographic retention time may provide important information about the identity of unknown isomers. The behaviour of different fatty acid isomers on various stationary phases was described in section 5.2.

It is important to note that on the most polar stationary phases, there is a large degree of overlap in the retention time intervals where fatty acids with different chain lengths elute. This is especially a problem with the most unsaturated C20 fatty acids, which elute in the regions where C22 monoenes and dienes usually elute. Thus, a large number of alternatives exist for peaks that elute in this area, and identification will certainly be a problem if high quality mass spectra do not exist. If trans fatty acids or NMI fatty acids are possible options, reliable identification is difficult even with mass spectrometric detection because the number of possible isomers will be very large, and because the rules of mass spectral interpretation applied on methylene interrupted cis isomers are not valid on trans isomers.

The identification of unknown isomers based on retention times has traditionally been solved in various ways. One option is to use several columns of different polarity. The number of double bonds in an unknown isomer can usually be told by comparing the retention time on e.g. a cyanopropyl and on a PEG column. The tentative identification may be confirmed if reference compounds are available and are run on the same columns. However, changing the columns is a cumbersome and time-consuming procedure, especially when a mass spectrometric detector is applied.

Another solution has been the application of ECL and FCL values (James 1959, Miwa et al. 1960, Woodford and van Gent 1960). In isothermal gas chromatography there exist a linear relationship between log(Rt) and the chain length of the unbranched saturated fatty acid methyl esters. By definition the ECL of the saturated FAMEs are set equal to their chain length, ECLs of unsaturated compounds may then be found in plots of log(Rt) versus ECL. FCL is the difference between ECL for an unsaturated fatty acid and the saturated fatty acids with the same number of carbons.

Fatty acids with similar functional groups will have similar FCL values. Thus 18:3n-3, 20:3n-3 and 22:3n-3 will have very similar FCL values, so will 18:1n-9, 20:1n-9 and 22:1n-9. Tentative identification of unknown isomers can be achieved by comparing their calculated FCL values with FCL values of known isomers of different chain lengths

Both the above mentioned methods has certain limitations. The ECL method will fail if the number of carbon atoms is not known, and changing columns is too cumbersome. However, the stationary phase is only one of several parameters that determines the elution pattern of fatty acid methyl esters. It has been shown (Thompson 1997) that there are small changes in ECL values of unsaturated fatty acids with different temperatures in isothermal runs. Another parameter that may be of importance is the carrier gas flow. The idea is to induce changes in ECL values of the fatty acids by running the samples at the same column, but with different temperature and pressure programs. The shifts in ECL values with different programs may provide information about the structure of the fatty acid.

The pressure and temperature parameters that can be applied are limited by the dimensions and temperature limits of the column, upper and lower pressure limits of the injector, and the demand for the necessary resolution of the peaks in a reasonable time. In the simplest linear program three parameters must be set; the starting temperature, the temperature gradient and

- 80 -

the column head pressure (gas flow). The parameters were varied within the following limits: Starting temperature; 160-190°C, temperature gradient; 2-4°C, gas flow 18-26 cm/sec. A full fractional design including upper and lower levels of the three parameters gives 23 = 8 programs. By including center values of 175°C, 3°C/min and 22cm/sec, a full design consists of 33 = 27 programs.

All programs started with injection at 60°C where the temperature was held for four minutes. The temperature was then rapidly increased (30°C/min) to the starting temperature (A). Thereafter the temperature was increased by the temperature gradient (B) until the last isomer had eluted. The column head pressure was set to the pressure necessary to give the wanted average column flow (C). The various GC programs will be referred to in the form A-B-C. The GC programs are described in appendix 8.9.

The retention times were converted to ECL values. When temperature programs are applied there is no longer a linear relationship between log(Rt) and the ECL value. However, a direct relationship between the retention time and ECL can be established by using non-linear regression (Nichols et al. 1982, Gillan 1983, Krupcík & Bohov 1985). In this case third order polynomial regressions were applied, which gave R2 better than 0.9995.

The ECL values for each of the different fatty acid isomers analysed by each of the 27 programs where calculated. By selecting the GC programs as variables and the fatty acids as objects and subjecting the data matrix to a PCA the score plot in Figure 5.7-1 was achieved. The first principal component describes the “size” of the object, or the average ECL values. Thus PC1 gives no more information than what can be directly read from the chromatograms or traditional ECL plots. However, PC2 adds valuable information to the plot. PC2 contains information about the small changes in ECL values that are induced by the different chromatographic conditions. Together the two principal components form a pattern of fatty acids where gradients corresponding to the number of carbons and number of double bonds are clearly seen. The highly unsaturated C20 isomers, which usually coelute with 22:1 and 22:2 isomers, are very well separated in the plot. Even 20:3n-3 and 20:4n-6, which are critical pairs of the same chain length are well separated and identified by PC2. The second principal component, which contains the important information about the number of double bonds, describes only 0.01 percent of the variation in the data set.

Figure 5.7-1)

PCA of ECL values of the fatty acid in reference mixture GLC 461

Full 33 design

-0.4

-0.2

0

0.2

0.4

-40 -30 -20 -10 0 10 20 30

X-expl: 99.99%,0.01%

14:0

14:1

15:016:0

16:1

17:0

17:1

18:0

18:1

18:2

18:3n6 18:3n3

20:0

20:1

20:2

20:3n620:4n6

20:3n3

22:0

22:1

20:5n3

22:2

22:4n6

24:0

24:1

22:5n3

22:6n3

PC1

PC2

Even with autosamplers and automated settings of the GC, 27 chromatograms to identify the isomers in one sample may cause too much work. Based on the loading plot (not shown) corresponding to the score plot in Figure 5.7-1 five programs were selected. These were the

- 81 -

programs in the four “corners” of the loading distributions plus the center point. The selected programs were: 160-2-26, 160-4-18, 190-2-26, 190-4-18 and 175-3-22.

The score plot based on these five programs is shown in Figure 5.7-2. The pattern is similar to that seen when all programs were used in the PCA. To test the stability of the method with column ageing, etc, the data for the score plot in Figure 5.7-2 were achieved several months and several hundred analyses after the first experiment summarised in Figure 5.7-1. Two additional FAME standards, 19:2n-6 and 22:3n-3, which is not present in the GLC-461 reference mixture were also analysed and treated as “unknowns”. Both these samples were identified correctly by the method. 19:2n-6 was placed in the middle between 18:2 and 20:2. 20:3n-3 were placed between 22:2 and 22:4 and in the extension of the line through 20:5n-3 and 22:6n-3.

Double bond positions have some influence in the plot. For the C20 and C22 PUFA there is one line going through the n-6 series and one line going through the n-3 series. The extreme values in the plot deviate slightly from the general pattern. There is a sharp bend in the saturated and monoenoic trends for C14 acids. A slight deviation may be seen for the C24 acids. Similar deviations for 22:5n-3 and 22:6n-3 were also seen in some plots. The deviations for C14 acids, which are always present, may be explained by too high lower limit for the starting temperature. The deviations for the last eluting isomers, which are sometimes present, can be probably connected with leaks or column ageing. Figure 5.7-2)

PCA of ECL values of the fatty acid in reference mixture GLC 461

Five selected programs:

160-2-26 160-4-18 175-3-22 190-2-26 190-4-18

-0.2

-0.1

0

0.1

0.2

-15 -10 -5 0 5 10 15

X-expl: 99.983%, 0.010%

14:0

14:1

15:0 16:0

16:1

17:0

17:1

18:0

18:1

18:2

18:3n618:3n3

20:0

20:1

20:2

20:3n6

20:4

20:3n3

22:0

22:1

20:5

22:2

22:4

24:024:1

22:522:6

19:2 22:3n3

PC1

PC2

5.7.2) Application of the method The method was tested on two natural samples containing unknown isomers, one unhydrogenated fish oil, and one human blood sample. In the cases where the peaks were large enough, the identity of the peaks could be confirmed by mass spectrometry.

The plot for the fish oil sample is shown in Figure 5.7-3. The blue dots are the fatty acids in the reference mixture, while the red dots are the fatty acids in the fish oil. The PUFA isomers were identified as follows:

C16: A saturated isomer is eluting before n-16:0. Several 16:1 isomers can bee seen. One of them is appears between the group of cis 16:1 and 16:0, this is a possible trans-16:1 (see section 7.7.3). One 16:2 isomer was found and confirmed by mass spectrometry. One possible 16:3 isomer and two 16:4 isomers, all identified as PUFA by the MSD were observed. One of these is probably 16:4n-1, which is frequently reported in fish oils. This isomer deviated from the normal n-3 and n-6 lines. Because of overlap with 18:1 isomers in some programs, the ECL values achieved for these isomers is uncertain.

- 82 -

C18: Two 18:2 isomers were observed. One is 18:2n-6, the other elutes after the n-6 isomer and may be the n-4 isomer (Young 1996). Three 18:3 isomers were identified, two of them is 18:3n-6 and 18:3n-3, the third isomer elutes between these two. A tetraene was also observed, the retention time indicates n-3 structure.

C20: Two 20:2 isomers were observed, one of them is the n-6 isomer, the other, which elutes before n-6 is most likely the n-9 isomer (Young 1996). 20:3n-3, 20:3n-6, 20:4n-6, and 20:5n-3, which are all present in the reference mixture were found. An unknown isomer, which is probably 20:4n-3, was also identified.

C22: In addition to the unsaturated isomers present in the reference mixture, an isomer that is probably 22:5n-6 was found.

Other: 21:5n-3, which is common in fish oils was identified by its position between 20:5n-3 and 22:5n-3. The compound between 16:1 and 16:2 marked x2 had a mass spectrum that indicated 17:0. This is not n-17:0, the position of x2 in the plot also indicates that the branch is not in the same position as the branch in the two 15:0 and 16:0 isomers. The compound x1, eluting between 18:2 and 20:2 has not been identified. All-cis 19:2 or CLA (see section 5.7.5) are among the possible explanations.

Figure 5.7-3)

Score plot of the fish oil sample.

20:3n6

-0.2

-0.1

0

0.1

0.2

-15 -10 -5 0 5 10 15

X-expl: 100%,0%

14:1

17:0

17:1

18:3n6 18:3n3

20:1

20:2

22:1n9

22:2n6

22:4n6

24:0

22:5n3

14:015:0 15:0

16:016:0

16:1

x2 16:216:3

16:4

18:0

16:4n1

18:118:2

20:0

18:4n3

x1

20:4n6

20:3n3

22:0

20:4n3

20:5n321:5n3

24:1n9

22:6n3

PC1

PC2

22:5n6

The score plot of the blood sample is given in Figure 5.7-4. The isomers in the GLC-461 reference mixture are not included. A few isomers that were not present in the reference mixture were identified. Among these was a trans-18:1 and a trans-18:2 isomer (see section 5.7.3). The branched 15:0 and 16:0 were seen also in this sample. Two 20:2 isomers were observed. The sample x2 is probably the same as seen in the fish oil. The sample x3 probably has a similar structure, both mass spectra are similar to n-17:0. X4, which appeared between 20:1 and 20:0 has not been identified. It is probably not a trans 20:1. The isomers x5 and x6 are probably CLA isomers (see section 5.7.5).

5.7.3) Trans fatty acids Trans fatty acid standards prepared by isomerisation with p-toluenesulfinic acid were tested for identification by the method explained above. The behaviour of C16-C20 monoenes and dienes is shown in Figure 5.7-5. The trans monoenes have values at PC2 that are intermediate between cis monoenes and saturated fatty acids. Thus the trans monoene gradient are found between the saturated fatty acids and the cis monoenes. In these areas there is little or no interference from other fatty acids. Thus, a reliable identification of trans monoenes can be made by this method. This can also be seen in the plots in section 5.7.4 where a large number of different trans and cis monoenes are analysed. There is no overlap between the cis and

- 83 -

trans monoenoic groups in the plot. This result may be of importance because trans geometry of monoenes can not be told from the mass spectra, and the retention time in a single chromatogram is usually not enough to give reliable identification.

Figure 5.7-4)

Score plot of the blood sample.

-0.2

-0.1

0

0.1

0.2

-15 -10 -5 0 5 10 15

X-expl : 99.97%, 0.02%

14:0

14:115:0

16:0

16:1

x2

x3

17:0

17:1

18:0

18:1t

18:1 18:2ct

18:2n6

18:3

20:0

x4

20:1

20:220:2

x5x620:3n6

20:4n6

22:0

20:5n3

23:0

22:4n6

24:0

24:1

22:5n322:6n3

PC1

PC2

The trans-trans dienes are grouped between the cis monoenes and trans monoenes, and the dienes with one double bond are grouped between the cis monoenes and cis-cis dienes.

The plot of isomerised C18 fatty acids with zero to three double bonds is given in Figure 5.7-6. In this plot the fatty acids are grouped according to the number of double bonds. The n-6 trienes are also separated from the n-3 trienes. Gradients corresponding to the number of cis double bonds are also seen, and marked with grey lines. The spacing between these gradients may be large enough to tell the number of trans double bonds in an isomer, especially if the total number of double bonds and positional isomerism is known from mass spectra. The position of the cis and trans double bonds seems to be of little influence on the plots.

The trends of separation according to the number of cis double bonds are also clear in the plots of 20:4n-6 and 22:4n-6 (Figure 5.7-7a and b). In the case of n-6 tetraenes, there is very large chromatographic overlap between groups with different numbers of trans double bonds. This can bee seen by the poor separation between the groups based on PC1 alone. In the case of n-6 tetraenes the all-cis isomer does not have the highest value along PC1. This means that several trans isomers elute after the all-cis isomer. This can also be seen in Figure 5.6.9. Because of the limited chromatographic resolution, all isomers could not be assigned an ECL value. The number of samples in each of the groups is therefore lower than the number of possible isomers.

Figure 5.7-5)

Score plot of common trans and cis monoenoic and dienoic fatty acids

-0.05

0

0.05

0.10

-10 -5 0 5 10

X-expl: 99.993%,0.005%

14:0

14:1c

15:0

16:0

17:0

17:1c

18:0

18:1c

18:2

20:0

20:1c

20:2

14:1t16:1t

18:1t18:2tt

19:1t

19:2tt 20:2tt

18:2ct18:2tc 19:2ct

19:2tc20:2ct

20:2tc

16:1c

17:0

19:2PC1

PC2

- 84 -

Figure 5.7-6.

C18 fatty acids with zero to three double bonds.

-0.04

-0.02

0

0.02

0.04

0.06

-3 -2 -1 0 1 2

X-expl: 99.963%,0.034%

18:0

18:1

n3ccc18:1t

18:2tt

n6ttt

18:2ct18:2tc

n6cttn6tct

n6ttcn3ctt

n3tctn3ttc

n3ttt

n6cctn6ctc

n6tcc

n3cctn3ctc

n3tcc

18:2

n6cccPC1

PC2All trans

1 cis

2 cis

3 cis

Figure 5.7-7)

Score plot of 20:4n-6 cis and trans isomers

-0.04

-0.02

0

0.02

0.04

0.06

-1.0 -0.5 0 0.5

X-expl: 99.470%, 0.518% PC1

PC2

2 cis

3 cis

4 cis

1 cis

0 cis

Figure 5.7-7b)

Score plot of 22:4n-6 cis and trans isomers

-0.04

-0.02

0

0.02

0.04

-1.5 -1.0 -0.5 0 0.5 1.0

X-expl: 99.823%,0.121% PC1

PC2

1 cis 2 cis

3 cis

4 cis

0 cis

The separation between the groups is more complicated when both trienes and tetraenes are present in the same chromatogram, which will usually be the case with real samples. The score plot when 20:3n-6, 20:3n-3 and 20:4n-6 trans isomers are included in the same PCA is given in Figure 5.7-8. There is overlap between the 2-cis groups of 18:3n-3 and 20:4n-6 and the separation between other groups are not very clear. However, in unhydrogenated fats, isomers with more than one trans double bond are rare. A score plot with C18 and C20 fatty acids with zero or one double bond are given in Figure 5.7-9. In no cases is there overlap between the trans isomers and other fatty acids. However, the trienoic and tetraenoic isomers with one trans double bond are grouped close to the line of all-cis dienoic and trienoic fatty acids respectively.

- 85 -

Because of limitations in the HPLC procedure used for isolation of the isomerised fatty acids, pentaenes and hexaenes was not included in this study of trans isomer behaviour. However, one can expect isomers of 20:5n-3 with one trans double bond to be grouped close to the all-cis 20:4 isomers. Correspondingly, similar isomers of 22:5n-3 and 22:6n-3 will group close to the 22:4 and 22:5 isomers respectively.

Figure 5.7-8)

Score plot of 20:3n-6, 20:3n-3 and 20:4n-6 geometric isomers in the same PCA.

-0.06

-0.04

-0.02

0

0.02

0.04

-1.5 -1.0 -0.5 0 0.5 1.0

X-expl: 99.697%,0.294% PC1

PC2

2 cis

3 cis

1 cis

4cis 20:4n-6

All trans20:4n-6

Figure 5.7-9)

C18 and C20 isomers with zero or one trans double bond.

-0.10

-0.05

0

0.05

0.10

0.15

-6 -4 -2 0 2 4

X-expl: 99.948%, 0.044%

18:0

20:0

20:4n6

22:0

1tr-20:4

PC1

PC2

2 cis3 cis

20:3n-3

1 cis

All trans20:3n-3

1 cis

2 cis

3 cis20:3n-6

All trans20:3n-6

18:3n618:3n3

20:3n6

1tr-20:3

20:3n3

1tr-18:3n61tr-18:3n3

18:1

20:1

18:1t

19:1t20:1c

20:2cc18:2ct19:2ct 20:2ct

18:2cc19:2cc

5.7.4) Application on hydrogenated fats The method was applied on fractions isolated from hydrogenated fats by silver ion HPLC. The score plots for the C16, C18, C20 and C22 fatty acids are given in Figure 5.7-10a to d. The saturated, trans monoenes, cis monoenes and dienes elute in different HPLC fractions. The identification of these is therefore quite certain. The separation of dienes and trienes into groups of different number of trans and cis double bonds by Ag-HPLC is poor. The separation of these isomers into different groups is therefore highly tentative. The inclusion of the all-cis dienes and trienes reference standards in the models gave some additional information about the identity of the groups.

For all chain lengths the cis and trans monoenes are separated in the plots. These can not be separated by any single gas chromatographic run. However, the C16 and C18 cis monoenes overlap with the group that is supposed to be mainly trans-trans dienes. In the case of C20 and C22 isomers, there is no overlap, but the groups are very close in the score plots. With respect to the dienes, no certain assignments of the geometry in the isomers can be given. However, tendencies to grouping are seen in the plots. The group with the lowest retention times are all-trans isomers. All-cis reference standards that were included in the PCA also

- 86 -

indicate all-cis groups. The borders between the groups are diffuse, and the diene groups probably contain some trienoic isomers.

One sample that could be an all-cis n-3 isomer was seen in the C18 plot. In the other plots, all unknown isomers appeared below the all-cis standards, indicating that methylene interrupted all-cis trienes (or less saturated) PUFA is not present in the product after hydrogenation.

Figure 5.7-10a)

Score plot of C16 fatty acids isolated from a sample of PHFO with melting point around 31°C

The identification of dienes is highly speculative.

-0.06

-0.04

-0.02

0

0.02

0.04

-2.0 -1.5 -1.0 -0.5 0 0.5 1.0 1.5 2.0

X-expl: 99.937%, 0.052%

n-16:0

?

PC1

PC2

Figure 5.7-10b)

Score plot of C18 fatty acids isolated from a sample of PHFO with melting point around 31°C

The identification of dienes and trienes is highly speculative.

-0.06

-0.04

-0.02

0

0.02

0.04

0.06

-2 -1 0 1 2 3

X-expl: 99.929%,0.057%

18:0

PC1

PC2

Figure 5.7-10c)

Score plot of C20 fatty acids isolated from a sample of PHFO with melting point around 31°C

The identification of dienes and trienes is highly speculative.

-0.06

-0.04

-0.02

0

0.02

0.04

0.06

-2 -1 0 1 2 3

X-expl: 99.890%,0.091%

n-20:0

PC1

PC2

trans 16:1

cis 16:1

trans 18:1

cis 18:1

cis-20:1

trans-20:1

trans-trans 16:2

cis-trans 16:2

cis-cis 16:2

trans-trans18:2

cis-trans18:2

cis-cis18:2

20:2n-6

cis-cis20:2

trans-cis20:2

trans-trans20:2

18:3n3

18:3n6

All cis18:3

20:3n-3

20:3n-6

All cis20:3

cct-20:3?

- 87 -

Figure 5.7-10d)

Score plot of C22 fatty acids isolated from a sample of PHFO with melting point around 31°C

The identification of dienes and trienes is highly speculative.

-0.10

-0.05

0

0.05

0.10

-2.0 -1.5 -1.0 -0.5 0 0.5 1.0 1.5 2.0

X-expl: 99.887%,0.98%

n-22:0

PC1

PC2

22:2n-6

cis-cis22:2

cis-trans22:2trans-trans

22:2trans-22:1

cis-22:1

5.7.5) Application of conjugated isomers, CLA Conjugated isomers are another analytical challenge. Conjugated isomers are usually not found in hydrogenated fats (see section 3) but are formed by heating of natural fats and oils. Conjugated linoleic acid is also known from several natural sources.

The behaviour of CLA was investigated by analysing a reference mixture (O5632, Sigma-Aldrich) containing different amounts of the 9,11- and 10,12-18:2 isomers, which are formed by heating of 18:2n-6. The mixture was separated by Ag-HPLC before GC analysis. Complete separation could not be achieved by HPLC; five HPLC fractions were collected, which in sum contained ten peaks. The total possible geometric isomers are eight. Some of the peaks are therefore probably of the same isomer. Because of the incomplete separation by the Ag-HPLC method no attempt to achieve complete identification of the isomers was made.

The score plot of the CLA isomers, calculated together with C18 and C20 fatty acids, is shown in Figure 5.7-11. Overlap with C18 isomers does not seem to cause any problems. CLA may overlap chromatographically with 18:4 and 18:5 isomers if these are present. CLA also elute in the same retention time region as the 20:1 isomers. However the separation from 20:1 by the second principal component is good. Possible problems might be confusion with all-cis 20:2 isomers with the double bonds further from the methyl end than in the n-6 isomers, e.g. the n-9 isomer. Trans isomers of 20:3n-6 may also cause problems.

Because of the higher polarity in the conjugated system, than in ordinary methylene-interrupted double bond systems, CLA elute far from other 18:2 isomers. However, all CLA isomers grouped close to the line between all-cis 18:2 and all-cis 20:2, indicating that CLA, irrespective of the double bond geometries, will react similar to the all-cis dienes on changes in the chromatographic conditions.

5.7.6) Application on SP-2560 The method of identification based on changes in ECL values was also applied with a 100m SP-2560 (Supelco) column, which is more polar than the BPX-70 column. The program parameters chosen were similar to those used with the BPX-70 column. However, some modifications had to be done because of differences in column length and upper temperature limit. The higher temperature gradient was set to 2.0°C/min and the higher column flow was set to 24 cm/sec. It was assumed that the five programs chosen for the BPX 70 would be the best programs also on SP-2560. However, this was never tested by application of a full 33

- 88 -

fractional design. The parameters used were 160-1.0-24, 160-2.0-18, 175-1.5-21, 190-1.0-18 and 190-2.0-24. Details about the programs are given in appendix 8.9.

Figure 5.7-11)

C18 and C20 fatty acids and 9,11- and 10,12-CLA isomers

-0.05

0

0.05

0.10

0.15

-6 -4 -2 0 2 4 6

X-expl: 100%,0%

17:0

17:1

18:0

18:1

18:3n6 all cis 18:3n3

20:0

20:1

20:2

20:3n620:3n3

all tr. 18:2

1 tr.18:2

All cis 18:2

all tr.18:3n3

2 tr.18:3n3

1 tr. 18:3n3

PC1

PC2 Scores

CLA

The score plot of all fatty acids in the GLC-461 reference mixture is given in Figure 5.7-12. The plot deviated significantly from the corresponding plot for BPX-70, which is shown in Figure 5.7-2. PC1 is still correlated with the average ECL value. PC2 gives no information about the number of double bonds. This is caused by the large deviations found for the late eluting compounds. When these were left out of the PCA model, the plot shown in Figure 5.7-13 was achieved.

Figure 5.7-13 shows the same trends as seen in Figure 5.7-2. Gradients corresponding to the number of double bonds are found. Because of the larger polarity of the SP-2560 C18 trienes and C20 monoenes elute in the same chromatographic regions. These peaks have similar values along PC1, but are clearly separated by PC2. Similarly, C20 PUFA are resolved from C22 monoenes and saturated compounds.

The deviations seen for C14 fatty acids when BPX-70 was applied is not seen with SP-2560. The reason for the large deviations for late eluting compounds with SP-2560 is not known. A possible explanation may be technical problems with the large pressures applied with the 100 m column. The split/splitless injector used on the HP-5859 gas chromatograph is programmed to give constant flow in the column when the temperature increase. To achieve a column flow of 24 cm/sec at this column, the column head pressure is close to the injector limit of 60 psi at the end of the temperature program. When the equipment is used at the limits of its performance unlinearities may arise. Another explanation may be that the SP-2560 may change its phase characteristics as the column approaches the temperature limit.

With respect to trans fatty acids, the trends were similar to that seen on BPX-70. This is shown in Figure 5.7-14, which corresponds to Figure 5.7-5 for BPX-70. When the C20 and C22 n-6 tetraenes were applied (plots not shown) there was overlap between the groups with one trans and two trans double bonds. All-cis 18:3n-3 also appeared in the same areas as these two groups when trienes were included in the models.

- 89 -

Figure 5.7-12)

All fatty acids in GLC-461 reference mixture on the 100 m SP-2560 column

-0.5

0

0.5

1.0

1.5

2.0

-15 -10 -5 0 5 10 15 20

X-expl: 99.636%,99.384%

14:014:1

15:016:0 16:1

17:017:1

18:018:1

18:220:018:3n6

20:1 18:3n320:2 22:0

20:3n622:1

20:3n3

20:4

22:220:524:0

24:122:4

22:5

22:6

PC1

PC2

Figure 5.7-13)

Selected fatty acids in GLC-461 reference mixture on the 100 m SP-2560 column

-0.2

-0.1

0

0.1

0.2

-15 -10 -5 0 5 10

X-expl: 99.979%,0.016%

14:015:0

16:0

17:018:0 20:0

22:0

20:4

PC1

PC2

Figure 5.7-14)

Cis and trans isomers, monoenes and dienes, SP-2560

-0.10

-0.05

0

0.05

0.10

0.15

-10 -5 0 5 10

X-expl: 99.988%,0.010%

14:0

15:0 16:0

17:0

18:0 20:0

PC1

PC2

14:1

16:117:1 18:1

20:1

22:1

14:1

16:117:1

18:120:1

14:1t

16:1t

18:1t 19:1t

14:1

18:2

20:2

18:2

18:2tt 19:2tt 20:2tt

18:2ct18:2tc

19:2ct19:2tc

20:2ct20:2tc

20:2

19:2

18:3n618:3n3

20:3n620:3n3

- 90 -

5.7.7) Summary and possible improvements The analysis of changes in ECL values proved to be a valuable tool for fatty acid identification, including the identification of trans isomers. On hydrogenated fats the method is of limited value. It has not been tested with NMI fatty acids with known structure. The method may be useful for identification of the trans isomers that arise with thermal stress, where only geometrical and not positional isomerisation occurs.

Some unlinearities and deviations were seen in the plots, especially at extreme values, e.g. C14 and C22 fatty acids. These problems can probably be solved by application of supervised LV methods, where the information about the structure of the reference standards is included in the models. Such supervised models may for instance be PLS-2, where the number of double bonds and chain length are included as y-variables for compounds of known structure. The matrix decomposition method will then extract latent variables that specifically explain chain length and number of double bonds; thus giving plots that may be easier to interpret than plots of principal components.

Both PLS-1 regression and MLR was tested on a dataset of the GLC-461 reference mixture applied on the BPX-70 column. The number of double bonds was used as the dependent variables. The predicted results were rounded to the nearest positive integer. PLS failed in the case of 20:5n-3, which was predicted to have four double bonds. Although the variables were highly collinear, MLR gave correct predictions of the 27 fatty acids included in the model. The X-variables were standardised in the case of PLS prediction.

In the case of CLA identification, SIMCA classification (Wold 1976, 1978) was tested as an alternative to PCA. With 95% confidence level, no other fatty acids was classified as belonging to the group of CLA. However one CLA isomer was outside the limit of sample to model distance, and one isomer was outside the leverage limit. Still no other isomers were closer to the CLA group than these two.

- 91 -

6) Results section

6.1) Quantitative results achieved by rapid GC/IR

6.1.1) Analytical conditions and quality of the results The samples are analysed by the rapid GC-IR method described in 5.5 and 8.7. Calibration is done on first derivative spectra on saturated, mono-, di-, and triunsaturated cis-trans isomers with methylene-interrupted double bonds.

Because of some chromatographic overlap, the peaks of SFA and MUFA was analysed as one peak and the amount of saturated, cis monoenes and trans monoenes was calculated from the cis-trans values in the fused peak. Some influence of early eluting dienes in these peaks may give an overestimation of monoenes, in particular trans monoenes, and thus a corresponding underestimation of the saturates. In some of the least saturated fats where the amounts of 20:0 and 22:0 are very low, this method gave negative values for these fatty acids. The negative values were corrected to zero before further computations were done.

The area percent of total PUFA may be slightly overestimated because of late eluting monoenes and saturates with odd numbered chain length (17:0, 19:0, 21:0), which will contribute to the peak area of PUFA. These isomers contribute to the PUFA peak area, but not with double bonds, which will lead to a slight discrepancy between the PUFA area and the amount of double bonds in the peak The average number of double bonds in the PUFA peak may therefore be less than two.

More accurate results may be achieved by increased segmentation of the chromatograms and optimisation of the injected amount of each sample, but on the cost of increased analysis time.

Parameters

The parameters described in Table 6.1-1 has been calculated from the chromatograms and spectra of each sample. Results with the 27 parameters are rather complex to interpret even if multivariate techniques are used. To simplify the picture, sum-parameters were calculated from the original variables. The sum-parameters are described in Table 6.1-2.

6.1.2) Description of samples, product overview Samples of hydrogenated fish oils from three factories, two European (A and B) and one South American (C), have been analysed. An overview of the samples and their properties is given in Table 6.1-3.

- 92 -

Table 6.1-1) Overview over original measured variables for each chain length.

< C16 1 Area percent of 12:0 2 14:0 Area percent of 14:0

15:0 Area percent of 15:0 C16 4

12:0

3

16:0 Calculated area percent of 16:0 5 16:1t Calculated area percent of 16:1t Calculated from the same peak 6 16:1c Calculated area percent of 16:1c 7 16:PU Area percent of C16 PUFA 8 16:PU-t Average trans in C16 PUFA peak 9 16:PU-c Average cis in C16 PUFA peak C18 10 18:0 Calculated area percent of 18:0 11 18:1t Calculated area percent of 18:1t Calculated from the same peak 12 18:1c Calculated area percent of 18:1c 13 18:PU Area percent of C18 PUFA 14 18:PU-t Average trans in C18 PUFA peak 15 18:PU-c Average cis in C18 PUFA peak C20 16 20:0 Calculated area percent of 20:0 17 20:1t Calculated area percent of 20:1t Calculated from the same peak 18 20:1c Calculated area percent of 20:1c 19 20:PU Area percent of C20 PUFA 20 20:PU-t Average trans in C20 PUFA peak 21 20:PU-c Average cis in C20 PUFA peak C21 22 21:0 Measured area of 21:0 C22 23 22:0 Calculated area percent of 22:0 24 22:1t Calculated area percent of 22:1t Calculated from the same peak 25 22:1c Calculated area percent of 22:1c 26 22:PU Area percent of C20 PUFA 27 22:PU-t Average trans in C20 PUFA peak 28 22:PU-c Average cis in C20 PUFA peak C24 29 24:0 Calculated area percent of 24:0 30 24:1t Calculated area percent of 24:1t Calculated from the same peak 31 24:1c Calculated area percent of 24:1c

- 93 -

Table 6.1-2) Overview over “sum-parameters”

Var. Name Description 28 Sat Sum of the area percents of the saturated fatty acids (1, 4, 10, 16, 22,

23, 29) 29 Tot MUFA Sum of the area percent of monoenes (5, 6, 11, 12, 17, 18, 24, 25, 30,

31) 30 Tot PUFA Sum of the area percent of PUFA peaks 31 Total dbb Total amounts of double bonds, calculated iodine value. This value is

higher than the sum of Tot MUFA and Tot PUFA because PUFA contains more than one double bond. (5 + 6 + 7x8 + 7x9 + 11 + 12 + 13x14 + 13x15 + 17 + 18 + 19x20 + 19x21 + 24 + 25 + 26x27 + 26x28 + 30 + 31)

32 Tr-MUFA Sum of the area-percents of trans monoenes (5 + 11 + 17 + 24 + 30) 33 Cis-MUFA Sum of the area-percents of cis monoenes (6 + 12 + 18 + 25 + 31) 34 Tr-PUFA Total amounts of trans double bonds in PUFA (7x8 + 13x14 + 19x20 +

26x27) 35 Cis-PUFA Total amounts of cis double bonds in PUFA (7x9 + 13x15 + 19x21 +

26x28) note that Cis-PUFA + Tr-PUFA > Tot PUFA 36 Tot Trans Sum of Tr-MUFA and Tr-PUFA (32 + 34) 37 Tot Cis Sum of Cis-MUFA and Cis-PUFA (33 + 35) 38 % tr (tot) Percent of all double bonds which are trans (36 / 31 x 100) 39 % tr (MUFA) Percent of all monoenes which are trans (32 / 29 x 100) 40 % tr (PUFA) Percent of all double bonds in PUFA which are trans (34 / (34+35)x100)

- 94 -

Table 6.1-3) Overview over product specifications

Code Factory Giv. Mp1 Anal. Mp2 IV1 NMR 201 NMR 301

A-26 A 25-27 (30.0) max 80 6-12 0-2 A-29 A 28-30 (31.0) max 83 21-27 2-6 A-31 A 30-32 30.0-35.5 max 83 30.5-37.5 2-11 A-33 A 32-34 32.5 75 32-42 8-16

A-33b A 31-35 ca. 75 32-42 5-15 A-35 A 34-36 34.5-35.5 70 39-49 13-22 A-37 A 36-38 39.0 70 46-56 20-29 A-39 A 38-40 36.5-40.5 65 56-64 29-39 A-41 A 40-42 39.0 60 63-71 36-42 A-43 A 42-44 42.0-43.5 55 69-79 46-55 A-45 A 44-46 (44.5) 50 76-86 54-66 A-51 A 50-52 56.0 30 88-95 82-92 Mix1 A - (38) - 33-39 14-20 Mix2 A 38-41 40-42 - 42-50 18-25 Mix3 A min 41 - - 48-55 24-30 B-33 B 33 37.0 B-35 B 35 37.5 B-37 B 37 39.5 B-41 B 41 43.0 B-43 B 43 43.5 B-FH B - 56.0 C-1 C - 27.0 C-2 C - 33.5

Notes: Mix1: 38% PHFO with mp 45, 30% PHFO with mp 31 and 32% refined soybean oil Mix2: 88% PHFO with mp 31, 12% PHFO with mp 51. Mix3: 74.5% PHFO with mp 31, 15.5% PHFO with mp. 10% PHFO with mp 39. 1) Typical values, given from producer, some deviations from these values occur, especially with reference to the melting points 2) Range of measured values. Numbers in brackets may not be representative for the population of samples where only one or few of several samples are analysed.

- 95 -

6.1.3) The difference between the various products, overview Average values for the products are given in Tables 8.12-1 to 8.12-3, appendix 8.12. A bar plot of the amounts of SFA, MUFA and PUFA for the various products is shown in Figure 6.1-1. These three variables give a picture of the degree of hydrogenation. A similar plot of the % trans in MUFA and PUFA for the same products is shown in Figure 6.1-2. PC-plots are given in Figure 6.1-3 a-d.

In Figure 6.1-1 one can see that A26 deviates significantly from the other fats from factory A. As opposed to the other samples, this fat is produced from a raw oil rich in C20 and C22 monoenes. Considering the samples with melting points from 31 to 56 the following trends can bee seen:

Samples from factory A with melting points from 31 to 37°C show a slight decrease in the amounts of PUFA and a corresponding increase in the amounts of MUFA. The amounts of SFA are stable. At these low levels of hydrogenation there is still methylene-interrupted polyenes available for the PAM, which will dominate in a selective hydrogenation process. Thus methylene-interrupted PUFA are converted to monoenes and NMI-PUFA, conversion of MUFA to SFA is suppressed by the domination of PAM, which explains that SFA is not increasing.

From mp 37 to 51 at factory A and 33-56 at factory B, most methylene interrupted PUFA is probably converted to NMI-PUFA and MUFA, which is then converted to SFA by the HHS-mechanism. Thus an increase in SFA and a corresponding decrease in MUFA and PUFA is observed.

Figure 6.1-1)

Bar plot showing the percent of saturated fatty acids, MUFA and PUFA in selected products.

0

10

20

30

40

50

60

70

80

90

100

A26 A31 A33 A35 A37 A39 A41 A43 A45 A51 B33 B35 B41 B43 B-FH C1 C2

SFAMUFAPUFA

Per c

ent

Figure 6.1-2 that explains the percent of trans double bonds in monoenes shows that these levels are between 60 and 80 percent except for the least hydrogenated products A26, B33 and C1. There is a trend of increasing percentage of trans double bonds with increasing degree of hydrogenation. This trend is most evident when moving from the least hydrogenated fats to the medium hydrogenated fats. In the least hydrogenated fats there is still a lot of the original cis double bonds present which is rapidly hydrogenated and converted to new double bonds as hydrogenation proceed. As hydrogenation proceed further

- 96 -

the trans to cis ratio seem to be approaching an equilibrium which can be explained with the fact that the new double bonds are formed with a constant cis to trans ratio.

The difference between percent trans in MUFA and percent trans in PUFA also gives some information about selectivity and degree of hydrogenation. In the least hydrogenated fats, e.g. A26, B33, and C1 percent trans are significantly higher in PUFA than in MUFA. As hydrogenation proceeds this difference vanishes and in the most hydrogenated fats (A39-A51) the MUFA value is the larger of the two. This observation may be explained by selectivity for PAM over HHS mechanism. As long as methylene interrupted PUFA is present in the fats both hydrogenation and isomerisation will preferentially take place at these groups; thus monoenes is protected by the presence of MI PUFA in the least hydrogenated products. As hydrogenation proceed and MI PUFA is consumed the reaction rate for monoenes will increase, and some trans monoenes are also formed from hydrogenated PUFA, thus giving higher trans values for MUFA.

It is important to note that the samples from factory B has lower trans to cis ratios, especially in the case of monoenes, than similar samples from factory A. Since this is true also for the most hydrogenated fats where very little of the original double bonds are present this difference is probably explained by a difference in the trans to cis ratio of the double bonds formed and not by raw oil composition or degree of hydrogenation. Thus there is a process difference (e.g. catalyst, hydrogen pressure, temperature, etc.) between factory A and B giving lower trans values in the products from B.

Figure 6.1-2)

Bar plot showing the percent of double bonds that has trans configuration in selected products.

0

10

20

30

40

50

60

70

80

90

A26 A31 A33 A35 A37 A39 A41 A43 A45 A51 B33 B35 B41 B43 B-FH C1 C2

MUFAPUFA

Per c

ent t

rans

A more detailed description of the data is achieved by principal component analysis (PCA).

PC-plots of the samples is given if Figure 6.1-3 a-d, where Figures a and b are based on the sum-parameters. Most samples are grouped quite close in the score plots, indicated by the eclipse. The five samples outside this eclipse can be said to represent extreme cases. A rough indication of the degree of hydrogenation is given by the dotted line.

The two first principal components explain 75% of the variation in the nine original variables, the loading plot (figure 6.1-3b). PC1 (x-axis) is related to the degree of unsaturation (number of double bonds) in the samples. Sat has a negative value along PC1 while the parameters describing unsaturation has a positive value along PC1. PC2 (y-axis) explains cis to trans

- 97 -

ratio, parameters describing trans unsaturation has a positive value along PC2 while parameters describing cis unsaturation has a negative value along PC2.

% Trans (both PUFA and MUFA) has a negative value at PC1. This has the following explanation: As the degree of hydrogenation (and amount of SFA) increase, the percent trans in the remaining double bonds also increase. Thus this correlation leads to negative values of % trans in PC1, even though %-trans is a quality related to unsaturation which has a positive value along PC1.

The “outliers” in the score plots may be explained in the following manner: mix1 is the only sample which is a blend of an unhydrogenated oil (soybean) and PHFO, which explains the high values of cis double bonds and total double bonds. This sample will not be discussed further.

Regarding the samples A26, C1 and B33: These samples deviate from the eclipse principally along PC2. These are the least hydrogenated samples where much of the original cis double bonds are still present, giving high cis to trans ratios. In this area of the plot hydrogenation will occur mainly by PAM, giving rapid consumption of original cis double bonds and conversion to trans double bonds.

The other two extreme samples are the highly hydrogenated fats B-FH and A51. When moving from the encircled area to these samples the methylene interrupted polyenes are already consumed, leading to hydrogenation by the HHS-mechanism only.

Even though it may be a bold assertion to make, the bend in the arrow indicating the degree of hydrogenation may confirm the existence of both PAM and HHS-mechanisms under conditions of selective hydrogenation. In the presence of just one mechanism the degree of hydrogenation should bee explained by a straight line. In this respect it is important to note that the samples with different degrees of hydrogenation do not represent different stages in one hydrogenation process, but is actually products of different processes and to a certain degree also differences in the raw oil composition.

The PC-plots from the PCA using the original variables (Figure 6.1-3 c and d) is more complicated to interpret than the plots from the sum-variables due to the larger number of variables. Only 54% of the total variation is explained by the first two principal components, thus one should be careful not to draw too firm conclusions based on these plots alone. The trends that PC1 explains the degree of saturation and that PC2 explains the cis-trans ratio is clearly visible also in these plots. All saturated fatty acids, C16 and longer, are grouped together in the left side of the loading plots, and thus correlates with the highly saturated fats A51 and B-FH. All cis variables, C18 and longer, has positive values along PC2 while the corresponding trans-variables has negative values. C1 has a different position in this plot and is probably pulled down along PC2 by correlations with C2 and its high content of C20 and C22 PUFA. This is possible caused by the influence of the raw oil composition and will be discussed further in section 6.1.4.

By looking at the encircled samples in the score plot (and including B33) one can see that samples from factory B differs significantly from samples from factory A and C. The loading values and Figure 6.1-2 show that the main difference is caused by a higher cis to trans ratio in products from factory B. This difference is discussed further in section 6.1.5.

- 98 -

Figure 6.1-3a)

Score plot of representative samples of different products.

-4

-3

-2

-1

0

1

2

-8 -6 -4 -2 0 2 4

X-expl: 49%,26%

A26

A31A33

A35

A37A39

A41A43A45

A51

mix2mix3

A33bA29

mix1

B33

B35B41

B43

B-FH

C1

C2

PC1

PC2

degree

of hyd

rogenati

on

Figure 6.1-3b)

Loading plot with selected sum-variables, corresponding to the above score plot. Variables are standardised

-0.4

-0.2

0

0.2

0.4

0.6

-0.6 -0.4 -0.2 0 0.2 0.4

X-expl: 49%,26%

Sat

Tot MUFA

Tot PUFA

Tr-MUFA

Cis-MUFA

Tr-PUFA

Cis-PUFA

% trans (MUFA)% trans (PUFA)

PC1

PC2

Figure 6.1-3c)

Score plot of representative samples of different products.

-4

-2

0

2

4

6

8

-15 -10 -5 0 5

X-expl: 38%,16%

A26

A31

A33

A35

A37

A39A41

A43A45

A51

mix2mix3

A33b

A29

mix1

B33B35B41

B43

B-FHC1

C2

PC1

PC2

- 99 -

Figure 6.1-3d)

Loading plot with original variables, corresponding to the above score plot. Variables are standardised

-0.4

-0.2

0

0.2

0.4

-0.3 -0.2 -0.1 0 0.1 0.2 0.3

X-expl: 38%,16%

12:0

14:0

15:016:0

16:1t

16:1c

16:PU

16:PU-t16:PU-c18:0 18:1t

18:1c

18:PU

18:PU-t

18:PU-c

20:020:1t

20:1c

20:PU

20:PU-t

20:PU-c

21:0

22:0

22:1t

22:1c

22:PU

22:PU-t

22:PU-c24:0

24:1t

24:1c

PC1

PC2

6.1.4) The influence of the raw oil composition Description of the raw oils Figure 6.1-4 shows PC-plots of the raw oil compositions from factory A and factory C. The PCA is done on unweighted variables to allow the fatty acids with the largest absolute variance to have the greatest influence in the analysis. There is mainly two major effects seen in the loading plot. PC1 explains the difference between oils with a large content of long chain MUFA (herring and capelin like oils) and oils with low contents of monoenes, but larger content of long chain PUFA. The difference between the sample RA15 and RC1 in 20:1 content and in 22:1 content is 14.8% to 2.3% and 17.8% to 1.0% respectively.

PC2 separates principally between oils with a large content of EPA (typically Southern Hemisphere) from oils with a large content of DHA (typically Northern Hemisphere). In sample RC1, which is from the South-American factory, the EPA/DHA-ratio is 3.7 while this ratio is only 0.97 in sample RA3 (see also Table 8.12.7 appendix 8.12).

The oil RA15 is very rich in monoenes and only used for the production of fat A-26. The other products from factory A are produced from oils with significantly lower monoene content. In the raw oils used in factory A, a seasonal variation (encircled in the score plot) is seen along PC2. Raw oils used in the spring are separated from those used in the autumn. Each of these two groups have one outlier, RA4 and RA16.

The seasonal variation is principally caused by difference in EPA and 18:1n-9. By comparing the most extreme samples in each of the groups, RA3 and RA10, an indication of the difference is given. RA3 has 14.0% 18:1n-9 and 10.9% EPA while corresponding values for RA10 is 9.1% and 15.6%.

A bar plot of the most important variables in the loading plot, for selected samples is given in Figure 6.1-5. This may give an indication of the proportions of the relative differences.

It should be emphasised that the samples in the autumn and spring groups in Figure 6.1-4a are all from 1997, except RA14 which is from spring ’98. Thus the autumn-spring distinction seen in the plot can probably not be used as a general rule. However, the two main effects seen in the loadings, one as a result of the long chain monoene content, and the other as a result of variation of the EPA/DHA contents is a typical pattern seen in PC plots of marine fish products (with the exception of products from farmed fish where e.g. vegetable oils introduce additional effects).

- 100 -

Figure 6.1-4a)

Score plot of raw oils used in the survey.

-4

-2

0

2

4

6

-5 0 5 10 15 20 X- expl : 71%,15%

RA11RA13

RA7

RA10

RA4A12

RA9RA6

RA1

RA8RA2

RA5

RA3

RA16

RC1

RA14

RA15

PC1

PC2

Spring

Autumn

RC2

Figure 6.1-4b)

Loading plot with fatty acids in raw oils, corresponding to the above score plot. Variables are unweighted.

-0.6

-0.4

-0.2

0

0.2

0.4

0.6

-0.4 -0.2 0 0.2 0.4 0.6 0.8

X-expl: 71%,15% PC1

PC2

22:6n3

18:1n9

16:0 18:0 18:2n6

22:1n918:4n3

16:4

16:1n716:314:0

22:5n3 18:3n3

16:2

20:1n9 22:1n11

20:5n3

Figure 6.1-5)

Bar plot of the most important fatty acids (with reference to absolute variation) in selected raw oils.

0

5

10

15

20

25

14:0 16:0 16:4 18:1n9 20:1n9 22:1n11 20:5n3 22:6n3

RC1 RA16RA3 RA10RA15

- 101 -

The raw oil’s influence on the products In Figure 6.1-3c the sample A26 deviates significantly from the other products along PC2 even though its melting point is similar to C1, which has a value at PC2 close to zero. This large difference in composition between products with similar melting characteristics can be explained by their raw oil composition. A26 and C1 were produced from raw oils corresponding to RA15 and RC1 respectively. These raw oils are very different in composition and both oils represent extreme cases in Figure 6.1-4a and 6.1-5. The contrast is mainly in the contents of long chain monoenes and long chain polyenes. RA15 has particularly large amounts of C20 and C22 MUFA; RC1 has the lowest levels of these fatty acids, but is very rich in EPA. By comparing A26 and C1 in Figure 6.1-1 one can observe that the difference in raw oil composition is conserved in the products.

The difference between A26 and C1 is a case where there are extreme differences between the raw oils. These products are also from different factories with possible large differences in process conditions. To tell if smaller variations in the raw oil composition may have influence on the final products, samples from the winter 2000 were compared with samples from spring 1997.

In Figure 6.1-5 and 6.1-4a the raw oils are represented by samples RA16 and RA10 respectively. PC-plots of the products, which are supposed to have melting points around 31, 39, and 45° C, are given in Figure 6.1-7.

Both score plots are very clear: The first principal component explains the degree of hydrogenation, while the second principal components differentiate between samples from winter and samples from spring. The loading plot of the sum-parameters indicates that the rather small differences between the raw oils are conserved in the products. All variables that describe the amount of MUFA have positive values along PC2 and thus correlates with the spring samples. All variables describing amount of PUFA has negative values along PC2 thus correlating with winter samples. Also the amount of saturates has a negative value along PC2, indicating larger amounts of saturated fatty acids in the winter samples. Regarding the trans values, percent trans seems to have weak positive correlation with the spring samples.

PC1 explains the degree of hydrogenation. All variables that usually correlates with larger degree of hydrogenation (Amounts of saturated and percent trans in both MUFA and PUFA) have large positive values while the parameters describing degree of unsaturation have negative values. The two principal components describe as much as 95% of the variance in the dataset.

The loading plot of the original variables (figure 6.1-7d) is more complicated to interpret, but the trends are rather clear: The long chain saturated fatty acids (encircled with grey) has large positive values along PC1, while the parameters describing poly-unsaturation (encircled with grey) has large negative values. Thus PC1 describes the degree of hydrogenation. The long chain MUFA (encircled with red) has the largest values along PC2. Thus PC2 describes differences related to the raw oils. The two principal components describe 65% of the original variance in the 27 variables.

Bar plots of selected variables are presented in Figure 6.1-6 and demonstrate the same trends as seen in the PC-plots. The amount of saturates, MUFA and PUFA are given in Figure a-c. The winter samples have larger values of saturates and PUFA while the spring samples have larger values of MUFA, thus the minor differences that were observed in the raw oils are conserved.

- 102 -

Regarding the monoene content one should distinguish between cis MUFA (Figure d) and trans MUFA (Figure e). Except in the case of melting point 31, the spring samples are only higher in trans MUFA and not cis MUFA. Because of the balance of PUFA, which has a large trans content, and is lower in spring than in winter samples, there are only small differences in total trans content. The spring samples has a total trans value that is significantly higher than the winter samples in one of three cases only.

It should be emphasised that these results are probably not representative for winter and spring samples in general. The raw oil from the winter 2000 is one single sample that is actually an outlier in the PC-plot of the raw oils.

Regarding the raw oil representative for the spring 97-samples, this is probably represented by a sample between RA3 and RA2 in Figure 6.1-4a, which means that the assumptions made on the long-chain monoene content probably is true. In this group there is little variation along PC1, which explains the variation in 20:1 and 22:1 content.

The products with different melting points are single samples, or representative samples picked from a group by PCA survey.

The conclusion is that also modest differences in the monoene content in the raw oils have influence on the products. This is because PUFA is only to a limited extent converted to MUFA in the hydrogenation process (with an exception for the most hydrogenated products).

Figure 6.1-6)

Bar plots of selected parameters indicative of the differences between samples from spring 1997 and winter 2000. Samples with assumed melting points 31, 39 and 45. All samples from factory A.

0

10

20

30

40

50

60

31 39 450

10

20

30

40

50

60

31 39 450

10

20

30

40

50

60

31 39 45

a) Saturated (%) b) MUFA (%) c) PUFA (%)

0

5

10

15

20

25

30

35

31 39 450

5

10

15

20

25

30

35

31 39 450

10

20

30

40

50

60

31 39 45

d) cis MUFA (%) e) trans MUFA (%) f) Tot. trans

Winter ’00 Spring ’97

The other effect in the raw oils explained by PC2 in Figure 6.1-4, the differentiation between oils rich in EPA and oils rich in DHA will also be conserved in the products. This is an effect that is related to differences in chain length, which obviously is not altered by hydrogenation.

- 103 -

Figure 6.1-7a)

Score plot of PCA, samples with assumed melting points 31, 39 and 45°C from spring 1997 and winter 2000. All samples from factory A.

-2

-1

0

1

2

-3 -2 -1 0 1 2 3 4

X-expl: 71%,24%

31 (W-00)

31 (S-97)

39 (W-00)

39 (S-97)

45 (W-00)

45 (S-97)

PC1

PC2

Figure 6.1-7b)

Loading plot with selected sum-variables, corresponding to the above score plot. Variables are standardised

-0.4

-0.2

0

0.2

0.4

0.6

0.8

-0.4 -0.2 0 0.2 0.4

X-expl: 71%,24%

Sat

Tot MUFA

Tot PUFA

Tr-MUFA

Cis-MUFA

Tr-PUFACis-PUFA

% tr (MUFA)

% tr (PUFA)

PC1

PC2

Figure 6.1-7c)

Score plot of PCA, samples with assumed melting points 31, 39 and 45°C from spring 1997 and winter 2000. All samples from factory A.

Variables are standardised.

-4

-2

0

2

4

-6 -4 -2 0 2 4 6

X-expl: 52%,31%

31 (W-00)

31 (S-97)

39 (W-00)

39 (S-97)

45 (W-00)

45 (S-97)

PC1

PC2

- 104 -

Figure 6.1-7d)

Loading plot with original variables, corresponding to the above score plot.

Trans: red

Cis: green

Saturated: blue

Total PUFA (cis+tr): grey.

-0.3

-0.2

-0.1

0

0.1

0.2

0.3

-0.3 -0.2 -0.1 0 0.1 0.2 0.3

X-expl: 52%,31% 12:0

14:0

15:0

16:0

16:1t

16:1c

16:PU

16:PU-t

16:PU-c

18:0

18:1t

18:1c

18-PU

18:PU-t

18:PU-c

20:0

20:1t20:1c

20:PU

20:PU-t

20:PU-c

21:0 22:0

22:1t22:1c

22:PU

22:PU-t

22:PU-c 24:0

24:1t

24:1c

PC1

PC2

6.1.5) The difference between samples from factory A and factory B The difference in composition between samples from factory A (and C) and factory B is obvious from the plots in Figure 6.1-3. Figure 6.1-8 presents bar plots of selected variables and selected samples that is supposed to have the same melting points, from factories A and B.

The difference in the contents of MUFA (Figure b) and PUFA (Figure c) indicates that the difference between the two factories is caused by difference in raw oil composition (unfortunately raw oils from factory B was not available). These plots show similar trends as seen in Figure 6.1-6 a-c. However, comparison of Figures d and e reveals a different pattern. Factory B is significantly higher in cis MUFA for all melting points. Factory B is also slightly lower in trans PUFA. Following the “rules” from Figure 6.1-6 one should expect factory B to have higher values than A in this variable. There is also a distinct difference in total trans, a difference that was not present in Figure 6.1-6.

Figure 6.1-9 presents the PC-plots of the samples from factory B together with samples with samples with melting points in the same range from factory A. The samples from the two factories are clearly separated in the two score plots, both when the sum-variables and when the original variables are used. The distinction between the two groups is given by a combination of the two principal components and marked by a grey line in the plots. Along the grey line is a gradient of different melting points.

The loading plots reveal that percent trans in MUFA and PUFA, together with the total amounts of cis MUFA and trans MUFA is the most important parameters that separate the two groups.

Cis and trans MUFA are negatively correlated, while the two parameters were positively correlated in Figure 6.1-7 explaining the influence of monoenes in the raw oils.

- 105 -

Figure 6.1-8)

Bar plots samples from factory A and B that is supposed to have the same melting points.

Selected variables

0

5

10

15

20

25

30

35

33 35 41 430

5

10

15

20

25

30

35

33 35 41 430

10

20

30

40

50

60

33 35 41 43

d) cis MUFA (%) e) trans MUFA (%) f) Tot. trans

0

10

20

30

40

50

60

33 35 41 430

10

20

30

40

50

60

33 35 41 430

10

20

30

40

50

60

33 35 41 43

a) Saturated (%) b) MUFA (%) c) PUFA (%)

Factory A Factory B

These trends are also confirmed by the loading plot of the original variables, the trans and cis variables (e.g. 20:1 cis and 22:1 cis) do not group together as seen in the corresponding loading plot in Figure 6.1-7. Note that only 57 percent of the total variation is explained in Figures 6.1-9c and d, thus one should not draw to firm conclusions based on these plots only.

From this one might conclude that the difference between factories A and B is caused by a different (or additional) effect than only the raw oil's fatty acid pattern. This difference is probably related to the choice of catalyst, reaction temperature or hydrogen pressure.

Figure 6.1-9a)

Score plot of PCA, samples from factories A and B with similar melting points, sum-variables

-3

-2

-1

0

1

2

3

-4 -3 -2 -1 0 1 2 3 4

X-expl: 48%,31%

B33

B35

B41

B43

A33

A35

A37

A39

A41

A43

PC1

PC2

Factory B

Factory A

- 106 -

Figure 6.1-9b)

Loading plot with selected sum-variables, corresponding to the above score plot. Variables are standardised.

-0.4

-0.2

0

0.2

0.4

0.6

-0.5 0 0.5

X-expl: 48%,31%

Sat

Tot MUFA

Tot PUFA

Tr-MUFA

Cis-MUFA

Tr-PUFA

Cis-PUFA% trans (MUFA)

% trans (PUFA)

PC1

PC2

Figure 6.1-9c)

Score plot of PCA, samples from factories A and B with similar melting points, original variables

Variables are standardised

-4

-2

0

2

4

6

-4 -2 0 2 4 6

X-expl: 33%,24%

B33

B35

B41

B43

A33

A35

A37A39

A41

A43 PC1

PC2

Factory B

Factory A

Figure 6.1-9d)

Loading plot with original variables, corresponding to the above score plot.

Trans: red,

Cis: green

Saturated: blue.

Total PUFA (cis+tr): grey.

-0.3

-0.2

-0.1

0

0.1

0.2

0.3

0.4

-0.3 -0.2 -0.1 0 0.1 0.2 0.3

X-expl: 33%,24%

12:0

14:0

15:0 16:0

16:1t

16:1c

16:PU16:PU-t16:PU-c

18:0

18:1t

18:1c18:PU

18:PU-t18:PU-c

20:0

20:1t

20:1c

20:PU

20:PU-t20:PU-c

21:022:0

22:1t

22:1c

22:PU

22:PU-t22:PU-c

24:0

24:1t

24:1c

PC1

PC2

- 107 -

6.2) Detailed studies

6.2.1) Identification and quantification. A detailed description of five samples of hydrogenated oils has been achieved by the combination of Ag-HPLC and GC-IR and GC-MS. The solid fats were first derivatised to FAME by the method described in appendix 8.2 and then subsequently fractionated by Ag-HPLC. The various fractions were then analysed both on GC-MS and GC-IR by the methods described in 8.7 and 8.8.

Each of the fractions had a known amount of internal standard (15:0 FAME) added. By assuming there is no changes in the split ratio between the HPLC fraction collector and HPLC light scattering detector (see Figure 5.1-1) the total composition of the original may then be calculated by summing the results for each of the fractions.

Identification and separation of monoenes. Identification of monoenes was conducted by combining the information from several sources. A general description of the gas chromatographic behaviour of monoenes is given below. In addition to the information from the retention times the mass spectra of FAME with double bond positions from ∆3-9 gave some information (see section 5.6.2). Where mass spectra of acceptable quality could be achieved the application of DMOX-derivatives could be used to confirm the identity of the isomers.

Some doubts exist about the exact identity of the isomers with double bonds in ∆3-6 position. When the double bonds are too close to the carbonyl group exceptions from the general pattern occur, both with respect to mass spectra and retention time behaviour in GC and HPLC.

Gas chromatographic behaviour BPX-70 (SGE, Ringwood Australia) with cyanopropyl as the polar functional group in the stationary phase was chosen as column for separation of positional isomers. Usually the slightly more polar columns CP-Sil 88 or SP-2560 are chosen for separation of trans monoenes. Although these columns may be slightly better in separating trans isomers from cis isomers they seem to have no advantage over BPX-70 in separating positional isomers when the cis/trans separation are already done by Ag-HPLC. Even the PEG columns with relatively low polarity have been shown to give good resolution of positional 18:1-isomers (Thompson 1997b). BPX-70 is a bonded phase, which show lower column bleed and can be used at higher temperatures than SP-2560, a feature that is of importance when analysing long chain fatty acid methyl esters. Generally, both the column capacity and the separation efficiency decrease with increasing column polarity. This property may also be of importance. Severe skewing and peak broadening of the long chain saturated fatty acids were observed with SP-2560, but not with BPX-70.

The elution pattern of both positional trans and positional cis isomers seem to be similar regardless of the stationary phase used (Christie 1988, Duchateau et al. 1996, Thompson 1997b, Wolff and Precht 1998, Kitayama et al. 1998). The separation of cis from trans fatty acids increases with column polarity, and also by operating temperature (Thompson 1997a). There seem to be some minor differences in the elution patterns between FAME and DMOX derivatives (Aro et al. 1998).

With the exception of ∆3 to ∆6 isomers the retention times increase when the distance between the double bond and carbonyl group increases. Cis isomers are generally more easy to separate than trans isomers. Examples of excellent resolution of individual C18 isomers

- 108 -

using low temperature programs can be found in the above references and in Molkentin and Precht (1995), Wolff and Bayard (1995), Wolff (1999), Mossoba et al. (1997). Elution patterns for individual C16 isomers are reported in Molkentin and Precht (1997).

Less work is done on the C20 and C22 isomers. The elution pattern on OV-275 is given in Svensson et al. (1982) and ECL values for the most common cis isomers on Silar 5CP and Silar 7CP are reported by Sebedio and Ackman (1982). The column used in these two articles differs significantly from the cyanopropyl columns generally used today.

Usually the ∆6-8 trans isomers can not be separated. There is also a problem separating n-4 and n-5 isomers both in the C16 (Molkentin and Precht 1995) and C18 isomers (Wolff and Bayard 1995, Wolff and Precht 1998, Aro et al. 1998). The n-4 and n-5 isomers have been resolved using low temperatures and extremely long retention times (Mossoba et al. 1997, Wolff and Precht 1998, Wolff 1999). The partial overlap between n-4 and n-5 combined with the retention times of n-3 and n-2 is useful for identification of the isomers with double bond positions far from the carbonyl group.

For the analysis of positional isomers four Ag-HPLC fractions were collected of the monoenes, the first two containing trans isomers and the last two containing the cis isomers. An example is shown in Figure 6.2-1a and b. When the distance between the carbonyl group and the double bond is eight carbons or more the elution pattern of the positional monoenoic isomers is opposite that of gas chromatography. The retention time will decrease as the double bonds move further apart from the carbonyl group. (Christie and McG.Breckenridge 1989, Adlof et al. 1995, Nikolova-Damyanova et al. 1992) Between ∆3 and ∆8 the retention behaviour is less predictable because of more complex interactions between the double bond, the carbonyl group, and the silver ion (Christie 1998). It is important to note that even though the elution pattern in Ag-HPLC is reverse that found in GC, isomers that elute close in GC will still elute close in Ag-HPLC, which means that the HPLC separation is of limited value in improving the total resolution of the system.

Quantification of monoenes:

An example of typical MS chromatograms are given in Figure 6.2-1 a and b. There is substantial overlap between chromatographic peaks, especially in the case of the trans isomers. Thus the figures reported give only a rough indication of the levels of certain isomers.

Polyunsaturated fatty acids, positional distribution

Except that the natural isomers 16:2n-6 and 18:2n-6 were identified in the least hydrogenated oil (C1), no attempt of complete identification of the large number of dienoic and trienoic isomers was made. The mass spectra of the majority of these peaks generally differ significantly from the mass spectra of methylene interrupted and conjugated isomers, indicating that most isomers are of the NMI type. While m/z 67 normally is the base peak of the methylene-interrupted isomers, m/z 55 is the base peak in the majority of the monoenoic and trienoic isomers found in these samples. Other large peaks are usually 67, 69, 81 and 82 and 95. It may be possible to deduce the number of carbons between the double bonds from the FAME mass spectra, but this has not been investigated further.

Sébédio and Ackman (1983b) also found a large amount of NMI dienes/trienes in three hydrogenated fats from menhaden oil. Some details of the double bond positions in dienes and trienes may be found in this reference.

- 109 -

Figure 6.2-1a)

GC-MS chromatogram of the first of two fractions containing C20 trans monoenes. Sample A39.

32.50 33.00 33.50 34.00 34.50 35.00 35.50 36.00 36.50

∆18

∆17

∆15/16∆14

∆13

∆12

∆11

∆10

∆5

Figure 6.2-1b)

GC-MS chromatogram of the first of two fractions containing C20 cis monoenes. Sample A39.

32.50 33.00 33.50 34.00 34.50 35.00 35.50 36.00 36.50

∆5

∆10

∆11

∆12

∆13

∆14∆15 ∆16

∆17 ∆18

Calculating the amounts of dienes and trienes in mixed IR-spectra There is substantial overlap between ct dienes, cc dienes, and trienes in certain peaks. Assuming that only dienes and trienes are present in a mixed peak, and that all dienes present are either of the cis-cis or cis-trans type (usually cis-cis) the amounts of each type of isomers can be calculated. An example is shown below.

The spectrum of the C20 isomers in HPLC fraction 11 of sample A51 is given in Figure 6.2-2 below. The PLS regression on the derived spectrum gives an average trans content of 2.27 and an average cis content of 0.53. Summarising these numbers, the average number of double bonds is calculated to 2.80. Since only dienes and trienes are present, the amount of trienes and dienes can be calculated to 80% and 20% respectively.

Where x is the average amount of trans and y is the average amount of cis in the trienes the following equations can then be set up:

0.20 • 0 trans = 0 trans+ 0.80 • x trans = 0.80x trans= 2

0.20 • 2 cis = 0.40 cis+ 0.80 • y cis = 0.80y cis= 0.53 cis

.27 trans(I)

(II)

- 110 -

Simple subtraction in (I) and (II) give 0.80x is 2.27 trans and 0.80y is 0.13 trans. The average cis and trans content in the trienes is then found by dividing by 0.80, thus the average trienes are 2.84 trans and 0.16 cis. Assuming that only all-trans and trans, trans, cis isomers are present this corresponds to 16% (13% of total C20) of the trans, trans, cis isomer and 84% (67% of total) all-trans isomer.

The C20 peak cluster in this fraction contains only 0.29% of total fatty acids, the contributions of these isomers to the total fat is therefore 0.06, 0.04 and 0.19% for cc dienes, ctt trienes and all-trans trienes respectively.

From the figures above it can be seen that the contribution from the trienes in this fraction to total trans can be neglected in this sample. In fats with higher triene content, where the trienes may have a significant contribution to the total trans, tetraenes are usually present in the same peak clusters as dienes and trienes. In such cases the amount of dienes can not be calculated from the average number of double bonds, and thus dienes are reported as PUFA in some of the least hydrogenated fats.

Figure 6.2-2) Infrared spectrum of C20-fatty acids in HPLC fraction 11, sample A52

6.2.2) The composition of sample C1 This fat has a high iodine value, and thus is very little hydrogenated. The sample is partially liquid at room temperature and usually separates into two phases. The original cis dienes and monoenes found in raw fish oils are still present in rather large amounts. A relatively large amount of trienes and tetraenes was also found but none of the natural polyenoic fatty acid isomers seem to be present at significant amounts.

Figure 6.2-3)

GC-MS trace, unfractionated fat, sample C1

10.00 15.00 20.00 25.00 30.00 35.00 40.00 45.00 50.00 55.000

50000

100000

150000

200000

Time-->

AbundanceTIC: S991122O.D

15:0

14:0

16:0

16:1n7

18:2n6

18:0

18:1n9

22:1n1120:1n9

- 111 -

The GC/MS-trace is given in Figure 6.2-3 and quantified results are summarised in Table 6.2-1.

Table 6.2-1) Overview of the fractions, sample C1

Chain length Sat. Trans

mono. Cis

mono. tt-

dienesct-

dienescc-

dienes PUFA Avg

trans (PUFA)

Avg cis

(PUFA) 14 6.81 0.03 0.01 - - - - - - 15 0.55 - - - - - - - - 16 20.00 3.48 6.07 0.62 1.33 0.51 1.03 0.73 2.16 17 0.55 0.42 0.11 0.02 - - - - - 18 3.99 5.18 13.91 0.55 1.39 0.86 1.41 1.16 2.13 19 0.04 0.02 0.02 - - - - - - 20 0.13 1.48 2.80 1.36 2.74 * 8.98 1.48 2.25 21 - 0.01 - - 0.02 - - - - 22 - 0.93 2.38 0.74 1.50 * 7.98 1.66 1.99 24 - 0.19 - 0.07 0.09 - - - -

SUM 32.08 11.73 25.30 3.35 7.06 1.37 19.40

* Reported as PUFA, see text

The large amount of PUFA present makes quantification of dienes and polyenes a difficult task. Usually the late eluting HPLC fractions contains mixtures of dienes and trienes, and the amount of each can be quantified from the average number of double bonds found by IRD. In this fat large amounts of tetraenes are also present in the same fractions. In cases where dienes, trienes and tetraenes are present in overlapping peaks the amount of each can not be quantified from the average number of double bonds. The C20 and C22 cis-cis dienes are therefore included in the tables as PUFA.

IRD-results were missing for one of the fractions. This fraction contained mostly cis-trans dienes, but there could also be trans-trans dienes present. All isomers in this fraction are reported as cis-trans dienes. Thus trans-trans dienes may be slightly underestimated and cis-trans dienes may be slightly overestimated.

The amounts of the positional isomers of monoenes are given in Table 6.2-2. Contrary to the more hydrogenated fats, this sample has larger amounts of cis than trans isomers. Low degree of isomerisation and hydrogenation of monoenes explain the large amount of cis monoenes. The original monoenoic isomers, 16:1n-7, 18:1n-9, 18:1n-7, 20:1n-9, 20:1n-7, 22:1n-11 and 22:1n-9 is not destroyed by the hydrogenation process and is therefore still present at rather large amounts.

The distributions among the trans monoenes show high values for those isomers with the double bond in positions close to the original cis isomers. The value drops sharply as the double bonds move towards the ends of the molecule.

- 112 -

Table 6.2-2) Positional distribution of the monoenes, sample C1. Percent of

total fat Percent Percent of

total fat Percent

Trans 16:1 Cis 16:1 t5-16:1 0.01 0.42 c5-16:1 0.05 0.86t6-16:1 0.06 1.72 c7-16:1 0.24 4.03t7-16:1 0.38 10.90 c8-16:1 0.15 2.39t8-16:1 0.52 14.92 c9-16:1 5.11 84.24t9-16:1 1.47 42.24 c10-16:1 0.25 4.09t10-16:1 0.59 16.83 c11-16:1 0.16 2.63t11-16:1 0.18 5.30 c12-16:1 0.06 0.99t12-16:1 0.08 2.36 c13-16:1 0.03 0.49t13-16:1 0.16 4.60 c14-16:1 0.02 0.28t14-16:1 0.02 0.71

3.48 100.00 6.07 100.00

Trans 18:1 Cis 18:1 t6-18:1 0.02 0.40 c5-18:1 0.03 0.19t7-t9-18:1 3.16 61.01 c7-18:1 0.14 1.04t10-18:1 0.77 14.91 c8/9-18:1 10.69 76.84t11-18:1 0.77 14.78 c10-18:1 0.40 2.84t12-18:1 0.25 4.77 c11-18:1 2.33 16.75t13/14-18:1 0.18 3.42 c12-18:1 0.16 1.16t15-18:1 0.02 0.44 c13-18:1 0.10 0.73t16-18:1 0.01 0.27 c14-18:1 0.04 0.27

c15-18:1 0.03 0.19 5.18 100.00 13.91 100.00

Trans 20:1 Cis 20:1 t5-20:1 0.02 1.12 c5-20:1 0.03 1.16t6-20:1 0.05 3.40 c8/9-20:1 0.25 8.82t7-9-20:1 0.25 16.82 c10-20:1 0.25 9.06t10-20:1 0.34 22.89 c11-20:1 1.85 65.91t11-20:1 0.36 24.15 c12-20:1 0.08 2.96t12-20:1 0.15 9.85 c13-20:1 0.23 8.20t13-20:1 0.12 8.15 c-14:20:1 0.04 1.25t14-20:1 0.07 4.87 c15-20:1 0.04 1.36t15/16-20:1 0.10 6.83 c16-20:1 0.01 0.52t17-20:1 0.03 1.93 c17-20:1 0.02 0.77

1.48 100.00 2.80 100.00

Trans 22:1 Cis 22:1 t8-10-22:1 0.17 18.09 c11-22:1 1.94 81.60t11-22:1 0.40 43.21 c13-22:1 0.31 13.02t12-22:1 0.11 11.61 c15-22:1 0.13 5.38t13-22:1 0.10 10.60 * t14-22:1 0.03 3.75 t15-22:1 0.05 5.09 t16-22:1 0.02 2.44 t17/18-22:1 0.03 3.74 t19-22:1 0.01 1.47

0.93 100.00 2.38 100.00

* Small amounts of isomers in even numbered positions are probably masked the natural isomers

- 113 -

Residues of the original dienes 16:2n-6 and 18:2n-6 was found in one of the late eluting HPLC-fractions. GC-MS chromatogram is shown in Figure 6.2-4 below. 16:2n-6 accounts for 0.24 percent of total fatty acids and 47 percent of the C16 cis-cis monoenes. 18:2n-6 accounts for 0.47 percent of total fatty acids and 55 percent of the C18 cis-cis monoenes.

None of the original more unsaturated PUFA was discovered. Together with the large amounts of original cis monoenes this reveals a process with high selectivity for methylene interrupted double bond systems.

Figure 6.2-4)

C16 and C18 region in GC-MS chromatogram of HPLC fraction of dienes and trienes. Original 16:2n-6 and 18:2n-6 are still present at significant amounts. Sample C1

20.00 21.00 22.00 23.00 24.00 25.00 26.00 27.00 28.00 29.00 30.000

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AbundanceTIC: S991122K.D

dienes and trienes

dienes and trienes

18:2n6

16:2n6

6.2.3) The composition of sample C2 This is the most hydrogenated of the two South-American fats. The quantified content of PUFA is limited to 5.56% of total fat; dienes represents 18.37% of the total and monoenes 37.19%. Some attention should be paid to the C20 and C22 fatty acids accounting for 18.7 and 15.5% of the total fatty acids.

The original polyunsaturated isomers are completely destroyed by the process, but only small amounts of saturated fatty acids are produced. A hydrogenation process where hydrogenation primarily occur via the conjugated intermediate will lead to rapid deterioration of the original methylene interrupted polyenes, but will not attack isolated double bonds, thus leading to accumulation of monoenes and NMI dienes and polyenes. The monoenes accounts for 35 and 32% of C20 and C22 fatty acids respectively, and the dienes accounts for 43 and 42% in this fat. The GC/MS-trace of an unfractionated sample is given in Figure 6.2-5 and quantified results are summarised in Table 6.2-3.

Figure 6.2-5)

GC-MS trace, unfractionated fat, Sample C2

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AbundanceTIC: S991129O.D

24:015:0

14:0

16:0

18:0

20:022:0

- 114 -

Table 6.2-3) Overview of the fractions, sample C2 Chain length Sat. Trans

mono. Cis

mono. tt-

dienesct-

dienescc-

dienes PUFA Avg

trans (PUFA)

Avg cis

(PUFA) 14 6.07 15 0.45 16 21.27 5.46 2.84 0.83 0.64 0.08 17 0.51 0.46 0.08 18 6.49 10.36 5.79 0.92 0.92 0.08 19 1.63 20 1.51 4.56 2.08 5.27 1.76 1.11 2.43 2.60 0.40 21 0.06 0.09 22 0.88 3.35 1.56 3.10 2.47 1.00 3.13 2.58 0.54 24 0.41 0.24 0.10

38.89 24.60 12.59 10.32 5.78 2.27 5.56

The monoenoic pattern is described in Table 6.2-4. Although this fat is more hydrogenated than C1 the original monoenoic isomers still dominate among the cis monoenes accounting for 35-60% of the fractions. Among the trans double bonds the isomers with double bonds close to the position in the original cis isomers dominate, and the levels falls rapidly towards the ends of the carbon chain.

6.2.3) The composition of sample A31' This fat is comparable to the sample C2. The GC/MS traces of the unfractionated fats are similar. Sample A31' has more monoenes (43%) than C2 (37.2%). The trans-cis ratio of the monoenes is similar in the two samples 63/37% in A31' and 66/34% in C2. This means that the process has not reached the "equilibrium state" seen in A39' and A51 where the trans-cis ratio is approximately 75/25. The mark on "A31' " and "A39' " indicates that these two fats are harder and outside the normal range of A31 and A39 samples and can thus not be compared directly to the samples in section 6.1.

Figure 6.2-6)

GC-MS trace, unfractionated sample. Sample A31'

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AbundanceTIC: S991109O.D

22:020:0

18:0

16:0

14:0

- 115 -

Table 6.2-4) Positional distribution of the monoenes. Sample C2

Percent of total fat

Percent Percent of total fat

Percent

Trans 16:1 Cis 16:1 t5-16:1 0.05 0.83 c5-16:1 0.07 2.39 t6-16:1 0.30 5.51 c7-16:1 0.30 10.67 t7-16:1 0.28 5.08 c8-16:1 0.30 10.41 t8-16:1 1.04 19.07 c9-16:1 1.44 50.80 t9-16:1 1.94 35.47 c10-16:1 0.39 13.81 t10-16:1 0.95 17.34 c11-16:1 0.14 4.80 t11/12-16:1 0.69 12.56 c12-16:1 0.11 4.02 t13-16:1 0.16 2.85 c13-16:1 0.05 1.87 t14-16:1 0.07 1.29 c14-16:1 0.04 1.24

5.46 100.00 2.84 100.00

Trans 18:1 Cis 18:1 t6-18:1 0.17 1.66 c5-18:1 0.03 0.60 t9-18:1 0.13 1.29 c8-18:1 0.64 11.06 t8/9-18:1 5.37 51.83 c9-18:1 3.17 54.73 t10-18:1 1.96 18.95 c10-18:1 0.66 11.34 t11-18:1 1.32 12.71 c11-18:1 0.81 13.92 t12-18:1 0.69 6.71 c12-18:1 0.25 4.35 t13/14-18:1 0.53 5.10 c13-18:1 0.10 1.81 t15-18:1 0.13 1.25 c14-18:1 0.06 1.07 t16-18:1 0.05 0.51 c15-18:1 0.04 0.69

c16-18:1 0.02 0.41 10.36 100.00 5.79 100.00

Trans 20:1 Cis 20:1 t5-20:1 0.07 1.50 c5-20:1 0.19 9.09 t6/9-20:1 0.66 14.38 c6/9-20:1 0.38 18.30 t10-20:1 0.83 18.19 c10-20:1 0.10 5.00 t11-20:1 1.18 25.93 c11-20:1 0.72 34.55 t12-20:1 0.53 11.63 c12-20:1 0.15 7.27 t13-20:1 0.30 6.50 c13-20:1 0.16 7.79 t14-20:1 0.26 5.78 c14-20:1 0.10 4.67 t15/16-20:1 0.47 10.40 c15-20:1 0.06 2.85 t17-20:1 0.18 3.92 c16-20:1 0.07 3.16 t18-20:1 0.08 1.77 c17-20:1 0.11 5.50

c18-20:1 0.04 1.79 4.56 100.00 2.08 100.00

Trans 22:1 Cis 22:1 t5-22:1 0.11 3.16 c5-22:1 0.09 5.51 t6/9-22:1 0.30 8.83 c6/10-22:1 0.13 8.53 t10/11-22:1 1.36 40.61 c11/12-22:1 0.93 59.62 t12-22:1 0.47 14.06 c13-22:1 0.14 9.09 t13-22:1 0.28 8.24 c14-22:1 0.06 3.59 t14-22:1 0.16 4.70 c15-22:1 0.05 3.40 t15-22:1 0.15 4.53 c16-22:1 0.04 2.60 t16-22:1 0.14 4.07 c17-22:1 0.03 1.73 t17/18-22:1 0.25 7.57 c18-22:1 0.03 1.78 t19-22:1 0.11 3.13 c19-22:1 0.05 3.25 t20-22:1 0.04 1.11 c20-22:1 0.01 0.91

3.35 100.00 1.56 100.00

- 116 -

Table 6.2-5) Overview of the fractions, sample A31'

Chain length Sat. Trans

mono. Cis

mono. tt-

dienes ct-

dienes cc-

dienes PUFA Avg

trans (PUFA)

Avg cis

(PUFA) 14 7.50 15 0.49 16 18.77 7.03 4.09 0.59 0.49 0.18 17 0.47 0.32 0.08 18 4.40 9.14 5.41 1.46 1.12 0.44 0.16 1.73 1.27 19 20 1.02 5.52 3.05 3.99 2.13 1.14 2.48 1.85 1.15 21 22 0.46 4.93 3.09 0.71 1.47 0.76 4.70 2.21 0.81 24 0.39 0.26 1.74

33.12 27.33 15.98 8.49 5.22 2.52 7.34

The content of dienes and PUFA are higher in sample A31' than in the more hydrogenated fats from Factory A. The levels are also similar to the levels found in sample C1.

The monoenoic pattern is presented in Table 6.2-6. Some of the original cis isomers are still present in fairly large amounts. 16:1n-7 accounts for more than 60% of the total 16:1 cis isomers, and 18:1n-9 accounts for more than 50% of the 18:1 cis isomers. Regarding the trans monoenes, the isomers with the double bonds close to the positions in the original cis isomers dominate and the levels drops rapidly for positions closer to the end of the molecule.

6.2.4) The composition of sample A39' This fat is the second most hydrogenated fat analysed by the combined method, and the GC/MS-trace of the unfractionated sample is very similar to that of A51. A more detailed analysis reveals lower amounts of saturated fatty acids and higher amounts of the unsaturated isomers.

The GC/MS-trace is given in Figure 6.2-7 and quantified results are summarised in Table 6.2-7.

Figure 6.2-7)

GC-MS trace, unfractionated fat, sample A39'

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AbundanceTIC: S991123N.D

- 117 -

Table 6.2-6) Positional distribution of the monoenes, sample A31' Percent of

total fat Percent Percent of

total fat

Trans 16:1

Percent

Cis 16:1

t5-16:1 0.04 0.06 1.54t6-16:1 0.17 0.26 6.34

0.55 c5-16:1 2.36 d7-16:1

c7-16:1 0.21 2.93 d8-16:1 9.780.92 13.11 d9-16:1 60.873.45 49.02

0.40 c8-16:1 2.49 t9-16:1 d10-16:1 0.51 12.39t10-16:1 1.34 19.08 c11-16:1 0.03 0.71t11-16:1 0.50 7.13 c10-16:1 0.17 4.27t12-16:1 0.26 3.71 c12-16:1 0.11 2.64t13-16:1 0.10 1.41 c13-16:1 0.04 0.92t14-16:1 0.05 0.71 c14-16:1 0.02 0.55

7.03 100.00 4.09 100.00

Trans 18:1 t6-18:1 1.84 c5-18:1 0.76t7/9-18:1 4.07

Cis 18:1 0.17 0.04

44.54 c7-18:1 0.13 2.48t10-18:1 1.68 18.33 c8-18:1 6.55t11-18:1 1.57 c9-18:1 2.74 t12-18:1 0.73 8.00

0.35 17.17 50.62

c10-18:1 0.57 10.53t13/14-18:1 0.68 7.46 c11-18:1 0.92 16.94t15-18:1 1.80 c12-18:1 0.31 5.67t16-18:1 0.08 0.86 c13-18:1 3.13

c14-18:1 0.09 1.72 c15-18:1 0.05 0.96

c16-18:1 0.03 9.14 100.00 5.41 100.00

Trans 20:1 Cis 20:1 t5-20:1 0.04 0.80 c5-20:1 3.23t6-20:1 0.35 6.33 c7/9-20:1 0.56 18.49t7/9-20:1 0.93 c10-20:1 0.16 5.38

2.11 38.29 c11-20:1 1.42 t12-20:1 0.73 13.28 0.25 8.10t13-20:1 0.38 6.93 c13-20:1 0.18 6.03t14-20:1 5.05 c14-20:1 0.11 3.53t15/16-20:1 0.44 8.03 c15-20:1 3.09t17-20:1 0.17 3.06 c16-20:1 0.07 2.30t18-20:1 0.08 c17-20:1 0.07 2.44

c18-20:1 0.03 5.52 100.00 3.05 100.00

Trans 22:1 Cis 22:1 t5-22:1 0.04 0.78 c5-22:1 1.45t6/9-22:1 0.19 3.89 c7/10-22:1 0.16 5.08t10/11-22:1 2.92 c11/12-22:1 2.37 76.64

0.53 10.73 c13-22:1 0.24 t13-22:1 0.41 8.33

0.16 0.17

0.64

0.10

16.82t10/11-20:1 46.58

c12-20:1

0.28 0.09

1.40 0.84

0.04

59.10t12-22:1 7.85

c14-22:1 0.06 2.09t14-22:1 0.19 3.89 c15-22:1 0.06 2.09t15-22:1 0.16 3.32 c16-22:1 0.03 1.02t16-22:1 0.12 2.36 c17-22:1 0.03 1.00t17/18-22:1 0.21 4.35 c18-22:1 0.03 0.85t19-22:1 0.11 2.30 c19-22:1 0.06 1.92t20-22:1 0.05 0.96

4.93 100.00 3.09 100.00

- 118 -

Table 6.2-7) Overview of the fractions, sample A39'

Chain length

Sat. Trans mono.

Cis mono.

tt- dienes

ct- dienes

cc- dienes

PUFA Avg trans

(PUFA)

Avg cis

(PUFA) 14 7.41 - - - - - - 15 0.68 - - - - - - 16 25.41 4.68 1.55 0.15 0.08 0.03 - 17 0.89 0.25 - 18 12.32 6.92 2.26 0.47 0.26 0.07 - 19 0.23 - - - - - - 20 6.30 6.36 2.10 2.15 0.88 0.08 0.48 1.66 1.34 21 0.26 - - 0.06 - - - 22 4.93 5.59 1.97 2.62 0.92 0.09 1.03 1.93 1.07 24 - 0.35 0.20 - - - -

58.41 24.14 8.08 5.45 2.15 0.26 1.52

The PUFA found is probably trienes only. The infrared spectra of the peaks in the last fractions was of poor quality and the cis-trans ratio of these isomers was therefore estimated by visual inspection of the spectra combined with a subjective evaluation of which isomers to be expected in these HPLC-fractions.

The monoenoic distributions are given in Table 6.2-8. Among the cis isomers, the isomers found in largest amounts are the original isomers (16:1n-7, 18:1n-9, 20:1n-6, 22:1n-11), but the amounts of these isomers does not deviate much from the amounts found for isomers with double bonds in adjacent positions. The amounts drop rapidly as the double bond moves towards the end of the carbon chain. A similar pattern is also seen for the trans isomers.

The cis-trans ratio for the monoenes was similar to that found in sample A51 with 75% trans isomers and 25% cis isomers.

A small amount, 0.02% of total fat, was found of 18:2n-6. Else none of the original dienoic and trienoic cis isomers were observed.

Figure 6.2-8)

GC-MS trace, unfractionated fat, sample A51

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AbundanceTIC: S991127O.D

- 119 -

Table 6.2-8) Positional distribution of the monoenes, sample A39. Percent of

total fat Percent Percent of

total fat Percent

Trans 16:1 Cis 16:1 t5-16:1 0.06 1.21 c5-16:1 0.08 4.99t6-16:1 0.15 3.19 c7-16:1 0.22 13.97t7-16:1 0.25 5.38 c8-16:1 0.25 16.35t8-16:1 1.07 22.96 c9-16:1 0.40 25.94t9-16:1 1.33 28.41 c10-16:1 0.28 18.25t10-16:1 1.02 21.84 c11-16:1 0.16 10.05t11-16:1 0.33 7.06 c12-16:1 0.09 5.78t12-16:1 0.29 6.29 c13-16:1 0.04 2.69t13-16:1 0.11 2.28 c14-16:1 0.03 1.99t14-16:1 0.06 1.38

4.68 100.00 1.54 100.00

Trans 18:1 Cis 18:1 t5-18:1 0.04 0.62 c5-18:1 0.06 2.65t6/9-18:1 3.17 45.77 c7-18:1 0.15 6.47t10-18:1 1.17 16.88 c8-18:1 0.36 15.97t11-18:1 0.98 14.14 c9-18:1 0.50 22.25t12-18:1 0.66 9.52 c10-18:1 0.37 16.39t13/14-18:1 0.67 9.65 c11-18:1 0.31 13.86t15-18:1 0.15 2.17 c12-18:1 0.20 8.84t16-18:1 0.09 1.26 c13-18:1 0.13 5.66

c14-18:1 0.09 3.96 c15-18:1 0.05 2.37 c16-18:1 0.04 1.59 6.92 100.00 2.26 100.00

Trans 20:1 Cis 20:1 t5-20:1 0.18 2.90 c5-20:1 0.19 9.15t6-20:1 0.64 10.07 c6/10-20:1 0.71 33.70t7/9-20:1 1.32 20.82 c11-20:1 0.29 13.69t10-20:1 1.06 16.65 c12-20:1 0.22 10.64t11-20:1 0.60 9.37 c13-20:1 0.16 7.75t12-20:1 0.58 9.15 c14-20:1 0.13 6.32t13-20:1 0.47 7.37 c15-20:1 0.12 5.65t14-20:1 0.38 5.96 c16-20:1 0.12 5.54t15/16-20:1 0.74 11.58 c17-20:1 0.09 4.45t17-20:1 0.24 3.74 c18-20:1 0.07 3.11t18-20:1 0.15 2.40

6.36 100.00 2.10 100.00

Trans 22:1 Cis 22:1 t5-22:1 0.25 4.45 c5-22:1 0.17 8.76t7/10-22:1 1.45 25.88 c6/10-22:1 0.41 20.68t11-22:1 1.45 26.04 c11-22:1 0.40 20.35t12-22:1 0.59 10.61 c12-22:1 0.24 11.96t13-22:1 0.39 6.94 c13-22:1 0.16 8.00t14-22:1 0.28 4.99 c14-22:1 0.11 5.78t15-22:1 0.24 4.27 c15-22:1 0.10 4.88t16-22:1 0.22 3.93 c16-22:1 0.09 4.37t17-22:1 0.22 3.92 c17-22:1 0.08 4.10t18-22:1 0.27 4.81 c18-22:1 0.08 4.15t19-22:1 0.14 2.49 c19-22:1 0.09 4.61t20-22:1 0.09 1.68 c20-22:1 0.05 2.36

5.59 100.00 1.97 100.00

- 120 -

Table 6.2-9) Overview of the fractions, Sample A51

Chain length

Sat. Trans mono.

Cis mono.

tt- dienes

ct- dienes

cc- dienes

PUFA Avg trans

(PUFA)

Avg cis

(PUFA) 14 6.61 15 0.70 16 28.11 2.02 0.76 0.05 0.06 17 0.80 0.28 0.01 18 18.43 4.18 1.53 0.16 0.15 0.04 19 0.22 0.01 20 11.27 4.16 1.50 1.01 0.42 0.11 0.41 2.55 0.45 21 0.38 0.17 0.26 0.02 22 7.90 3.84 1.39 0.76 0.63 0.09 0.63 2.21 0.79 24 0.57 0.24 0.12

SUM 75.00 14.90 5.31 2.24 1.28 0.24 1.03

6.2.4) The composition of sample A51 This is the most hydrogenated fat analysed, with as much as 75% saturated fatty acids, 20% monoenes, 3.8% dienes and only traces of trienes. Among the unsaturated isomers, the trans content is considerably higher than the cis content. The GC-MS-trace of an unfractionated sample is given in Figure 6.2-8 and quantified results are summarised in Table 6.2-9.

The amounts and cis/trans ratios reported for the trienes should be considered as very rough estimates only. The last fraction eluting with tetraenes had too low amounts to be analysed by GC-IRD. The cis/trans content in this fraction was set to 1.5/1.5. The fraction contained C20 trienes corresponding to 0.07% of total fat and 16% of total C20 PUFA. The C22 trienes in this fraction represents 0.09% of total fat and 14% of total C22 PUFA.

Regarding the monoenes, 74% of these are trans isomers and 26% are cis isomers. This is a proportion that is close to the thermal equilibrium found between cis and trans isomers when isomerisation was conducted in PTSA and dioxan.

The monoenoic distribution is reported in Table 6.2-10. Contrary to the less hydrogenated fats, the original cis isomers are not present in larger amounts than isomers with double bonds in nearby positions. The level of the positional isomers is evenly distributed both for cis and for trans isomers, with a slight decrease towards the ends of he carbon chain.

6.2.5) Trends in the monoene distribution with increasing hydrogenation Although the quantification of the isomers is inaccurate, some trends are evident. The distribution of the C20 monoenes is shown in Figure 6.2-9 a and b.

In the least hydrogenated fats the cis isomers with the double bonds in the original positions dominate. The low amounts of trans isomers that are present are also centred around these positions.

As hydrogenation proceed both trans isomerisation and double bond migration occur, leading to decreased amounts of cis isomers relative to trans isomers and a large spread in the distribution of positional isomers. The distribution of the positional isomers has its maximum around the centre of the carbon chain and decreases toward both the methyl end and the carboxyl end of the molecule. There is a possible skewing of the distribution toward the carboxyl end. Sebedio et al. (1981) and Sebedio and Ackman (1983a) discuss the same trends.

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Table 6.2-10) Positional distribution of the monoenes. Sample A51

Percent of total fat

Percent Percent of total fat

Percent

Trans 16:1 Cis 16:1 t5-16:1 0.04 2.01 c5-16:1 0.04 5.75t6-16:1 0.09 4.56 c7-16:1 0.09 12.62t7-16:1 0.13 6.30 c8-16:1 0.11 15.08t8-16:1 0.49 24.15 c9-16:1 0.22 29.49t9-16:1 0.53 25.95 c10-16:1 0.12 16.49t10-16:1 0.35 17.27 c11-16:1 0.08 10.74t11-16:1 0.20 9.71 c12-16:1 0.04 5.79

6.13 0.02 2.40t13-16:1 0.05 2.66 c14-16:1 0.01 1.62

1.26 2.02 100.00 0.75 100.00

0.91 c5-18:1 0.05

t6-18:1 0.15 3.66 c7/8-18:1 0.34 22.40t7/9-18:1 1.84 c9-18:1 43.97 0.39 25.74

16.07 c10-18:1 0.21 13.39t11-18:1 0.61 14.60 c11-18:1 0.22 14.08t12-18:1 0.36 8.71 c12-18:1 0.14 8.83t13-18:1 0.18 4.21 c13-18:1 0.09 5.63t14-18:1 0.21 4.96 c14-18:1 0.06 3.84t15-18:1 0.08 1.91 c15-18:1 0.03 1.94t16-18:1 0.04 1.00 c16-18:1 0.02 1.17

4.18 100.00 1.53 100.00

Trans 20:1 Cis 20:1 t5-20:1 0.17 4.00 c5-20:1 0.15 10.05t6-20:1 0.51 12.33 c7/9-20:1 0.34 22.43

0.88 21.08 0.16 10.63t10-20:1 0.50 12.12 c11-20:1 0.20 13.29t11-20:1 0.30 7.26 c12-20:1 0.15 9.87

7.89 0.11 7.29t13-20:1 0.33 7.85 c14-20:1 0.11 7.03t14-20:1 0.30 7.13 c15-20:1 0.10 6.63

0.60 14.44 0.09 6.243.53 c17-20:1 0.06 3.89

t18-20:1 0.10 2.38 c18-20:1 0.04 2.64 4.16 100.00 1.50 100.00

Trans 22:1 Cis 22:1 t6-22:1 0.23 5.96 c5-22:1 0.15 10.47t7-9-22:1 0.83 21.69 c7-9-22:1 0.29 21.13t10/11-22:1 1.09 28.46 c10-22:1 0.19 13.83t12-22:1 0.25 6.60 c11-22:1 0.10 6.89t13-22:1 0.18 4.63 c12-22:1 0.10 7.29t14-22:1 0.19 5.02 c13-22:1 0.10 7.30t15-22:1 0.21 5.49 c14-22:1 0.08 5.98t16-22:1 0.20 5.22 c15-22:1 0.08 5.52t17/18-22:1 0.47 12.21 c16-22:1 0.07 5.28t19-22:1 0.12 3.02 c17-22:1 0.07 5.32t20-22:1 0.07 1.71 c18-22:1 0.07 5.21

c19-22:1 0.05 3.67 c20-22:1 0.03 2.12 3.84 100.00 1.39 100.00

t12-16:1 0.12 c13-16:1

t14-16:1 0.03

Trans 18:1 Cis 18:1 t5-18:1 0.04 2.98

t10-18:1 0.67

t8/9-20:1 c10-20:1

t12-20:1 0.33 c13-20:1

t15/16-20:1 c16-20:1 t17-20:1 0.15

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Figure 6.2-9a)

C20 monoenoic distribution of sample A51

most hydrogenated fat

0.0

0.1

0.2

0.3

0.4

0.5

0.6

4 5 6 7 8 9 10 11 12 13 14 15 16 17 18

transcis

Perc

ent o

f tot

al

Position of double bond (dist. from carbonyl group)

Figure 6.2-9b)

C20 monoenoic distribution of sample C1

0.0

0.5

1.0

1.5

2.0

4 5 6 7 8 9 10 11 12 13 14 15 16 17 18

transcis

least hydrogenated fat

Original 20:1 n-9 retained

Perc

ent o

f tot

al

Position of double bond

6.3) Comparison of the two methods A comparison of calculated sum-variables achieved by the rapid method and the combined method is given in Table 6.3-1. PC-plots of the values are presented in Figure 6.3-1a-d. Both the score plot and the loading plot show similar trends as seen in Figure 6.1-3 where there is a bent arrow indicating the degree of hydrogenation. In this case there is increasing hydrogenation going from B1 to A31'/C2 and further to A39' and A51.

The plot also indicates that there is little difference between the results achieved by the two methods, except for sample B1. For the rest of the fats the difference between the samples seem to be far more important than the difference between the methods. For medium to highly hydrogenated fats, both methods give similar results and the choice of method is therefore of minor importance.

In general, one should be careful not to draw to firm conclusions about the variation within a class based on a PC plot, because such variations are often in different directions in m-space than the main variation (difference between classes) and will therefore not be explained by the first principal components. By including the third PC, which only explains 6% of the total

- 123 -

variation, a small systematic difference between the two methods are indicated. PC3 versus PC1 is shown in Figure 6.3-1 c and d.

The figures in Table 6.3-1 give more detailed information about the difference between the two methods. The area of PUFA (Tot PUFA) is always larger in the combined method than in the rapid method, as much as 2:1 in the case of C1.

Table 6.3-1) Calculated sum- variables (see definitions in Table 6.1-2) from the combined method compared with the rapid method

C1 C2 A31' A39' A51 Com Rap Com Com Com Rap Rap Rap Com Rap

32.1 < 38.9 < 33.1 < 58.4 = 75.0 = Tot mono 37.0 < 44.7 37.2 = 37.0 43.3 = 42.2 32.2 = 32.1 20.2 < 23.3Tot PUFA 31.2 >> 16.5 23.9 > 19.7 23.6 >> 16.2 9.4 > 8.7 4.8 >> 0.8Tr-MUFA 11.7 << 17.4 24.6 < 25.9 27.3 = 27.7 24.1 < 25.7 14.9 < 18.2

Cis-MUFA 25.3 < 11.127.3 12.6 > 16.0 > 14.5 8.1 > 6.4 5.3 > 5.0Tr-PUFA 42.7 >> 22.9 40.8 >> 28.7 37.5 >> 23.0 15.8 >> 10.6 8.2 >> 0.3

51.1 >> 15.8 13.0 10.3 17.1 >> = 4.3 2.4 >> Tot trans 54.4 >> 40.4 65.4 > 54.6 64.8 > 50.7 40.0 > 36.3 23.1 > 18.5Tot cis 76.4 >> 43.1 25.6 > 21.4 33.1 >> 23.6 12.5 5.2> 10.7 7.8 >> Tot dbb 130.8 >> 83.5 97.991.0 > 76.0 > 74.3 52.5 > 47.0 30.9 > 23.7

% trans (tot) 41.6 < 48.3 71.9 = 71.9 66.2 < 68.2 76.2 = 77.3 74.9 = 78.1% trans (MUFA) 31.7 < 39.0 66.2 < 70.1 63.1 = 65.6 74.9 < 80.1 73.7 < 78.4% trans (PUFA) 45.5 < 59.2 >75.9 = 73.6 68.6 = 71.7 78.2 71.2 77.1 > 62.9

IV: analysed 115 79 79 50 32 57.6 37.9 21.1

Sat 38.8 43.3 41.7 59.2 75.9

Cis-PUFA > 9.1 4.4 0.2

Trans (AOCS) = Smaller value 95-100% of larger value < Smaller value 75-95% of larger value << Smaller value less than 75% of larger value

In the variables Tot-PUFA, Tr-PUFA, Cis-PUFA, Tot cis, Tot trans and Tot dbb the variables are always higher in the results from the combined method than in the results from the rapid method, the difference tend to increase with increasing amounts of PUFA. This difference can basically be explained by the way the PUFA peaks are integrated and quantified. The estimate of the Tot-PUFA area has large influence of the other parameters.

In the combined method a highly polar GC-column is used. The complex mixtures of different isomers of dienes and PUFA are distributed over a large retention time interval leading to low and broad peaks. An example is illustrated in Figure 6.3-2. The setting of the baseline for integration is difficult and depend on subjective judgement. There is no chromatographic window where the true baseline (i.e. where only column bleed contributes to the signal) is seen. Small variations of these parameters will lead to large variations in the integrated area because of the large peak width. Because of the possible inclusion of noise in the peak integration, the peak areas of PUFA are probably overestimated, the effect increases with increasing amounts of unsaturation.

The same values are probably underestimated by the rapid method where quantification is based on IRD chromatograms. In tis case there is with increasing underestimation with increasing degree of unsaturation, thus leading to large differences between the two methods for the least hydrogenated fats and less significant differences for the most hydrogenated fats.

- 124 -

Figure 6.3-1a)

Score plot of results obtained by the two different methods. Results from the combined method: green, rapid method: red.

Figure 6.3-1b)

Loading plot corresponding to the above score plot

-0.4

-0.2

0

0.2

0.4

0.6

0.8

-0.4 -0.2 0 0.2 0.4 RESULT3, X-expl: 67%,24%

Sat

Tot MUFA

Tot PUFA

Tr-MUFA

Cis-MUFA

Tr-PUFA

Cis-PUFA

%-trans (MUFA)

%-trans (PUFA)

PC1

PC2 X-loadings

Figure 6.3-1c)

Score plot of results obtained by the two different methods showing PC2 versus PC3.

Results from the combined method: green, rapid method: red.

-2.0

-1.5

-1.0

-0.5

0

0.5

1.0

-3 -2 -1 0 1 2 RESULT3, X-expl: 24%,6%

C-B1

R-B1

C-B2

R-B2

C-A31'

R-A31'

C-A39'

R-A39'

C-A51

R-A51

PC2

PC3 Scores

-3

-2

-1

0

1

2

-6 -4 -2 0 2 4 RESULT3, X-expl: 67%,24%

C-B1

R-B1

C-B2 R-B2C-A31' R-A31'

C-A39'

R-A39'

C-A51

R-A51

PC1

PC2 Scores

- 125 -

Figure 6.3-1d)

Loading plot corresponding to the above score plot

-0.5

0

0.5

-0.4 -0.2 0 0.2 0.4 0.6 0.8 RESULT3, X-expl: 24%,6%

Sat

Tot MUFA

Tot PUFA

Tr-MUFA

Cis-MUFA

Tr-PUFA

Cis-PUFA

%-trans (MUFA) %-trans (PUFA)

PC2

PC3 X-loadings

This effect is explained by decreasing IRD response as the number of double bonds in the isomers increase, thus leading to large underestimation of the amounts of unsaturated fatty acids in the least hydrogenated fats and modest underestimation in the more saturated samples.

This effect is visible by comparing the Tot-PUFA of samples C1 and C2, which is produced from the same raw oil but differ in the degree of hydrogenation. C1, which is the least hydrogenated fat, has lower Tot-PUFA value than C2. An increase in the PUFA content going from C1 to C2 should not be possible.

Figure 6.3-3, showing the C20 and C22 regions of the two fats, illustrates this difference. In C1 the PUFA-peaks are wider than in C2, indicating a higher degree of unsaturation, which leads to increased suppression of the PUFA signal and lower PUFA areas.

In Figure 5.4-14 the chromatographic responses of various cis isomers are shown. The figure shows an average underestimation relative to the amounts of saturated fatty acids of approximately 20% for monoenes, 35% for dienes and 50% for trienes.

Figure 6.3-2)

MS-trace of C20 and C22 PUFA region in sample C1, HPLC fraction number 14

34.00 36.00 38.00 40.00 42.00 44.00 46.00 48.00 50.00 52.00 54.00

C20 PUFA C22 PUFA

min.

Baseline Baseline

It should be emphasised that the closure effect (the fact that the sum of the areas must sum to 100%) will compensate for much of this underestimation. This can be illustrated by the

- 126 -

following example: Given a sample with equal amounts of saturated, monoenes and dienes, 33.3/33.3/33.3 percent. The detector response (assuming 20% underestimation of monoenes and 35% underestimation of dienes) will give values corresponding to: 33.3/26.9/21.6, which sum to 81.8%. Since the sum of the areas shall equal 100% all areas must be corrected by multiplying with (100/81.8) giving the following new percents: 40.7/22.0/26.4, thus the areas of monoenes and dienes are underestimated by only 2.0% and 20.7% respectively and not by 20 and 35%. Since the saturated fatty acids has no double bonds, the 22% overestimation of the saturated fatty acids is of no importance for the calculation of the amounts on trans double bonds. Compensation by the closure effect will increase as the amounts of unsaturated (MUFA and PUFA) fatty acids increase, thus in the highly unsaturated fats where the lower response for the unsaturated isomers are most significant, the compensation for this by the closure effect will be large.

Figure 6.3-3)

IR-trace of C20 and C22 region in samples C1 and C2

0,0

1,0

2,0

3,0

C22 PUFA region

C20 PUFA region

22:0

22:1

20:0

20:1

24:1

24:0

C1 C2

Response factors calculated from reference mixtures may compensate for the lower response of the unsaturated fatty acids. By applying response factors of 1.22 for monoenes and 1.49 for dienes, corresponding to a detector response of 0.82 and 0.67 respectively, the estimation of total trans and total double bonds is increased significantly (other variables are off course also affected, but will for the sake of simplicity be omitted in the following discussion). The results from the methods compared to iodine value and AOCS value are summarised in Table 6.3-2.

After the introduction of response factors the results from the rapid method are more in accordance with the results from the combined method. Compared to the AOCS-trans and the iodine value there is no general trend of improvement; some samples has increased their distance while others are closer to the reference values. In this context it should be emphasised that the reference values may not be the best estimate of the “true” values. The calibration of the AOCS trans value by IR is for instance based only on the use of C18 isomers, while most of the trans double bonds in fish oils are in the C20 and C22 isomers (see discussion in 5.5-3).

The application of the response factors that are calculated on the basis of Figure 5.4-14 is a simplified approach. The response factors are calculated from cis isomers only. Trans isomers will probably have very similar response factors, but this has not been investigated.

- 127 -

The assumption that the response factors for the dienes may be applied on PUFA with a satisfying result is also wrong. In the case of C1 a large portion of the PUFA has more than two double bonds, thus larger response factors must be used on some samples. In these cases the amounts of double bonds, and thus the response factors, must be calculated from the infrared spectra before the chromatographic areas can be corrected.

Table 6.3-2) comparison of the results of the various methods.

Tot. trans Total trans Combined Rapid meth. Rapid with RF* AOCS trans IR

C1 54.4 40.4 47.5 - C2 65.4 56.4 64.5 -

64.8 50.7 59.3 57.6 A39' 40.0 36.3 43.0 37.9 A51 23.1 18.5 27.5 21.1

Tot. dbb. Total dbb Combined Rapid meth. Rapid with RF* Iodine value

C1 130.8 83.5 95.7 115 C2 91.0 76.0 89.5 79

A31' 97.9 74.3 86.3 79 A39' 52.5 47.0 56.0 50

30.9 23.7 27.5 32

A31'

A51 * Corrected with response factors, all PUFA assumed to be dienes

The above procedure to estimate correct response factors is time consuming and complicated. A way to circumvent this problem is to extract the chromatograms from the carbonyl peak (1800-1700 cm-1) in the spectra was used in section 5.5, validation. The chromatographic area will then be less dependent on the double bond contents of the peaks, and the errors may possibly be neglected. Too large loss of unsaturated fatty acids in the injector may still reduce the accuracy, and correction for chain length (which is a simple task) should be applied.

There are also a few other factors that may have influence on the results. An improved spectroscopic background correction procedure has been used in the case of the rapid method, but not in the case of the combined method. Too much noise and drift in spectra may have major impact on the estimation of the values, especially in the estimate of cis values, where the whole spectrum is used in the PLS models.

There is also some doubt whether methylene interrupted polyenes may be used in the calibration models for the prediction of the amount of NMI double bonds without the introduction of errors. This may be a problem for the estimation of cis double bonds and was discussed in 5.4.8.

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7) Summary and concluding remarks A rapid GC-IR method for the analysis of hydrogenated fats has been developed. For each chain length, the method is capable of describing the amount of saturated fatty acids, cis monoenes, trans monoenes, polyenes and the average content of cis and trans double bonds in the polyenes. The method depends on only one single GC-run and may be a versatile tool for documentation, process optimisation and quality control. The method has not been thoroughly validated, detection limits and precision have not been determined, these will probably vary considerably with the type of fat analysed.

The rapid GC-IR method was used to screen a large number of PHFO samples from three different factories. In addition to the degree of hydrogenation, both the raw oil composition and process conditions was found to have significant impact on the hydrogenated products.

By pre-fractionation of the samples on Ag-HPLC and subsequent analysis of the fractions on GC-IR and GC-MS (combined method), more detailed information about the fatty acid composition, especially the monoenoic distribution was achieved. Traces of original diunsaturated isomers were only seen in the least hydrogenated fats. No attempt to achieve complete identification of the other polyunsaturated isomers was done. The number of isomers produced in a hydrogenation process is very large and complete identification of the isomers, if possible, would involve a tremendous amount of work. The usefulness of such detailed information may also be limited. However, this method can still be recommended if the task is to look for certain isomers, e.g. remnants of original isomers. Even if the isomers to be quantified can not always be isolated chromatographically, the differences between the mass spectra seem to be large enough to achieve quantification by regression or curve resolution techniques.

Compared to the number of parameters analysed by the two methods, the number of parameters responsible for the outcome of the products is rather few. Two latent variables were found to account for most of the variation in the raw oil composition. In addition, a few process parameters are varied. All these parameters which are responsible for the composition of the products could be tracked by the rapid GC-IR method. One can assume that fats that are similar in the parameters described by the rapid GC-IR method will also be similar when the detailed information achieved by pre-fractionation on HPLC and subsequent analysis on GC-MS and GC-IR (combined method) is considered. It should therefore be possible to make accurate estimates about the detailed composition of fats based on GC-IR alone, as long as similar fats are previously analysed by the combined method.

The focus in the project has mainly been on method development. The selection of products and raw oils to be analysed was rather arbitrary. More information about the hydrogenation process and the products could possibly been achieved with a proper experimental design.

Regarding the dienes and trienes found in hydrogenated fats, most of these isomers seem to be of the NMI type. Chemically these isomers behave like monoenes with more than one double bond and differ significantly from the natural methylene interrupted isomers. MI isomers have reactive hydrogens between the double bonds, diallene hydrogens, and can for instance protolyse to form conjugated systems. Isolated double bonds can not form such systems. From a nutritional point of view it may be of importance to distinguish between MI and NMI polyenes. The large variation in the mass spectra of PUFA in hydrogenated fats indicates that this can be solved by GC-MS. However more work has to be done on this subject.

Little work has been done on non-hydrogenated oils, where isomerisation is caused by thermal stress. However, possible isomers has been synthesised and analysed on GC-MS and

- 129 -

GC-IR. Both the mass spectra and the retention time characteristics of these isomers proved to be useful for the identification of trans isomers found in unhydrogenated oils. Most trans isomers of common unsaturated fatty acids found in vegetable oils have been characterised. More work remains on the isomers of EPA and DHA, which are abundant in refined fish oils.

- 130 -

8) Appendixes

8.1) Silver ion HPLC fractionation method The method is based on results described in section 5.1.

System description: • Mobile phase gradient pump: Shimadzu LC-9A, Kyoto, Japan

• Column: Chomspher 5 Lipids, 4.6 x 250 mm, Varian- hrompack, Middelburg, The Netherlands

• Detector: Evaporative light scattering detector, DDL-31, Eurosep, Cergy-Pontoise, France

• Fracton collector: Gilson FC 203B, Middleton, WI)

• Injector: Rheodyne manual injector 7125, 100 µl loop, Rheodyne, Berkeley, CA

• Splitt: Approximately 75% to fraction collector, 25% to detector.

• The column is normally kept at room temperature

Solvents • Solvent A: Hexane

• Solvent B: 50% hexane, 50% acetone

• Solvent C: Methanol

Program The program varies slightly with sample and column conditions. The following can be used as a guide:

• Start: 5% solvent B, 95% solvent A, hold for 5 minutes. Increase linearly to 20% solvent B at 30 minutes, increase linearly to 50% solvent B at 45 minutes, increase linearly to 100% solvent B at 50 minutes, hold for 5 minutes.

• Solvent C is used to flush the column before the system i shut down.

• Saturated, trans monoenes and cis monoenes should be completely separated. The fractions with cis monoenes and and all-trans dienes should elute within 15-20 minutes. When analysing samples where trienes can be present, 18:3n-3 should elute 5-10 minutes before the end of the program.

Detector parameters: • Gas pressure: 1 bar

• Temperature: 25°C

• Photomultiplicator: 100V

- 131 -

Fractionation and quantification: • The following guiding rules are used for fraction collection and quantification:

• FAME 15:0 is used as internal standard. 100 mg / 10 ml hexane is dilluted to 1:100 to 1:200 depending on the type of sample to be analysed. 100-3000 µl is added to each fraction.

• One fraction is collected for SFA

• Two fractions are collected for trans MUFA

• Two fractions are collected for cis MUFA

• After this, fractions are collected approximately every third minute.

• The fractions are preconcentrated under nitrogen or dilluted befor GC run. Proper concentrations for GC-analysis and the amount of internal standard depend on the type of fat and must be found by trial and error. In general, 2-3 fractionations of each fat must be done to find the correct concentrations for the GC-analysis.

- 132 -

8.2) Fame derivatisation method The method is based on AOCS Ce 1b-89

Reagents Methanolic NaOH solution (0.5 M) •

•

• Methanolic BF3 (12%)

• Aqous NaCl solution (saturated NaCl solution dilluted to 50% by water)

• Hexane

Procedure • Solid fat samples are heated on heating block and 3 drops of fat are added to 100 x 18 mm

vials with screw cap.

2 ml 0.5 M methanolic NaOH is added and the sample is heated for 10 minutes (gentle stirring after 5 minutes to dissolve droplets of fat.)

• After cooling to room temperature, 2 ml 12% methanolic BF3 is added and the sample is heated for 10 min.

• After cooling to room temperature, 1.5 ml hexane is added and the sample is shaken for approximately 30 sec.

• 4 ml aqous NaCl solution (50% of saturated concentration) is added and after a few minutes the hexane phase are transferred to 2.5 ml sample vials.

• The sample is extracted with another 1 ml hexane, which is transferred to the same vial.

• The hexane extract is dilluted to proper concentration for GC analysis or injected on HPLC for fractionation.

Hexane can be replaced with isooctane without further modifications.

- 133 -

8.3) Picolinyl derivatisation method The method is based on Harvey (1982), acetonitrile was replaced with DCM as solvent and the concentration of thionylchloride was significantly reduced.

Reagents and solvents •

• Nicotinyl alcolhol solution: 1% nicotinyl alcohol in DCM

• The sample is cooled and acidified by addition of 1 ml 2M HCl.

• After 5 minutes in room temperature, thionylchoride / DCM is evaporated under nitrogen.

Saponification reagent: 1M KOH in 95% ethanol, 5% water (5.6 g KOH dissolved in 100 ml)

• Acidification reagent: 2M HCl(aq) : 20 ml 37% HCl, 100 ml water

• Thionyl Chloride solution: 2% thionyl chloride in dichloromethane (DCM)

• Hexane

Procedure

• To 100 µl FAME in hexane, 1 ml saponification reagence is added. Heating at 100°C for 15 minutes.

• The sample is extracted by 2 x 1 ml hexane into 2ml GC vials.

• The hexane is evaporated under a stream of nitrogen. Gentle heating is applied

• Thionyl chloride solution (2 ml) is added.

• Nicotinyl alcohol solution (2 ml) is added.

• After 5 minutes in room temperature, nicotinyl alcohol / DCM is evaporated under nitrogen. Small amounts of nicotinyl alcohol will be left in the vial.

• 1ml isooctane or hexane is added.

• The aliqout may be injected directly on the GC. Dillution or preconcentration may be necessary.

- 134 -

8.4) DMOX derivatisation method The method is based on Fay and Richili (1991)

Reagents and solvents • 2-amino-2-methylpropanol (AMP)

• Methanol

• Hexane

• Distilled water

Preparation • Fame in hexane solution is added to 13 x 100 mm screw cap vials and evaporated to

dryness.

• Approximately 0.5 ml AMP is added and the vial is flushed with nitrogen before capping.

• Heating at 180-190°C for approximately 8 hours.

• AMP is dissolved in 2 ml DCM and washed with 2 x 1 ml water. The DCM phase is evaporated under nitrogen to approximately 500 µl and injected on HPLC for the last cleanup step.

HPLC cleanup step • Solvent A: 20% water in methanol

• Solvent C: methanol

• Solvent B: hexane

Flush with A before next run

• Column: Waters 5µ spherical C18, 4.6 x 100 mm (p/n 85711)

• Column flow: 1 ml/min

• Injection loop 500 µl

• Detector: Eurosep ELSD DDL 31, Cergy-Pontoise, France

• Split: Approx. 10% to detector, 90% to fraction collector (Gilson FC 203B, Middleton, WI)

• Solvent program: Start with 100% A, 0-1 min: 100% B, 1-10 min: 100% C.

• Polar artefacts elute after 1-3 minutes, DMOX derivatives after 5-8 minutes.

• To the collected fraction (two phases) 1 ml water is added to increase polarity and the hexane phase is transferred to GC vials.

• Between each run, the HPLC system uses 15-20 minutes to stabilise.

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8.5) Cis to trans isomerisation by PTSA

• Methanol

•

• NaOH

•

The method is based on Kice et al. (1966), Gibson ans Strassburger (1976) and Snyder & Scholfield (1982)

Reagents • Para-toluenesulfinic acid (PTSA) natrium salt

• 37% HCl

• Distilled water

Dioxane

(aq), 1M

Making of PTSA reagent • Approximately 10g Na+PTSA is dissolved in 50 ml distilled water.

• To the solution 5 ml 37% HCl is added.

• The preciptated acid is separated on a glass filter funnel and is washed several times by ice cold distilled water to remove excess HCl.

• The acid is dried under vacuum or under nitrogen – do not heat!

• PTSA solution for isomeriation is made by dilluting 0.5 g dry PTSA in methanol.

Isomerisation of FAME • FAME, 5 mg in hexane is added to 100 x 13 mm vials with screw caps

• Methanolic PTSA solution (100 µl) is added

• Evaporation to dryness under a gentle stream of nitrogen – no heating applied.

• Dioxane, 1 ml, is added

The sample is heated at 60°C for 1 to 4 hours, the time depending on the degree of isomerisation wanted.

• Add 1 ml 1 M NaOH(aq)

• Extract with 1 ml isooctane

• The isooctane phase (100 µl) is injected onto HPLC for separation by the number of trans double bonds, fractions are collected. HPLC procedure as described in appendix 8.1.

- 136 -

8.6) Column parameters applied in section 5.2 100% methyl Manufacturer unknown Phase: 100 % methyl substituted polysiloxane Column length 25m Column diameter 0.20mm Phase thickness 0.17µm

Flow settings

0.25mm

Temperature program

Flow settings 6.6psi, estimated to give 26cm/sec flow, const. Flow mode Temperature program Injection at 60°C, hold for 4min, 30°C/min to 160°C, 2°C/min

to 250°C. DB-5 J&W, Folsom, CA Phase: 5% phenyl, 95% methyl substituted polysiloxane Column length 30 Column diameter 0.25mm Phase thickness 0.25µm

1.7psi, estimated to give 26cm/sec flow, const. Flow mode Temperature program Injection at 60°C, hold for 4min, 30°C/min to 160°C, 2°C/min

to 260°C. CP-Sil 13 Chrompack, Middelburg, The Netherlands Phase: 14% difenyl, 86% diphenyl substituted polysiloxane Column length 50 Column diameter Phase thickness 0.20µm Flow settings 12.6psi, estimated to give 26cm/sec flow, const. Flow mode

Injection at 60°C, hold for 4min, 30°C/min to 160°C, 2°C/min to 260°C.

DB-1701 J&W, Folsom, CA Phase: 14% cyanopropylfenyl, 86% dimethyl substituted polysiloxane Column length 60 Column diameter 0.20mm Phase thickness 0.25µm Flow settings 35.9psi, estimated to give 22cm/sec flow, const. Flow mode Temperature program Injection at 60°C, hold for 4min, 30°C/min to 175°C, 1.5°C/min

to 250°C, hold for 10min.

- 137 -

HP-Innowax Hewlett-Packard, Wilmington, DE Phase: 100% polyethylene glycol (PEG) Column length 60 Column diameter 0.20mm Phase thickness -

Column length

18.1psi, estimated to give 26cm/sec flow, const. Flow mode

100

29.4psi, estimated to give 21cm/sec flow, const. Flow mode

Flow settings 35.9psi, estimated to give 22cm/sec flow, const. Flow mode Temperature program Injection at 60°C, hold for 4min, 30°C/min to 160°C, 2°C/min

to 250°C, hold for 10min. BPX-70 SGE, Ringwood, Australia Phase: Cyanopropyl Polysilphenylene-siloxane

60 Column diameter 0.25mm Phase thickness 25µm Flow settings Temperature program Injection at 60°C, hold for 4min, 30°C/min to 160°C, 2°C/min

to 250°C SP-2560 Supelco, Bellefonte, PA Phase: 100% biscyanopropyl polysiloxane Column length Column diameter 0.25mm Phase thickness 20µm Flow settings Temperature program Injection at 60°C, hold for 4min, 30°C/min to 175°C, 1.5°C/min

to 250°C, hold for 5 min.

- 138 -

8.7) GC-IR instrument parameters

• Syringe: 5 µl

• Liner: 900 µl deactivated, single tapered with glass wool insert, Agilent p/n 5062-3587

GC parameters

Detector Parameters:

•

• Gas chromatograph: HP-5890 Series II, equipped with automatic liquid sampler and split/splitless injector with electronic pressure control, Agilent technologies

• Detector: HP-IRD, Bio-Rad,

• Carrier gas: Helium, 99.996%

• Detector purge gas: Nitrogen, 99.99%

• Column: CP Wax-52cb, 30 m x 0.32 mm, 0.25 µm phase thickness, Chrompack p/n 8843.

• Injector temperature: 260°C

• Injection volume: 1 µl (may be varied)

• Injector pressure program: Injection at 40 psi, hold for 1 minute. Linear gradient 40 psi/min to 7.6 min. The pressure was thereafter increased with oven temperature to give constant flow of 1.4 ml/min (26 cm/sec).

• Oven temperature program: Injection at 60°C, hold for 2 minutes. Increase linearly to 180°C at 40°C/min. Increase linearly to 250°C at 10°C/min. Hold for 3 minutes. Total run time: 15 minutes.

• IRD lightpipe temperature: 250°C

IRD transfer line temperatures: 250°C

• IRD optical resolution: 8 cm

• IRD Coadd factor: 4

• IRD Inst. Purge: 30-40 psi nitrogen

• IRD Purge A: 2,5 psi nitrogen

• IRD Purge B: 3,0 psi nitrogen

-1

- 139 -

8.8) GC-MS instrument parameters, method applied in section 6.2

• Gas chromatograph: HP-5890 Series II, equipped with automatic liquid sampler and split/splitless injector with electronic pressure control, Agilent technologies

• Detector: HP 5972 MSD

• Syringe: 5 µl

• Liner: 900 µl deactivated, single tapered with glass woll insert, Agilent p/n 5062-3587

• Carrier gas: Helium, 99.996%

• Column: BPX-70, 60 m x 0.25 mm, 0.25 µm phase thickness, GSE, Ringwood, Australia, p/n 054623

• Injector temperature: 250°C

• Injection volume: 1 µl (may be varied)

• Injector pressure program: Injection at 10.5 psi, at 60°C, Constant flow mode (column head pressure is increased with temperature to give constant flow of approximately 0.6 ml/min)

• Oven temperature program: Injection at 60°C, hold for 4 minutes. Increase lineary to 160°C at 30°C/min. Increase linearly to 210°C at 1°C/min. Total run time: 57.33 minutes.

Detector Parameters:

• Tune: Autotune program in MS chemstation G1034C, version C.03.00

• MSD interface temperature: 250°C (long interface)

• Mass scan from m/z 50 to 400, signal treshold: 4

• Solvent delay: 9 minutes

GC parameters

- 140 -

8.9) GC-MS instrument parameters, section 5.7

• Gas chromatograph: HP-5890 Series II, equipped with automatic liquid sampler and split/splitless injector with electronic pressure control, Agilent technologies

• Column I: BPX-70, 60 m x 0.25 mm, 0.25 µm phase thickness, GSE, Ringwood, Australia, p/n 054623

• Injector temperature: 250°C

The parameters A, B and C are varied, high, low and medium levels are chosen, see Table 8.8-1 below.

• Detector: HP 5972 MSD

• Syringe: 5 µl

• Carrier gas: Helium, 99.996%

• Column II: SP-2560, 100 m x 0.25 mm, 0.20 µm phase thickness, Supelco, p/n 24056

GC parameters BPX-70

• Injection volume: 1 µl (may be varied)

• Injector pressure program: Injection at C psi, at 60°C, Constant flow mode (column head pressure is increased with temperature to give constant flow)

• Oven temperature program: Injection at 60°C, hold for 4 minutes. Increase lineary to A°C at 30°C/min. with B°C/min to last fatty acid has eluted. Total run time: varies

Table 8.8-1 High, low and medium values chosen for BPX-70

A: Start temp. (°C) B: Temp. gradient (°C/min)

C: pressure / est flow (psi / cm/sec)

Low 160 7.99 / 18

Medium 175 3 13.0 / 22

High 190 4 18.1 / 26

• Injector temperature: 250°C

• Injection volume: 1 µl (may be varied)

• Injector pressure program: Injection at C psi, at 60°C, Constant flow mode (column head pressure is increased with temperature to give constant flow)

• Oven temperature program: Injection at 60°C, hold for 4 minutes. Increase lineary to A°C at 30°C/min. with B°C/min to last fatty acid has eluted, hold for 5 minutes. Total run time: varies.

• Liner: 900 µl deactivated, single tapered with glass woll insert, Agilent p/n 5062-3587

2

GC parameters SP-2560

- 141 -

The parameters A, B and C are varied, high, low and medium levels are chosen, see Table 8.8-2 below. Table 8.8-2 High, low and medium values chosen for SP-2560

A: Start temp. (°C) B: Temp. gradient (°C/min)

C: pressure / est flow (psi / cm/sec)

Low 1.0 23.1/ 18

Medium 175 1.5 29.4 / 21

190 2.0 35.7 / 24

• MSD interface temperature: 250°C (long interface)

• Mass scan from m/z 50 to 400, signal treshold: 4

• Solvent delay: 9 minutes

160

High

Detector Parameters:

• Tune: Autotune program in MS chemstation G1034C, version C.03.00

- 142 -

8.10) List of ECL values of analysed isomers on BPX-70

Analytical conditions are described in appendix 8.9

160-2-26 160-4-18 175-3-22 190-4-18

14:0 14,05 14,01 14,04 14,05 14,02

14:1n-5 c 14,54 14,57 14,58 14,60 14,61

14:1n-5 t 14,34 14,34 14,35 14,37 14,36

16:1n-7 t

16,97

18:0

18:2n-6 ct 18,96

18,71

19,84

18:3n-3 cct

19,64 19,71 19,74 19,79 19,84

19,68 19,74 19,76 19,82 19,85

18:3n-3 ttc

19,15

18:3n-6 ccc 19,61

19,35 19,37 19,45

18:3n-6 ctc 19,39 19,45 19,48 19,53 19,56

19,20 19,23 19,26 19,30 19,31

19,57

19,36

19,28

15:0 14,96 14,98 14,98 14,99

16:0 15,95 15,98 15,96 15,95

16:1n-7 c 16,39 16,45 16,44 16,44 16,49

16,21 16,27 16,26 16,24 16,28

17:0 16,99 16,99 16,99

17:1n-7 c 17,43 17,47 17,48 17,52

18,03 18,01 18,03 18,01 18,01

18:1n-9 c 18,39 18,41 18,43 18,44 18,46

18:1n-9 t 18,24 18,26 18,25 18,27

18:2n-6 cc 19,01 19,06 19,12 19,15

18,86 18,88 18,91 18,94

18:2n-6 tc 18,94 18,96 18,99 19,02 19,04

18,66 18,67 18,69 18,71

18:3n-3 ccc 19,75 19,86 19,92 19,97

19,48 19,54 19,56 19,64

18:3n-3 ctc

18:3n-3 ctt 19,36 19,39 19,42 19,46 19,47

18:3n-3 tcc

18:3n-3 tct 19,41 19,45 19,52 19,53

19,48 19,51 19,54 19,58

18:3n-3 ttt 19,15 19,18 19,21 19,21

19,40 19,48 19,51 19,56

18:3n-6 cct 19,29 19,42

18:3n-6 ctt

18:3n-6 tcc 19,43 19,49 19,52 19,61

18:3n-6 tct 19,27 19,30 19,34

18:3n-6 ttc 19,29 19,32 19,38 19,39

18:3n-6 ttt 19,00 19,00 19,03 19,05

19:1n-9 t 19,26 19,25 19,30 19,29

19,99 20,05 20,07 20,12 20,15

19:2n-6 ct 19,84 19,91 19,95 19,96

190-2-26

14,97

15,98

17,00

17,47

18,24

19,09

18:2n-6 tt

19,61

19,47

19,59

19,24

19,34

19,05

19:2n-6 cc

19,88

- 143 -

19:2n-6 tc 20,04

19,67 19,67 19,70 19,74 19,72

20,07

20:2n-6 cc 20,96

20,96

20,70

21,85 21,81

19,94 19,98 19,99 20,05

19:2n-6 tt

20:0 20,04 20,01 20,04 20,03

20:1n-9 c 20,37 20,40 20,42 20,46 20,46

20:2ct 20,80 20,87 20,87 20,91 20,94

21,06 21,05 21,09 21,14

20:2n-6 tc 20,89 20,96 21,00 21,02

20:2n-6 tt 20,63 20,65 20,67 20,71

20:3n-3 ccc 21,68 21,80 21,93

20:3n-3 cct 21,40 21,53 21,50 21,52 21,60

20:3n-3 ctc 21,56 21,71 21,67 21,68 21,79

20:3n-3 ctt 21,28 21,37 21,36 21,38 21,43

20:3n-3 tcc 21,60 21,75 21,71 21,72 21,81

20:3n-3 tct 21,34 21,44 21,42 21,44 21,50

20:3n-3 ttc 21,40 21,50 21,48 21,49 21,56

20:3n-3 ttt 21,08 21,13 21,13 21,15 21,17

20:3n-6 ccc 21,33 21,49 21,46 21,49 21,59

20:3n-6 ccc 21,31 21,48 21,45 21,47 21,58

20:3n-6 cct 21,18 21,31 21,29 21,32 21,40

20:3n-6 ctc 21,31 21,45 21,43 21,45 21,55

20:3n-6 ctt 21,09 21,18 21,18 21,20 21,27

20:3n-6 tcc 21,31 21,45 21,43 21,45 21,55

20:3n-6 tct 21,15 21,26 21,24 21,28 21,34

20:3n-6 ttc 21,17 21,26 21,24 21,28 21,34

20:3n-6 ttt 20,90 20,95 20,95 20,98 21,01

20:4n-6 cccc 21,58 21,79 21,74 21,75 21,93

20:4n-6 ccct1 21,45 21,63 21,59 21,61 21,73

20:4n-6 ccct2 21,67 21,86 21,81 21,82 21,97

20:4n-6 ccct3 21,70 21,89 21,85 21,85 21,99

20:4n-6 cctt1 21,41 21,56 21,53 21,54 21,64

20:4n-6 cctt2 21,55 21,71 21,67 21,68 21,79

20:4n-6 cctt3 21,62 21,77 21,73 21,74 21,85

20:4n-6 cctt4 21,69 21,87 21,82 21,82 21,94

20:4n-6 cttt1 21,32 21,44 21,41 21,44 21,51

20:4n-6 cttt2 21,47 21,60 21,57 21,59 21,66

20:4n-6 tttt 21,21 21,29 21,28 21,30 21,34

20:5n-3 22,33 22,62 22,53 22,49 22,72

22:0 21,95 21,98 21,96 21,95 21,97

22:1n-9 c 22,28 22,39 22,35 22,31 22,41

22:1n-9 t 22,14 22,22 22,18 22,16 22,22

- 144 -

22:2n-6 c 22,91 23,08 23,02 22,95 23,10

22:2n-6 ct 22,73 22,87 22,82 22,76 22,88

22:2n-6 tc 22,83 22,97 22,91 22,86 22,98

22:2n-6 tt 22,54 22,64 22,59 22,55 22,64

22:3n-3 ccc 23,71 23,90 23,84 23,80 23,93

22:3n-3 cct 23,39 23,56 23,50

23,75 23,94 23,89 23,85 23,97

23,43 23,57

22:3n-3 ctc 23,58 23,75 23,69 23,64 23,76

22:3n-3 ctt 23,24 23,38 23,32 23,26 23,38

22:3n-3 tcc 23,63 23,80 23,75 23,70 23,81

22:3n-3 tct 23,32 23,46 23,41 23,35 23,46

22:3n-3 ttc 23,39 23,53 23,47 23,41 23,52

22:3n-3 ttt 23,02 23,12 23,07 23,01 23,11

22:4n-6 cccc 23,71 23,95 23,88 23,84 23,96

22:4n-6 ccct1 23,54 23,76 23,69 23,63 23,80

22:4n-6 ccct2 23,74 23,96 23,90 23,85 24,00

22:4n-6 ccct3 23,79 24,01 23,95 23,91 24,05

22:4n-6 ccct4 23,84 24,05 24,00 23,96 24,09

22:4n-6 cct1 23,48 23,67 23,61 23,56 23,70

22:4n-6 cct2 23,58 23,77 23,71 23,66 23,80

22:4n-6 cct3 23,65 23,84 23,78 23,74 23,86

22:4n-6 cct4

22:4n-6 cttt1 23,39 23,55 23,49 23,43 23,56

22:4n-6 cttt2 23,49 23,65 23,60 23,55 23,66

22:4n-6 tttt 23,18 23,31 23,25 23,19 23,30

22:5n-3 24,63 24,87 24,84 24,95 24,93

22:6n-3 24,91 25,15 25,15 25,35 25,25

24:0 24,01 24,01 24,01 24,01 24,00

24:1n-9 c 24,44 24,46 24,49 24,55 24,45

24:1n-9 t 24,24 24,26 24,27 24,30 24,23

- 145 -

8.11) List of ECL values of analysed isomers on SP-2560

Analytical conditions are described in appendix 8.9

160-1,0-24 160-2,0-18 175-1,5-21 190-1,0-24 190-2,0-18

14:0 14,06 14,03 14,05 14,07 14,04

14:1n-5 c 14,83 14,87 14,88 14,92 14,97

14:1n-5 t 14,57 14,59 14,61 14,63 14,66

15:0 14,96 14,97 14,96 14,97 14,97

19,39 19,44

16:0 15,94 15,97 15,95 15,93 15,97

16:1n-7 c 16,68 16,74 16,74 16,76 16,81

16:1n-7 t 16,45 16,50 16,50 16,49 16,55

17:0 16,98 17,00 16,99 16,96 16,97

17:1n-7 c 17,71 17,77 17,78 17,80 17,84

18:0 18,03 18,02 18,02 18,01 18,02

18:1n-9 c 18,65 18,68 18,72 18,75 18,79

18:1n-9 t 18,47 18,47 18,50 18,52 18,55

18:2n-6 cc 19,58 19,64 19,69 19,78 19,83

18:2n-6 ct 19,48 19,54 19,59

18:2n-6 tc 19,47 19,52 19,56 19,63 19,68

18:2n-6 tt 19,19 19,21 19,25 19,30 19,32

18:3n-3 ccc 20,60 20,74 20,77 20,86 20,97

18:3n-3 cct 20,30 20,41 20,43 20,52 20,59

18:3n-3 ctc 20,46 20,59 20,61 20,70 20,78

18:3n-3 ctt 20,21 20,28 20,32 20,39 20,45

18:3n-3 tcc 20,50 20,62 20,64 20,73 20,80

18:3n-3 tct 20,21 20,28 20,32 20,39 20,45

18:3n-3 ttc 20,30 20,40 20,43 20,50 20,55

18:3n-3 ttt 20,01 20,02 20,07 20,11 20,13

18:3n-6 ccc 20,23 20,35 20,39 20,49 20,59

18:3n-6 cct 20,09 20,19 20,22 20,31 20,38

18:3n-6 ctc 20,23 20,35 20,38 20,48 20,55

18:3n-6 ctt 20,01 20,08 20,13 20,20 20,26

18:3n-6 tcc 20,21 20,31 20,35 20,44 20,51

18:3n-6 tct 20,01 20,08 20,13 20,20 20,26

18:3n-6 ttc 20,10 20,18 20,21 20,30 20,35

18:3n-6 ttt 19,82 19,86 19,90 19,96 20,00

19:1n-9 t 19,47 19,47 19,51 19,55 19,57

19:2n-6 cc 20,50 20,61 20,64 20,72 20,78

19:2n-6 ct 20,34 20,40 20,43 20,50 20,55

- 146 -

19:2n-6 tc 20,43 20,50 20,53 20,60 20,65

19:2n-6 tt 20,15 20,18 20,22 20,28 20,30

20:0 20,05 20,02

21,47

21,25

22,55 22,52 22,59

20:3n-3 cct

21,97

22,60

22,74 22,71 22,84

22,62 22,84

22,44 22,43 22,54

20:4n-6 cttt1b

22,38

23,95 24,01 23,99 23,75

22:0 21,95

22,45 22,67

20,05 20,09 20,06

20:1n-9 c 20,60 20,64 20,67 20,73 20,76

20:2n-6 cc 21,45 21,58 21,57 21,62 21,72

20:2n-6 ct 21,26 21,36 21,36 21,40

20:2n-6 tc 21,35 21,46 21,45 21,49 21,57

20:2n-6 tt 21,09 21,15 21,15 21,21

20:3n-3 ccc 22,45 22,58

22,13 22,32 22,28 22,27 22,40

20:3n-3 ctc 22,31 22,51 22,46 22,45 22,57

20:3n-3 ctt 22,02 22,16 22,13 22,13 22,24

20:3n-3 tcc 22,35 22,55 22,51 22,49 22,61

20:3n-3 tct 22,05 22,21 22,17 22,17 22,28

20:3n-3 ttc 22,14 22,30 22,26 22,25 22,36

20:3n-3 ttt 21,83 21,93 21,91 21,93 22,00

20:3n-6 ccc 22,06 22,28 22,24 22,25 22,40

20:3n-6 cct 21,89 22,07 22,04 22,07 22,17

20:3n-6 ctc 22,02 22,21 22,18 22,19 22,30

20:3n-6 ctt 21,80 21,94 21,97 22,06

20:3n-6 tcc 22,02 22,24 22,20 22,22 22,33

20:3n-6 tct 21,82 21,98 21,96 21,98 22,06

20:3n-6 ttc 21,89 22,05 22,02 22,05 22,13

20:3n-6 ttt 21,61 21,73 21,73 21,75 21,81

20:4n-6 cccc 22,55 22,82 22,78 22,75 22,92

20:4n-6 ccct1 22,40 22,61 22,58 22,55 22,69

20:4n-6 ccct2 22,65 22,88 22,85 22,80 22,94

20:4n-6 ccct3 22,70 22,92 22,89 22,83 22,98

20:4n-6 cctt1 22,37 22,58 22,54 22,52 22,65

20:4n-6 cctt2a 22,46 22,67 22,62 22,74

20:4n-6 cctt2b 22,47 22,69 22,65 22,62 22,76

20:4n-6 cctt3 22,57 22,78

20:4n-6 cctt4 22,79 22,76 22,89

20:4n-6 cttt1a 22,31 22,49

22,39 22,58 22,53 22,51 22,62

20:4n-6 cttt2 22,47 22,66 22,60 22,58 22,69

20:4n-6 tttt 22,18 22,31 22,28 22,27

20:5n-3 23,71

21,95 21,98 21,96 21,95

22:1n-9 c 22,63 22,61 22,75

- 147 -

22:1n-9 t 22,31 22,38

22:2n-6 cc 23,39 23,53

23,27 23,21 23,24

22:2n-6 tc

22:3n-3 ctc

24,75

23,81 23,87

24,59

24,84 24,99 24,56

24,66 24,73

24,94 25,14 25,39 24,79

22:4n-6 cctt4 24,82

24,19 24,34 24,18

26,97 28,30 26,27

24,01 24,00

22,35 22,33 22,38

23,53 23,47 23,48

22:2n-6 ct 23,16 23,29

23,28 23,40 23,39 23,32 23,33

22:2n-6 tt 22,97 23,07 23,05 22,98 23,02

22:3n-3 ccc 24,58 24,68 24,80 24,95 24,54

22:3n-3 cct 24,30 24,28 24,35 24,37 24,18

24,41 24,48 24,59 24,67 24,35

22:3n-3 ctt 24,04 24,11 24,17 24,15 23,98

22:3n-3 tcc 24,48 24,54 24,66 24,39

22:3n-3 tct 24,10 24,17 24,23 24,23 24,03

22:3n-3 ttc 24,21 24,26 24,34 24,35 24,12

22:3n-3 ttt 23,90 23,86 23,74

22:4n-6 cccc 24,89 24,98 25,19 25,44 24,88

22:4n-6 ccct1 24,66 24,73 24,89 25,02

22:4n-6 ccct2 24,91 24,94 25,16 25,36 24,79

22:4n-6 ccct3 25,01 25,03 25,27 25,52 24,88

22:4n-6 ccct3 25,06 25,08 25,33 25,60 24,92

22:4n-6 cctt1a 24,63 24,70

22:4n-6 cctt1b 24,87 25,03 24,58

22:4n-6 cctt2a 24,77 24,83 25,00 25,20 24,70

22:4n-6 cctt2b 24,83 24,86 25,04 25,25 24,70

22:4n-6 cctt3 24,91

24,96 24,99 25,19 25,46

22:4n-6 cttt1 24,60 24,64 24,77 24,90 24,47

22:4n-6 cttt2 24,68 24,70 24,85 25,00 24,53

22:4n-6 tttt 24,43 24,47

22:5n-3 26,50 26,34

22:6n-3 27,31 26,96 27,89 30,06 26,98

24:0 24,01 24,00 24,01

24:1n-9 c 24,67 24,61 24,76 24,89 24,40

24:1n-9 t 24,45 24,38 24,50 24,56 24,18

- 148 -

8.12) PHFO tables, section 6.1 Tables referred to in section 6.2:

Table 1, Products from factory A, by assumed melting point

Table 2, Products from factory A, mixtures

Table 3, Products from factory B and C

Table 4, Products with assumed melting point 31°C, factory A

Table 5, Products with assumed melting point 35°C, factory A

Table 6, Products with assumed melting point 39°C, factory A

Table 7, Raw oil composition

- 149 -

Table 8.12.1) PHFO Products from factory A, sorted by melting points A31 A26

n=2 A29 n=17 n=9

A33 n=1

A33b n=3

A35 n=3

A37 n=1

A39 n=11

A41 (05-97)

n=1

A43 (s-97) n=3

A45 (06-97)

n=1

A51 n=1

12:0 0.12 0.00 0.18 0.01 0.03 0.08 0.15 0.00 - 0.05 0.09 0.00 0.00 - 0.06 0.10 0.00 - 0.00 0.00 0.00 - 0.11 - 14:0 8.28 0.25 8.39 0.54 9.42 1.37 8.76 - 8.87 0.24 8.49 0.74 8.61 - 8.84 1.12 8.08 - 7.51 0.34 7.29 - 7.70 - 15:0 0.33 0.12 0.50 0.10 0.52 0.08 0.38 - 0.38 0.11 0.44 0.04 0.32 - 0.47 0.12 0.46 - 0.51 0.09 0.43 - 0.35 - 16:0 14.67 0.99 20.32 1.42 20.82 2.32 23.46 - 20.54 1.43 21.59 0.41 19.88 - 22.78 1.93 22.57 - 22.10 0.76 22.31 - 28.18 - 16:1t 2.68 0.73 6.30 1.48 7.62 1.42 8.04 - 6.65 0.48 8.43 0.77 6.85 - 7.33 1.77 7.06 - 5.17 0.31 5.71 - 2.35 -

7.66 0.40 4.14 1.16 3.91 1.12 4.32 - 3.40 0.56 3.21 0.63 3.81 - 2.61 0.74 3.18 - 1.54 0.21 1.15 - 1.41 - 16-pu 1.32 0.33 2.45 1.75 1.85 0.34 2.27 - 1.57 0.20 2.53 0.44 1.21 - 1.69 0.35 2.02 - 1.32 0.15 1.48 - 0.71 -

16-pu trans 0.65 0.23 0.69 0.29 0.95 0.39 0.91 - 0.67 0.23 0.91 0.07 0.21 - 0.54 0.19 0.89 - 0.51 0.69 0.43 - 0.00 - 16-pu cis 0.47 0.04 0.38 0.19 0.47 0.16 0.42 - 0.26 0.08 0.42 0.15 0.13 - 0.21 0.11 0.37 - 0.26 0.09 0.28 - 0.21 -

18:0 1.59 0.34 5.04 0.70 5.42 0.85 6.64 - 5.62 0.33 7.51 0.83 9.28 - 8.89 1.21 9.17 - 11.65 0.37 14.13 - 18.35 - 18:1t 6.19 0.33 10.02 1.91 9.68 1.59 10.16 - 11.53 0.41 10.91 1.32 10.76 - 10.51 0.95 10.81 - 9.34 0.56 8.19 - 5.22 - 18:1c 13.08 0.28 7.93 1.72 5.26 0.65 7.65 - 6.17 0.86 4.30 0.52 4.11 - 3.82 0.53 3.36 - 2.84 0.34 1.96 - 1.54 - 18-pu 1.06 0.04 2.12 3.19 2.89 0.36 0.71 - 0.93 0.35 1.07 0.31 0.65 - 0.76 0.20 0.85 - 0.57 0.05 0.46 - 0.10 -

18-pu trans 0.99 0.03 1.11 0.32 1.24 0.20 0.98 - 1.08 0.12 1.40 0.13 0.34 - 0.96 0.30 0.70 - 0.85 0.22 0.74 - 2.57 - 18-pu cis 1.19 0.22 0.73 0.18 0.73 0.19 0.97 - 0.77 0.15 0.43 0.29 0.43 - 0.54 0.26 0.00 - 0.43 0.21 0.00 - 0.00 -

20:0 0.22 0.26 0.60 0.48 0.78 0.49 0.00 - 1.03 0.06 1.15 1.10 0.13 - 2.81 1.06 3.73 - 5.82 0.89 6.31 - 13.42 - 20:1t 4.55 0.59 5.82 1.31 5.52 1.58 5.41 - 7.14 0.28 6.78 0.24 10.29 - 7.15 1.11 6.55 - 7.82 0.96 7.86 - 5.09 - 20:1c 12.52 0.41 3.37 0.73 2.64 1.01 3.10 - 3.61 0.13 2.19 0.38 2.77 - 2.03 0.33 2.31 - 1.76 0.23 1.65 - 0.00 - 20-pu 3.40 0.53 7.78 0.78 8.66 1.39 7.83 - 6.89 0.55 8.29 0.53 5.68 - 6.18 1.02 5.55 - 4.46 0.34 3.79 - 0.00 -

20-pu trans 1.18 0.26 1.45 0.21 1.42 0.07 1.58 - 1.41 0.20 1.54 0.01 0.82 - 1.41 0.11 1.34 - 1.25 0.22 1.40 - 0.00 - 20-pu cis 1.09 0.28 0.63 0.10 0.62 0.12 0.55 - 0.57 0.03 0.55 0.09 0.12 - 0.52 0.09 0.62 - 0.47 0.10 0.52 - 0.00 -

21:0 0.00 0.00 0.52 2.16 0.82 0.00 0.00 - 0.00 0.00 0.00 0.00 0.00 - 0.33 1.09 0.00 - 0.00 0.00 0.00 - 0.30 - 22:0 0.59 0.78 1.14 0.65 1.44 0.51 0.67 - 1.11 0.62 1.36 0.83 2.23 - 1.93 0.93 2.58 - 4.95 0.55 5.16 - 7.17 - 22:1t 3.65 0.65 3.62 1.54 4.20 2.17 2.99 - 5.88 0.64 3.76 0.50 6.62 - 4.96 2.12 4.77 - 6.29 0.50 6.47 - 5.57 - 22:1c 14.92 0.44 3.19 1.35 2.23 1.12 1.11 - 3.31 0.47 1.27 0.61 2.20 - 1.36 0.69 1.48 - 1.50 0.15 1.01 - 2.05 - 22-pu 2.44 0.28 5.99 1.05 5.68 0.85 6.33 - 5.01 0.37 6.33 1.42 4.35 - 5.13 0.55 4.97 - 4.27 0.25 4.08 - 0.00 -

22-pu trans 0.98 0.27 1.44 0.24 1.59 0.06 1.53 - 1.55 0.13 1.59 0.02 1.36 - 1.57 0.14 1.51 - 1.32 0.12 1.77 - 0.00 - 22-pu cis 0.78 0.16 0.82 0.88 0.59 0.13 0.48 - 0.76 0.17 0.46 0.03 0.55 - 0.43 0.11 0.61 - 0.44 0.01 0.19 - 0.00 -

24:0 0.27 0.01 0.09 0.12 0.09 0.14 0.18 - 0.19 0.23 0.00 0.00 0.17 - 0.09 0.13 0.37 - 0.35 0.14 0.00 - 0.36 - 24:1t 0.14 0.13 0.97 2.87 0.63 0.14 0.14 - 0.15 0.17 0.36 0.10 0.09 - 0.22 0.17 0.12 - 0.19 0.02 0.39 - 0.00 - 24:1c 0.31 0.19 0.49 1.34 0.33 0.16 0.00 - 0.04 0.04 0.23 0.19 0.00 - 0.13 0.15 0.00 - 0.03 0.03 0.25 - 0.03 -

16:1c

- 150 -

A26 A33b n=1

A51 n=2

A29 n=17

A31 n=9

A33 n=1 n=3

A35 n=3

A37 A39 n=11

A41 (05-97)

n=1

A43 (s-97) n=3

A45 (06-97)

n=1 n=1

Sat 26.08 1.39 35.82 1.42 38.92 3.99 2.00 40.34 46.13 2.26 39.93 - 37.74 3.17 40.62 - 2.36 46.95 - 52.89 55.54 - 75.93 -

Tot mono 65.70 2.56 45.84 2.28 34.65 4.50 42.01 6.10 42.92 - 47.86 0.75 41.44 2.35 47.49 - 40.12 2.86 39.66 - 36.48 - 23.26 -

Tot PUFA 8.22 1.17 18.33 4.88 19.07 1.47 11.89 13.39 2.26 17.15 - 14.41 18.22 1.48 - 13.75 1.84 - 10.63 0.20 9.81 - 0.81 -

Tot dbb 81.35 1.66 81.66 6.84 76.30 62.03 78.81 2.19 76.67 - 3.81 77.16 4.09 - 64.77 4.54 64.19 - 53.49 3.58 51.32 - 23.66 -

Tr-mono 17.21 1.78 26.72 2.86 27.65 4.14 26.74 - 31.34 0.77 30.24 1.54 34.60 - 30.17 2.39 29.32 - 28.81 2.20 28.62 - 18.22 -

Cis-mono 48.49 0.78 19.12 2.51 14.37 2.21 16.18 - 16.52 0.10 11.20 1.00 12.89 - 9.96 0.75 10.34 - 7.68 0.43 6.03 - 5.04 -

Tr-pufa 8.22 0.66 23.44 3.05 25.72 3.56 24.81 - 19.66 3.25 26.66 2.26 11.04 - 18.39 2.80 17.30 - 12.45 1.88 13.49 - 0.25 -

Cis-pufa 7.43 2.53 1.640.24 12.37 6.32 11.07 8.94 - 8.78 0.53 9.06 0.65 3.50 - 6.26 7.23 - 4.56 0.51 3.18 - 0.15 -

Tot trans 25.44 1.12 50.17 3.56 53.37 1.75 51.55 - 51.00 3.42 56.89 3.27 45.64 - 48.55 2.83 46.61 - 18.48 41.25 4.04 42.11 - -

Tot cis 55.92 0.54 31.49 6.08 25.44 1.25 25.12 - 25.30 0.63 20.26 16.21 0.53 1.59 16.39 - 1.93 17.57 - 12.24 9.21 - 5.19 -

% trans (tot) 31.26 0.73 67.24 1.5261.63 4.30 67.73 1.21 - 66.80 1.24 73.74 1.53 73.58 - 75.04 72.62 - 77.01 2.54 82.05 - 78.09 -

% trans (mono)

26.16 1.68 58.36 1.23 73.92 - 78.35 3.26 65.82 2.07 62.31 - 65.47 0.62 73.00 72.86 - 75.16 1.41 - 78.92 1.53 82.59 -

1.19 2.87 74.59 1.84 74.98 72.90 5.27 80.91 - - % trans (PUFA)

52.51 66.62 8.85 70.06 3.36 73.52 - 68.89 75.92 - 3.32 70.52 - 62.85

- 151 -

Table 8.12.2) Products from factory A, blends/mixtures

mix2 n=3

mix3 n=1

mix1 n=1

12:0 0.00 0.00 0.00 - 0.00 -

14:0 7.89 0.29 7.71 - 5.38 -

15:0 0.48 0.11 0.55 - 0.29 -

16:0 22.51 1.25 20.26 - 19.25 -

16:1t 6.38 4.05 - 0.97 7.18 -

16:1c 3.17 0.05 2.85 - 1.27 -

16-pu 1.84 0.28 1.90 - 0.97 -

trans 0.62 0.11 0.86 - 0.61 -

0.38 0.29 - 0.00 -

18:0 8.50 0.31 9.07 - 7.07 -

18:1t 10.84 0.59 11.17 - 8.14 -

5.92 0.59 5.16 - 11.89 -

18-pu 1.01 0.11 0.99 - 16.64 -

trans 1.33 0.17 1.39 - 0.05 -

cis 0.89 0.35 0.73 - 2.10 -

20:0 3.05 0.96 2.82 - 2.91 -

20:1t 5.44 0.41 6.12 - 4.84 -

20:1c 2.38 0.58 2.67 - 1.79 -

20-pu 6.43 0.76 6.20 - 3.38 -

trans 1.54 0.09 1.49 - 1.40 -

cis 0.59 0.04 0.60 - 0.40 -

21:0 0.00 0.00 0.00 - 0.00 -

22:0 3.06 1.07 1.93 - 2.19 -

22:1t 3.75 0.81 4.98 - 4.68 -

22:1c 1.52 0.36 2.57 - 1.83 -

22-pu 5.49 0.17 5.48 - 3.10 -

trans 1.62 0.06 1.51 - 1.59 -

cis 0.56 0.02 0.53 - 0.27 -

24:0 0.26 0.18 0.07 - 0.11 -

24:1t 0.07 0.13 0.33 - 0.10 -

24:1c 0.00 0.01 0.00 - 0.12 -

cis 0.43

18:1c

- 152 -

mix2 n=3

mix3 n=1

mix1 n=1

Sat 45.76 0.72 42.41 - 37.19 -

Tot mono 39.47 1.51 43.03 - 38.72 -

Tot PUFA 14.77 1.26 14.57 - 24.09 -

Tot dbb 69.23 0.96 71.47 - 86.92 -

Tr-mono 26.49 0.75 29.78 - 21.81 -

Cis-mono 12.98 1.32 13.25 - 16.90 -

Tr-pufa 21.30 2.41 20.51 - 11.12 -

Cis-pufa 8.46 0.70 7.93 - 37.09 -

Tot trans 47.78 1.90 50.29 - 32.93 -

Tot cis 21.44 0.95 21.17 - 53.99 -

% trans (tot) 69.01 1.78 70.37 - 37.89 -

% trans (mono) 67.15 2.31 69.21 - 56.34 -

% trans (PUFA)

71.48 2.95 72.13 - 23.07 -

- 153 -

Table 8.12.3) Products from factories B and C

B33 n=1

B35 n=1

B41 n=1

B43 n=1

B-FH n=1

C1 n=1

C2 n=1

12:0 0.00 - 0.00 - 0.00 - 0.00 - 0.00 - 0.00 - 0.00 -

14:0 7.80 - 7.32 - 6.71 - 6.65 - 8.05 - 9.25 - 7.76 -

15:0 0.38 - 0.39 - 0.44 - 0.32 - 0.42 - 0.52 - 0.48 -

16:0 19.16 - 17.93 - 19.83 - 20.96 - 28.54 - 25.54 - 22.55 -

16:1t 4.46 - 4.91 - 3.78 - 2.53 - 1.34 - 4.83 - 7.73 -

16:1c 3.81 - 3.71 - 2.01 - 1.94 - 0.12 - 6.71 - 3.17 -

16-pu 1.52 - 1.37 - 1.42 - 1.15 - 0.68 - 3.60 - 2.44 -

trans 0.60 - 0.41 - 0.00 - 0.71 - 0.00 - 1.20 - 1.01 -

cis 0.38 - 0.23 - 0.00 - 0.00 - 0.00 - 0.54 - 0.29 -

18:0 4.56 - 8.87 - 10.75 - 11.50 - 21.43 - 4.57 - 9.35 -

18:1t 11.17 - 10.93 - 11.21 - 11.18 - 0.89 - 8.48 - 11.03 -

18:1c 9.50 - 6.51 - 5.45 - 5.95 - 0.69 - 14.16 - 5.47 -

18-pu 1.81 - 1.74 - 0.76 - 1.17 - 0.00 - 2.13 - 1.21 -

trans 1.15 - 0.80 - 0.57 - 1.49 - 0.00 - 0.92 - 1.17 -

cis 0.73 - 0.50 - 0.82 - 1.23 - 0.00 - 0.77 - 0.45 -

20:0 2.04 - 2.74 - 4.38 - 4.97 - 18.79 - 0.75 - 1.74 -

20:1t 5.48 - 5.33 - 6.95 - 6.17 - 0.00 - 1.49 - 4.04 -

20:1c 4.57 - 3.46 - 2.71 - 3.02 - 0.53 - 2.61 - 1.64 -

20-pu 4.87 - 4.27 - 3.25 - 3.22 - 0.00 - 7.25 - 9.41 -

trans 1.77 - 1.61 - 1.38 - 1.17 - 0.00 - 1.40 - 1.52 -

cis 0.85 - 0.66 - 0.63 - 0.65 - 0.00 - 1.41 - 0.61 -

21:0 0.00 - 0.00 - 0.00 - 0.00 - 0.66 - 0.00 - 0.00 -

22:0 3.73 - 3.14 - 5.83 - 5.43 - 16.64 - 0.00 - 1.00 -

22:1t 5.31 - 6.85 - 6.55 - 6.26 - 0.40 - 2.39 - 3.06 -

22:1c 5.30 - 4.83 - 3.24 - 3.60 - 0.00 - 3.30 - 0.79 -

22-pu 3.97 - 5.15 - 4.13 - 3.44 - 0.00 - 3.52 - 6.67 -

trans 1.41 - 1.96 - 1.37 - 1.51 - 0.00 - 1.85 - 1.58 -

cis 0.59 - 0.91 - 0.30 - 0.62 - 0.00 - 0.58 - 0.50 -

24:0 0.13 0.08 - 0.55 - - 0.38 - 0.81 - 0.00 - 0.42 -

24:1t 0.00 - 0.00 - 0.00 - 0.16 - 0.00 - 0.24 - 0.05 -

24:1c 0.43 - 0.00 - 0.52 - 0.00 - 0.00 - 0.51 - 0.00 -

- 154 -

B33 n=1

B35 n=1

B41 n=1

B43 n=1

B-FH n=1

C1 n=1

C2 n=1

Sat 37.80 - 40.94 - 48.02 - 50.20 - 95.34 - 38.78 - 43.30 -

Tot mono

50.03 - 46.53 - 42.42 - 40.82 - 3.98 - 44.72 - 36.98 -

Tot PUFA

12.17 - 12.53 - 9.56 - 8.98 - 0.68 - 16.51 - 19.72 -

Tot dbb 75.55 - 74.09 - 56.92 - 57.98 - 3.98 - 83.47 - 75.99 -

Tr-mono 26.43 - 28.03 - 28.50 - 26.30 - 2.64 - 17.43 - 25.90 -

Cis-mono

23.61 - 18.51 - - 13.92 - 14.52 - 1.34 - 27.28 11.07 -

- 18.90 - - 11.50 - 0.00 - 22.92 - 28.71

Cis-pufa 8.34 - 8.65 - 3.93 - 5.66 - 0.00 - 15.83 - 10.31 -

Tot trans 43.60 - 46.93 - 39.07 2.64 - 54.61 - - 37.80 - 40.36 -

Tot cis 31.95 - - 21.38 - 27.16 - 17.85 20.18 - 1.34 - 43.11 -

% trans (tot)

57.71 - 63.34 - 66.33 71.86 68.64 - 65.20 - - 48.35 - -

% trans (mono)

52.82 - 60.23 - 67.18 - 64.43 - 66.33 - 38.99 - 70.05 -

% trans (PUFA)

67.31 - 68.59 - 72.90 - 67.02 - - - 59.16 - 73.57 -

Tr-pufa 17.17 10.57 -

- 155 -

Table 8.12.4) Products with assumed melting point 31°C

31 n=9 31 (01/00) n=2

31 v-97 n=4

31 v-98 n=1

31 08-97 n=2

12:0 0.08 0.15 0.33 0.01 0.00 0.00 0.00 - 0.05 0.08

14:0 9.42 1.37 11.41 0.19 8.39 0.52 9.75 - 9.32 0.08

15:0 0.52 0.08 0.55 0.05 0.53 0.08 0.64 - 0.40 0.01

16:0 20.82 2.32 22.90 0.58 19.08 1.26 24.27 - 20.51 0.56

16:1t 7.62 1.42 9.76 0.54 7.02 0.83 6.49 - 7.24 0.52

16:1c 3.91 1.12 5.50 0.90 3.55 3.47 0.34 3.20 - 1.06

16-pu 1.85 0.34 2.33 0.31 1.66 0.11 2.17 - 1.57 0.76

trans 0.95 0.39 0.96 0.49 0.90 0.44 0.72 - 1.16 0.30

0.47 0.16 0.37 0.09 0.12 0.50 - 0.71 0.06

18:0 5.42 0.85 6.12 0.44 4.75 0.83 5.70 - 5.91 0.50

18:1t 9.68 1.59 9.02 10.12 0.50 12.09 0.98 - 5.29 6.26

18:1c 5.26 0.65 5.23 0.23 6.39 0.44 5.54 - 2.89 3.44

18-pu 2.89 0.36 1.81 0.02 1.23 0.21 1.77 - 7.83 9.71

trans 1.24 0.20 1.46 0.30 1.24 0.21 1.35 - 0.98 0.49

0.73 0.19 0.80 0.15 0.86 0.20 0.57 - 0.47 0.17

20:0 0.78 0.49 0.48 0.69 0.76 0.25 0.80 - 1.09 0.90

20:1t 5.52 1.58 4.34 1.02 7.25 0.70 4.23 - 3.90 3.14

20:1c 2.64 1.01 1.34 0.20 3.58 0.38 2.86 - 1.94 1.52

20-pu 8.66 1.39 10.12 0.29 7.32 0.66 9.47 - 9.46 0.50

trans 1.42 0.07 1.53 0.01 1.51 0.06 1.63 - 1.05 0.53

cis 0.62 0.12 0.65 0.03 0.60 0.04 0.68 - 0.59 0.48

21:0 0.82 0.00 0.00 0.00 0.00 0.00 0.00 - 3.67 5.19

22:0 1.44 0.51 0.96 0.66 1.50 0.54 1.27 - 1.89 0.29

22:1t 4.20 2.17 1.22 0.92 5.64 0.23 2.23 - 5.29 0.55

22:1c 2.23 1.12 0.65 0.15 3.15 0.10 1.83 - 2.16 0.57

22-pu 5.68 0.85 5.88 0.09 - 5.77 1.02 7.30 4.50 1.80

trans 1.59 0.06 1.60 0.09 1.61 0.06 1.54 - 1.54 0.10

cis 0.59 0.13 0.58 0.05 0.64 0.11 0.55 0.64 - 0.35

24:0 0.09 0.14 0.16 0.23 0.23 0.17 0.01 0.02 - 0.12

24:1t 0.63 0.11 0.00 0.14 0.07 0.19 0.17 - 2.37 3.32

24:1c 0.33 0.16 0.00 0.00 0.33 0.08 0.11 - 0.76 0.90

cis 0.39

cis

- 156 -

31 n=9

31 (01/00) n=2

31 v-97 n=4

31 v-98 n=1

31 08-97 n=2

Sat 38.92 3.99 42.73 0.78 34.91 1.14 42.67 - 41.25 1.92

Tot mono 42.01 6.10 37.14 0.24 49.12 1.20 36.62 - 35.38 9.57

19.07 2.26 20.14 15.98 0.99 20.72 - 23.37 7.65

Tot dbb 78.81 2.19 79.19 0.30 81.58 2.18 80.35 - 72.12 17.31

Tr-mono 27.65 4.14 24.41 0.84 32.19 0.80 23.07 - 24.09 7.14

Cis-mono 14.37 16.93 0.78 2.21 12.72 1.08 13.55 - 11.30 2.42

Tr-pufa 25.72 3.56 29.79 0.64 23.30 1.81 30.58 - 24.08 3.35

Cis-pufa 11.07 2.53 12.26 1.17 9.17 0.27 13.15 - 12.65 4.39

Tot trans 53.37 1.75 54.21 0.20 48.17 55.49 2.39 53.65 - 10.49

Tot cis 25.44 1.25 0.58 24.98 0.09 26.10 26.70 - 23.95 6.82

% trans (tot) 67.73 1.21 68.45 - 0.00 67.99 1.26 66.77 66.97 1.53

% trans (mono) 65.82 2.07 65.75 2.69 65.54 1.10 63.01 - 67.83 1.85

% trans (PUFA) 70.06 3.36 70.86 2.42 71.70 1.46 69.93 - 66.06 4.81

Tot PUFA 0.54

- 157 -

Table 8.12.5) Products with assumed melting point 35°C

35 n=3

35 (05/97) n=3

86-35 08-97 n=1

12:0 0.00 0.00 0.00 0.00 0.00 -

14:0 8.49 0.74 8.07 0.19 9.32 -

15:0 0.44 0.04 0.42 0.04 0.47 -

21.59 0.41 21.46 0.48 -

16:1t 8.43 0.77 8.87 0.07 7.55 -

16:1c 3.21 0.63 3.50 0.54 2.63 -

16-pu 2.53 0.44 2.78 0.08 2.02 -

trans 0.91 0.07 0.93 0.08 0.88 -

cis 0.42 0.15 0.44 0.20 0.38 -

18:0 7.51 0.83 7.34 1.10 7.84 -

18:1t 10.91 1.32 11.67 0.09 9.38 -

18:1c 4.30 0.52 4.57 0.33 3.75 -

18-pu 1.07 - 0.31 1.25 0.03 0.72

trans 1.40 0.13 1.42 0.18 1.35 -

cis 0.43 0.29 0.55 0.29 0.19 -

1.15 1.10 0.53 0.29 2.39 -

20:1t 6.78 0.24 6.73 0.32 6.88 -

20:1c 2.19 0.38 0.47 2.29 1.97 -

20-pu 8.29 0.53 7.99 0.11 8.90 -

1.54 0.01 1.54 0.02 1.55 -

cis 0.55 0.09 0.55 0.12 0.53 -

21:0 0.00 0.00 0.00 0.00 0.00 -

22:0 1.36 0.83 0.94 0.57 2.20 -

22:1t 3.76 0.50 3.50 0.30 4.29 -

22:1c 1.27 0.61 0.96 0.40 1.89 -

22-pu 6.33 1.42 6.66 1.84 5.68 -

trans 1.59 0.02 1.58 0.01 1.61 -

cis 0.46 0.03 0.46 0.04 0.47 -

24:0 0.00 0.00 0.00 0.00 0.00 -

24:1t 0.36 0.10 0.35 0.14 0.38 -

24:1c 0.23 0.19 0.31 0.17 0.07 -

16:0 21.85

20:0

trans

- 158 -

35 n=3 35 (05/97) n=3

35 08-97 n=1

Sat 40.34 43.89 3.17 38.56 1.07 -

Tot mono 41.44 2.35 42.76 0.71 38.79 -

Tot PUFA 18.22 1.48 18.68 1.78 17.32 -

Tot dbb 77.16 4.09 79.35 2.13 72.77 -

Tr-mono 30.24 1.54 31.12 0.19 28.47 -

Cis-mono 11.20 1.00 11.64 0.90 10.32 -

Tr-pufa 26.66 27.15 2.26 2.96 25.67 -

Cis-pufa 9.06 0.65 9.44 0.12 8.32 -

Tot trans 56.89 3.27 58.27 3.15 54.14 -

Tot cis 20.26 1.59 21.08 1.02 18.63 -

% trans (tot) 73.74 1.53 73.41 2.00 74.40 -

% trans (mono) 73.00 1.23 72.79 1.66 73.40 -

% trans (PUFA) 74.59 1.84 74.12 2.33 75.53 -

- 159 -

Table 8.12.6) Products with assumed melting point 39°C

39 n=11

39 (01/00) n=2

39 (05/97) n=5

39 (06/97) n=3

39 (06/98) n=1

12:0 0.06 0.10 0.24 0.03 0.03 0.05 0.00 0.00 0.00 -

14:0 8.84 1.12 11.07 0.15 8.32 0.19 8.26 0.12 8.72 -

15:0 0.47 0.12 0.54 0.10 0.48 0.16 0.39 0.03 0.53 -

16:0 22.78 1.93 23.94 0.36 21.46 0.78 24.75 2.12 21.09 -

16:1t 7.33 1.77 10.18 0.35 6.12 0.82 7.57 1.54 6.90 -

16:1c 2.61 0.74 3.82 0.14 - 2.06 0.44 2.57 0.08 3.02

16-pu 1.69 0.35 0.24 1.86 0.40 1.50 2.00 0.30 1.35 -

trans 0.54 0.19 0.74 0.26 0.43 0.16 0.56 0.15 0.64 -

0.21 0.11 0.22 0.04 0.12 0.17 0.17 0.27 -

18:0 8.89 1.21 7.40 0.59 9.10 0.53 9.36 1.95 9.40 -

18:1t 10.51 0.95 9.49 0.49 1.15 10.47 0.86 11.01 11.18 -

18:1c 3.82 - 0.53 3.92 0.75 3.72 0.32 4.04 0.86 3.53

18-pu 0.76 0.20 0.99 0.03 0.69 0.19 0.65 0.19 0.95 -

trans 0.96 0.30 0.71 0.01 0.28 0.92 0.33 1.19 0.96 -

0.60 0.18 0.54 0.37 0.22 0.48

20:0 2.81 1.06 1.61 0.57 3.45 0.81 2.49 1.23 2.97 -

20:1t 7.15 1.11 6.01 0.39 0.40 8.10 0.84 6.38 6.98 -

2.03 0.33 1.83 0.23 2.31 0.09 1.78 0.39 1.84 -

20-pu 6.18 1.02 7.59 0.40 5.44 0.47 6.83 0.35 5.11 -

trans 1.41 0.11 1.45 0.17 1.39 0.14 1.39 0.05 1.46 -

cis 0.52 0.09 0.59 0.03 0.52 0.04 0.46 0.14 0.61 -

21:0 0.33 1.09 0.00 0.00 0.72 1.61 0.00 0.00 0.00 -

1.93 0.93 1.19 0.07 2.05 1.24 2.10 0.73 2.37 -

22:1t 4.96 2.12 2.19 0.01 6.76 0.55 3.31 0.94 6.44 -

22:1c 1.36 0.69 0.29 0.57 0.12 1.95 0.20 0.74 1.88 -

22-pu 5.13 0.55 5.45 0.28 4.80 0.68 5.36 0.18 5.42 -

trans 1.57 0.14 1.65 0.16 1.59 0.07 1.46 0.23 1.61 -

cis 0.43 0.11 0.50 0.01 0.40 0.14 0.43 0.10 0.39 -

24:0 0.09 0.13 0.02 0.06 0.02 0.16 0.16 0.04 0.03 -

24:1t 0.22 0.17 0.09 0.13 0.28 0.09 0.24 0.30 0.09 -

24:1c 0.13 0.15 0.23 0.00 0.00 0.10 0.09 0.26 0.20 -

cis 0.21

cis 0.54 0.26 0.52 -

20:1c

22:0

- 160 -

39 n=11

39 (01/00) n=2

39 (05/97) n=5

39 (06/97) n=3

39 (06/98) n=1

Sat 46.13 2.36 46.00 0.51 45.68 1.32 47.29 4.61 45.11 -

Tot mono 40.12 2.86 38.11 0.61 41.89 1.46 37.87 3.87 42.06 -

Tot PUFA 13.75 1.84 15.89 1.12 12.43 1.50 14.84 0.74 12.83 -

Tot dbb 64.77 4.54 68.36 0.33 63.95 8.12 66.01 3.06 63.32 -

1.42 28.52 2.78

10.14 0.50 10.14 0.45 1.23 10.47 -

Tr-pufa 18.39 2.80 -

1.52

2.83 -

1.35 -

1.52 73.13 1.26 75.44 1.38 75.63 1.58 75.04

% trans (mono) 75.16 1.41 73.38 1.74 75.76 1.17 75.34 1.28 75.11 -

% trans (PUFA) 74.98 3.32 72.80 0.65 75.16 3.83 76.17 4.32 74.92 -

Tr-mono 30.17 2.39 27.97 1.11 31.74 31.59 -

Cis-mono 9.96 0.75 9.35

22.02 0.49 16.48 2.13 19.29 2.51 17.94

Cis-pufa 6.26 1.64 8.23 0.45 5.58 6.16 1.91 6.01 -

Tot trans 48.55 49.99 0.62 48.23 2.14 47.81 5.20 49.53

Tot cis 16.21 1.93 18.37 0.95 2.93 15.72 15.51 16.47

% trans (tot) 75.04 -

- 161 -

Table 8.12.7) Raw oil composition

Date 29.05. 97

02.10. 97

10.10. 97

55 0.

10

28.04.97

apr/

may 97

apr/ may 97

20.05. 97

06.08. 97

14.08. 97

25.08. 97

31.10. 97

20.11. 97

Aut.

97

11.05. 98

may/ jun 98

jan.

00 Factory

A A A A A A A A A A A A A A A A C C

Code RA1 RA2 RA3 RA4 RA5 RA6 RA7 RA8 RA9 RA10 RA11

RA12 RA13 RA14 RA15 RA16 RC1 RC2 14:0 7.37 7.64 6.43 7.10 6.79 8.25 7.89 7.65 7.75 7.80 7.47 7.51 7.75 8.06 6.04 9.98 7.42 7.41 15:0 0.45 0.49 0.54 0.49 0.48 0.47 0.44 0.47 0.42 0.42 0.41 0.55 0.44 0.55 0.29 0.55 0.31 0.29 16:0 16.65 16.86 17.61 19.33 17.33 17.13 16.98 17.09 16.61 16.67 15.42 18.32 16.95 18.51 12.32 20.32 19.89 19.84 16:1n-7

7.67 7.46 6.27 7.87 6.94 8.13 7.52 7.76 7.07 7.77 7.06 7.30 7.77 8.02 7.51 10.99 8.46 8.44

17:0 0.44 0.42 0.41 0.41 0.36 0.41 0.43 0.45 0.33 0.46 0.32 0.52 0.46 0.39 0.06 0.44 0.24 0.20 16:2 0.77 0.81 0.70 1.17 0.89 1.04 0.94 0.90 0.94 1.05 0.87 0.84 1.03 0.95 0.57 1.56 1.48 1.43 16:3 0.80 0.92 0.60 1.20 0.86 1.27 1.09 1.04 0.92 1.14 0.87 0.96 1.03 0.90 0.33 1.88 1.90 1.85 18:0 2.58 2.76 3.69 3.74 3.19 3.06 3.11 2.97 2.83 2.86 2.48 3.46 3.05 2.83 1.20 3.55 2.85 2.83 16:4 1.63 1.73 1.19 2.77 1.81 2.03 1.87 1.66 1.99 1.96 1.74 1.50 2.12 1.16 1.24 1.41 5.01 4.94 18:1n-9 12.48 11.24 14.01 11.91 11.94 9.33 9.81

2.8910.30 8.91 9.14 8.98 10.51 9.86 11.51 9.03 8.25 11.15 10.97

18:1n-7

3.11 2.97 3.21 3.43 3.21 2.87 2.92 2.55 2.85 2.490.08

2.87 2.96 3.00 2.85 3.20 3.05 2.93 18:2 0.11 0.14 0.06 0.07 0.07 0.13 0.13 0.14 0.11 0.18 0.14 0.19 0.09 0.02 0.11 0.17 0.19 18:2n-6

1.76 1.52 1.56 1.00 1.31 1.07 1.19 1.36 1.27 1.03 1.06 1.59 1.38 1.99 1.09 1.93 0.70 0.70

18:2 0.29 0.30 0.30 0.50 0.34 0.40 0.34 0.33 0.29 0.33 0.23 0.30 0.34 0.28 0.16 0.41 0.67 0.64 18:3n-6 0.22 0.19 0.16 0.22 0.19 0.24 0.19 0.20 0.19 0.19

0.0.15

660.22 0.20 0.20 0.16 0.28 0.22 0.22

18:3n-3

1.02 0.77 0.81 0.43 0.66 0.53 0.58 0.70 0.61 0.63 0.58 1.08 0.61 0.83 0.32 0.33 20:0 0.18 0.20 0.23 0.20 0.17 0.30 0.32 0.27 0.29 0.29 0.21 0.35 0.25 0.22 0.11 0.28 0.11 0.12 18:4n-3 2.36 2.25 2.10 2.28 2.30 1.99 1.89 2.27 2.36 2.40 2.39 2.02 2.38 2.50 3.39 2.10 2.77 2.80 20:1n-11 0.52 0.61 0.88 0.52 0.79 0.57 0.68 0.62 0.79 0.56 0.86 0.48 0.49 0.87 0.90 0.51 0.60 0.58 20:1n-9 5.76 6.08 5.09 2.69 5.47 4.63 5.09 4.63 6.09 5.10

0.477.45 4.25 3.81 5.32 14.79 1.73 2.32 2.38

20:1n-7 0.42 0.47 0.36 0.35 0.44 0.44 0.53 0.43 0.39 0.52 0.48 0.35 0.34 0.98 0.26 0.26 0.24 20:2nX 0.13 0.14 0.09 0.06 0.10 0.16 0.18 0.16 0.15 0.16 0.12 0.14 0.20 0.15 0.02 0.24 0.17 0.17 20:2n-6 0.27 0.21 0.23 0.14 0.19 0.17 0.18 0.19 0.17 0.16 0.19 0.22 0.14 0.20 0.20 0.19 0.08 0.06

0. 20:3n-6 0.14 0.13 0.12 0.11 0.11 0.16 0.15 0.15 0.12 0.14 0.11 0.13 0.14 0.16 0.08 0.25 0.13 20:4n-6 0.54 0.58 0.64 0.68 0.60 0.72 0.67 0.68 0.53 0.66 0.51 0.94 0.67 0.64 0.18 1.01 0.28 0.28 20:4n-3 0.72 0.74 0.77 0.68 0.72 0.68 0.70 0.77 0.66 0.67 0.64 0.66 0.72 1.04 0.46 1.19 0.63 0.6022:1n-11 6.11 6.94 5.95 1.54 6.02 5.22 6.19 5.01 8.54 6.30 10.41 4.46 4.62 6.43 17.81 1.24 1.04 0.97 22:1n-9 0.82 0.85 0.67 0.37 0.78 0.72 0.68 0.66 0.83 0.84 0.98 0.65 0.60 0.62 2.30 0.25 0.29 0.26 20:5n-3 12.65 12.70 10.90 14.44 12.64 15.76 14.41 14.65 14.26 15.55 13.29 13.70 16.39 9.90 8.54 14.13 18.81 19.17 21:5n-3 0.46 0.48 0.42 0.64 0.50 0.59 0.58 0.58 0.57 0.57 0.57 0.51 0.60 0.47 0.32 0.70 0.91 0.89 22:5n-6 0.18 0.17 0.23 0.22 0.20 0.21 0.26 0.23 0.20 0.23 0.19 0.35 0.23 0.22 0.06 0.28 0.10 0.08 24:1n-9 0.60 0.55 0.55 0.48 0.54 0.43 0.48

1.950.44 0.53 0.36 0.55 0.46 0.41 0.50 0.56 0.24 0.28 0.28

22:5n-3 1.39 1.52 2.08 2.70 1.93 1.83 1.80 1.68 1.72 1.589.14

1.84 1.91 1.87 0.47 2.30 2.30 2.37 22:6n-3 9.43 9.12 11.16 10.28 10.13 9.06 9.66 10.52 9.05 9.42 11.15 9.98 9.02 5.35 7.41 5.10 5.44

- 162 -

8.13) List of abbreviations

AMU Atomic mass units, in mass spectrometry, equals m/z when z=1

AMP 2-amino-2-methyl propanol

CH Carbon-Hydrogen (group or bonds)

Lightpipe, interface between GC and IRD see section 4.3.2

CLA Conjugated linolenic acid

CO Coconut oil

CST CS matrix, concentration x spectra matrix in GC-IR and GC-MS, see section 4.5 and Liang et al. (1993)

dbb Double bonds

DCM Dichloromethane

DD Direct deposition (interface), see section 4.3.2

DHA Docosahexaenoic acid (22:6n-3)

DMOX Dimethyl oxazoline derivatives, see section 4.2.3

DPA Docosapentaenoic acid (22:5n-3)

ECL Equivalent chain length

EI Electron impact, ionisation in mass spectrometry

ELSD Evaporative light scattering detector

EPA Eicosapentaenoic acid (20:5n-3)

FAME Fatty acid methyl esters

FCL Fractional chain length

FH Fully hydrogenated

FT Fourier transform

GS Gram-Schmidt chromatograms, GC-IR, see section 4.3.1

GC Gas chromatography

HHS Half hydrogenated state, see chapter 3.

HPLC High performance liquid chromatography

IR Infrared / Infrared spectroscopy

IRD Infrared detectior/detection

IV Iodine value

LP

LV Latent variable, see section 4.5

mp Melting point

m/z Mass over charge, fragment masses in mass spectrometry

M+ Molecular ion (in mass spectrometry) see section 4.2

- 163 -

MI Methylene interrupted, the common pattern of double bonds in natural PUFA with one methylene group between each double bond.

MI Matrix isolation (interface), see section 4.3.2

MS Mass spectrometry

MSD Mass spectrometric detector/detection

MUFA Monounsaturated fatty acids

NMI Non-methylene interrupted fatty acids (refer to double bonds in dienes and PUFA), more than one methylene group between each double bond. Does not include conjugated isomers.

NMR Refers to melting point determined by NMR at 20 or 30°C

PAM π-allylic mechanism, see chapter 3.

PC Principal component

PCA Principal component analysis

PCR Principal component regression, see section 4.5.4

PEG Polyethylene glycol (coating in GC-columns, medium polarity)

PH Partially hydrogenated

PHSBO Partially hydrogenated soybean oil

PHFO Partially hydrogenated fish oil

PHVO Partially hydrogenated vegetable oil

PLS Partial least squares regression

PO Palm oil

PTSA Para toluene sulfinic acid, reagent for isomerisation of cis double bonds

PUFA

SEP

Saturated fatty acids

VO Vegetable

Polyunsaturated fatty acids

Rt Chromatographic retention time

SBO Soybean oil

Standard error of prediction, error measurement in calibrations

SFA

SWC Selected wavelength chromatogram, see section 5.4.8.

oil

- 164 -

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