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Applied Spectroscopy Reviews

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Exploring the Potential of Applying InfraredVibrational (Micro)Spectroscopy in Ergot AlkaloidsDetermination: Techniques, Current Status, andChallenges

Haitao Shi & Peiqiang Yu

To cite this article: Haitao Shi & Peiqiang Yu (2017): Exploring the Potential of Applying InfraredVibrational (Micro)Spectroscopy in Ergot Alkaloids Determination: Techniques, Current Status, andChallenges, Applied Spectroscopy Reviews, DOI: 10.1080/05704928.2017.1363771

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Ergot Alkaloids Research with Infrared Spectroscopy

Exploring the Potential of Applying Infrared Vibrational

(Micro)Spectroscopy in Ergot Alkaloids Determination: Techniques,

Current Status, and Challenges

Haitao Shi & Peiqiang Yu*

Department of Animal and Poultry Science, College of Agriculture and Bioresources, the University of

Saskatchewan, 51 Campus Drive, Saskatoon, Canada, S7N 5A8

*Corresponding author, Peiqiang Yu, Ph.D., Professor and Ministry of Agriculture Strategic Research

Chair, Department of Animal and Poultry Science, College of Agriculture and Bioresources, University of

Saskatchewan, 6D10 Agriculture Building, 51 Campus Drive, Saskatoon, Canada, S7░N 5A8, Tel: +1

306 966 4132, Fax: +1 306 966 4150, E-mail: [email protected]

ABSTRACT

Ergot alkaloids are toxins produced mainly by Claviceps fungi and are considered as one of the most

important groups of mycotoxins. Rapid and reliable detection techniques are urgently required by

producers, importers and market regulators. As a promising alternative to conventional wet chemistry,

infrared (IR) based techniques are non-destructive, rapid, and cost-effective. However, very limited

studies on the qualitative or quantitative analysis of ergot or ergot alkaloids in food or feed based on IR

vibrational spectroscopy have been reported so far. Being a secondary technique, the accuracy of IR

method heavily dependents on the robustness of chemometrics models. This paper aims to offer a brief

overview of the ergot alkaloids issue in food and feed, conventional detection methods, theoretical

principles of IR-based techniques, and commonly used chemometrics for spectral data processing. In

addition, the current application status of IR spectroscopy in ergot research is also considered.

KEYWORDS

ergot alkaloids; mycotoxin; vibrational spectroscopy; chemometrics

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Introduction

Concept of ergot alkaloids

Ergot alkaloids (EA) are toxic metabolites produced mainly by Claviceps fungal species, most notably by

Claviceps purpurea, which infect the seed heads of cereals and wild grasses at the time of flowering (1,

2). Claviceps fusiformis and Claviceps purpurea, which have been blamed as the causes of some severe

human/animal epidemics, are the two most important species among them (3). More than 600

monocotyledonous plants, including barley, wheat, rye, rice, corn, oats, and forage grasses, are vulnerable

to these fungal species (4). When infected by these fungi, the developing seeds will be replaced by ergot

bodies (also known as sclerotia) which may contain various EA and are usually harvested together with

uncontaminated grain (5, 6). The types of EA and their concentrations in ergot bodies can be affected by

the maturity of ergot bodies, types of fungi and plant, weather conditions, geographical locations, etc (7,

8). More than 80 different types of EA have been discovered and more than 70 of them are produced by

Claviceps species (4). Clavine EA, lysergic acid amide EA, and peptide EA are the three most common

categories. Ergocristine, ergosine, ergotamine, ergometrine, ergocornine, and ergocryptine that belong to

the group of Clavines are regarded as the most dominant and toxic EA which are frequently detected in

food and feed stuffs (7). Figure 1 shows the chemical structure of the six major EA that derived from

PubChem Compound database (9).

Contamination status in food and feed

Ergot contamination in feed and food could be observed in almost all parts of the world. In 2011, up to

20% of the wheat produced in western Canada was contaminated by ergot to a certain extent, and this

situation is likely to get worse in the future according to the results of climate-change prediction models

(10). The contamination status of six major EA and their epimers in cereals and cereal products (803

samples) in 13 European countries has been investigated between 2010 and 2011 (11). Results showed

that 34% of wheat feed, 48% of triticale feed, 52% of rye feed, 76% of food products, 86% of wheat food,

and 95% of rye food were contaminated with EA (concentration of total EA varies from 1 to 12340

μg/kg). The occurrence of ergometrine, zearalenone, deoxynivalenol, and alternariol in beer collected

from German market has been investigated and results showed that ergometrine was detected in 93% of

beer samples with the concentration between 0.07 and 0.47μg/L (12). Mulder et al. (2015)

(13)investigated the occurrence of EA and tropane alkaloids in cereal products for young children and

infants in Netherlands (13). Both the major EA and seven minor EA (i.e. agroclavine, chanoclavine-1,

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elymoclavine, ergine, erginine, festuclavine, and lysergol) content of 113 samples (i.e. breakfast cereals,

biscuits and cookies) has been determined and results showed that 54% samples were contaminated with

EA and tropane alkaloids were found in 22% samples. Tittlemier et al. (2015) developed a method for

analyzing 10 EA in grain samples and studied the contamination status of these EA in western Canadian

cereal grains (14). They observed a strong linear relationship between the amount of ergot bodies and the

content of EA (R2 = 0.826 and 0.911 for Canadian western Red Spring and Canadian western Amber

Durum, respectively), and grain samples collected at harvest had higher concentrations of total EA than

that collected from shipments. Besides, ergocristinine was reported as the most dominant alkaloids in

wheat and durum samples from grain shipments. Endophyte-infected grasses (e.g. fescue and tall fescue)

toxicosis in pasture-based regions have been frequently associated with EA, and increased occurrences of

EA issues has been observed in regions that typically not related to grazing-based farming due to

international forage trading (15).

Effects on animals and human

Ergot alkaloids have been recognized as a group of the most important natural toxins and pharmaceuticals

for a long time (16). The earliest recording of the effects of ergot that can be authenticated has been

discovered in Chinese ancient documents (originated around 1100░BC) which described the application

of ergot as an obstetric medication (4). The severe pathological syndromes caused by the ingestion of

food or feed that contaminated with EA are known as ergotism. In 944--945 AD, ergot poisoning caused

the death of about 20000 people in France, and this was probably the first documented epidemic of

ergotism (4). In Europe, St. Anthony’s Fire, the severe epidemics caused by intaking of ergot

contaminated grains and grass, was one of the things that made the Middle Ages a horrible time (17).

Due to the advancement in crop and food science (e.g. crop management, grain cleaning techniques,

and contaminants detection methods), ergotism is now regarded as a very rare disease for human and

usually resulted from overdosing of EA-based medicine rather than ingestion of EA contaminated food

(18). In many countries, ergot-contaminated grain is not allowed to be used as food for humans and

redirected for feedstuffs of animals (10). However, ergotism is continuing to be a significant issue for

people living in some underdeveloped regions and animals (2, 3, 18).

Many factors, such as host plants, fungal species, growth environment, size of ergot bodies, and the

interaction with other mycotoxins that might concurrent in cereals and feed could affect the toxicity of EA

(3, 10). After uptaking food or feed that contaminated with certain amount EA, clinical symptoms may

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appear in a few hours or several months (3). The diagnosis of ergot poisoning is usually difficult since

some symptoms are similar with other disease such as respiratory disorders, frostbite, and foot rot (10,

19).

In general, there are four forms of ergotism in mammals, including convulsive ergotism, gangrenous

ergotism, enteroergotism, and hyperthermic ergotism (10). Sheep and horses are usually more vulnerable

to convulsive ergotism than cows; gangrenous ergotism is more common in pigs and cows that could

cause lameness which may be followed by the partial or entire loss of hooves, tail, ears, and even limbs;

enteroergotism and hyperthermic ergotism are considered as less severe and might give rise to vomiting,

fever or endocrine disorders (2, 10).

The health and performance of farm animals can be harmed by eating EA contaminated feed and the

seriousness can be influenced by other factors such as animal species, age, environment temperature, etc

(10). Previous studies confirmed that the growth rate, reproductive performance, pregnancy rate, and

sperm motility of many animals (e.g. cattle, sheep, swine, poultry, etc.) could be negatively affected by

the consumption of ergot contaminated feed (10, 20, 21).

Due to the limitation of detection technology and other factors, previous toxicology studies mainly

focus on ergot bodies or total EA rather than single ergot alkaloid and their interactions. Consequently,

the regulatory limits are usually established based on concentration of ergot bodies or total EA (2).

However, the concentration and proportion of individual EA within sclerotia can vary significantly, and

their toxicities in humans and animals still need further research (10).

Strategies to reduce their harmful effects

Up to 82% ergot bodies in the grain could be cleared away with proper cleaning procedures at mills (2).

However, high concentration of EA still has been detected in cereals and cereal products from time to

time according to the results of previous surveys (7). Furthermore, cleaning procedures might be

ineffective when ergot bodies are produced in dry climatic environment which usually have similar size

with grain or when the original ergot bodies break into smaller pieces during harvest, transportation, and

storage (22).

Minimizing the production of ergot bodies in the field is of great importance for the control of EA

contamination of feed and food. Genetic engineering might be a potential approach for the developing of

ergot-resistant crop varieties. Some sorghum genotypes that present high levels of ergot-resistance have

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been discovered and further effort is required to explore the physiological basis and inheritance for

resistance in them (23). The genetic difference for ergot-resistance in cytoplasmic-male sterile rye has

been studied and more research on the specific resistant mechanisms is needed (24). In addition, field

management, environmental conditions, and the application of biological and chemical agents are also

considered as important factors which can influence the forming of mycotoxins pre-harvest (25).

It is generally assumed that ergot bodies can only be formed during pre-harvest period and the

concentration of EA is supposed to be relatively constant after host crops are harvest (19). However,

another study reported that the aerobic instability resulted from extended storage favors the growth of

ergot-producing fungi and could lead to an increase in ergopeptinines concentration in high-moisture

grain (2). Therefore, appropriate storage management is required to prevent the increase of EA content in

feed.

The toxicity levels of EA could be affected by their inherent chemical structure and processing

methods. Temperature and pH could influence the C9 = C10 double bond contained in EA, therefore heat

treatment might be an effective way to reduce the toxicity of EA (10, 26). The effect of hydrothermal

treatments on EA content in rye has been studied and the treatment resulted in approximately 10%

reduction in total EA content (27).

A very promising dietary approach to minimize the detrimental effects of mycotoxins is the application

of mycotoxin binders (28). Some mycotoxin binders are able to selectively bind EA and other mycotoxins

in feed. In a mycotoxin binding study, five major EA (i.e. ergosine, ergotamine, ergocornine,

ergocryptine, and ergocristine) and three other mycotoxins have been effectively absorbed by surfactant

modified zeolites (29). Some mycotoxins binders that are claimed to be capable of absorbing EA are

commercially available on the market now, while more animal studies are expected to further confirm

their effectiveness.

Brief introduction of infrared spectroscopy

Infrared spectroscopy is regarded as one of the most important analytical methods available to

researchers, which is developed based on the vibrations of chemical bonds within a molecule. (30).

Infrared region (14000-50░cm1) of the electromagnetic spectrum can be segmented into three major

areas, including near-infrared (NIR; 14000-4000░cm1), mid-infrared (MIR; 4000--400░cm1), and far-

infrared (FIR; 400--50░cm1) regions (31). The IR light can induce the excitation of vibrations of bonds,

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which can be influenced by many factors, such as the mass of atoms, firmness of bonds, as well as other

features of molecules (32). Specific functional group within a molecule has characteristic IR absorption at

certain frequency ranges, and a spectrum can be generated when the changes in absorption of IR light by

molecules are recorded by a spectrometer (33). In a spectrum, the absorption bands are characterized by

wavelength (energy), shape (environment of bonds), and intensity (polar character or polarizability) (30,

34). As a consequence, information related to the molecular characteristics of a sample can be revealed by

analyzing the specific spectrum.

Classical wet chemistry methods are well known due to their precise and accurate determination of

various constituents in food and feed. However, they are usually time-consuming, expensive, and based

on special skilled operators. IR spectroscopy is non-destructive, chemical-free, cost-effective, rapid, and

has been suggested as a powerful and promising substitution to conventional methods (30, 35). Another

distinct advantage easily overlooked is that IR spectroscopy could predict multiple parameters

simultaneously based on a single spectrum if proper calibration models were developed (36, 37).

The original chemical composition and structural characteristics of food and feed materials might be

altered by fungal infection and mycotoxin contamination (38-40). IR techniques have the potential to

detect such changes and perform qualitative and/or quantitative analysis by examining differences in

specific bands.

The application of IR based technique relies heavily on the construction of calibration model by

chemometrics, which needs great efforts, takes long time and could be regarded as a disadvantage.

However, the analysis time saved during the practical application of IR based method could easily make

up for the initial time investment required for calibration, and this advantage will keep growing as the

technique continues to be applied.

Conventional methodology for the determination of ergot alkaloids

Traditionally, visual inspection has been applied as a simple method to monitor the ergot contamination

status of grains since the ergot bodies usually have darker color and larger size than original grain kernels

(10). However, the size of sclerotia might vary greatly depending on grain types as well as other external

factors (e.g. climate, humidity, fungal strains, etc.). Besides, it’s impossible to determine the

concentration of EA by visual inspection. To effectively monitor the contamination status of EA in food

and feed and investigate their toxicodynamics and toxicokinetics in animal models, analytical techniques

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of high sensitivity and selectivity are highly required (18). Enzyme-linked immunosorbent assay

(ELISA), thin-layer chromatography (TLC), gas chromatography (GC), High-performance liquid

chromatography (HPLC), liquid chromatography-tandem mass spectrometry (LC/MS/MS) are commonly

used wet chemistry methods for the detection of EA in food and feed (2, 5, 18).

To make EA “available” for determination, these wet chemistry methods usually need time consuming

extraction and cleanup procedures that rely on hazardous and expensive organic solvents (39).

Furthermore, the final detection stage may also need well trained operators, expensive equipments, and a

long testing time.

Rapid screening methods

The spatial structure of the mycotoxin can be distinguished by a certain antibody, and the ELISA method

was developed based on this principle (41). Compared with other wet chemistry methods, ELISA seems

to be a sampler and faster technique for EA screening (2). A competitive inhibition ELISA method has

been used to detect ergot bodies and ergonovine in wheat and grass seed (42). This method has also been

applied to determine the total EA concentration in different native Turkish grasses (43). Nevertheless,

cross reaction is often a great concern for ELISA method and cross-reactivity might vary between

different groups of EA (2, 10).

As a rapid and cost-efficient method for mycotoxin detection, TLC can analysis multiple mycotoxin-

contaminated samples simultaneously, but it is unable to make precise or sensitive measurements (44). By

taking the approach, the EA need to be extracted from samples with proper solvent and then spotted on a

thin-layer plate. A semi-quantitative TLC method has been developed for the determination of ergovaline

content in tall fescue (21). Nevertheless, this method is now rarely used in EA determination area.

Quantitative methods

Compared with traditional liquid chromatography, HPLC employs much higher operational pressures

which can accelerate the separation process effectively. The HPLC with UV-diode array detection or

fluorescence detection (HPLC-FD) has been a powerful tool for quantitative mycotoxin analysis (45).

Most of the HPLC protocols applied for mycotoxins quantification are similar and many of them have

been adopted as the official AOAC methods (46). HPLC is an excellent tool for EA detection since it’s

able to quantify trace components at the parts per trillion levels (10). It has been frequently used for

quantification of the six major EA and their corresponding isomers (27).

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Gas chromatography is a widely used chromatography technique for separating and detecting

substances that can be vaporized without decomposition. In GC, a capillary or packed column is used as

stationary phase, and an inert gas usually works as the moving phase (47). The separation of components

is based on their relative affinity with the stationary phase. However, GC is unsuitable for the detection of

ergopeptide alkaloids since the hot injector could cause the decomposition of them (2).

Taking advantage of both the separation power of liquid chromatography and excellent sensitivity of

mass-spectrographic techniques, LC-MS/MS has gained more and more popularity in recent years (48).

Krska et al. (2008) developed and validated an accurate method for the simultaneous quantification of six

major EA as well as their corresponding epimers in food and cereal samples with limits of quantification

of 0.17 to 2.78 μg kg1 based on LC-MS/MS technique (49). Recently, Guo et al. (2016) demonstrated the

feasibility of quantifying up to 25 EA in cereal samples simultaneously by this technique (5).

Concept and principal of infrared spectroscopic techniques

Near-infrared spectroscopy

As a commonly used vibrational-spectroscopy-based technique, NIR spectroscopy has been applied to a

wide range of applications from agriculture to pharmaceutical, as well as petroleum industries (35). With

the optimization of instrument and development of chemometrics, NIR has become a prominent analytical

technique for both cereal quality monitoring and fundamental research related to characterizing complex

biological systems (36, 50).

The NIR spectra contain relatively weak overtones and combinations of fundamental vibrations mainly

related to O-H, N-H, and C-H functional groups (31, 36). The intensity of absorption bands decreases

with the increase of the rank of overtones. Moreover, absorbing a single photon might simulate more than

one vibration, and the interaction of these concurrent vibrations could give rise to combination bands

which also included in the NIR spectrum (30, 51). The structural selectivity of NIR spectra is much lower

than that of MIR due to the substitution of different combinations and overtones in NIR region (31).

Therefore, NIR spectra are usually difficult to be interpreted, and the assignment of specific absorption

bands to certain functional groups is technically more challenging (36).

However, NIR light can better penetrate into the sample and allow the directly analysis of strongly light

scattering or highly absorbing materials without complex sample preparation (52).

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Globar-sourced MIR molecular spectroscopy

MIR spectroscopy measures information from frequencies of fundamental molecular vibrations, and it is

commonly used to identify the structural characteristics of organic compounds (53). The MIR spectrum

could be divided into four major regions, including the X-H stretching region (4000-2500░cm1), the

triple bond region (2500-2000░cm1), the double bond region (2000-1500░cm1), and the fingerprint

region (1500-400░cm1) (30, 32). In contrast to NIR, most of the peaks in MIR region are sharp and

narrow due to fundamental vibrations of the matrix compounds (54). In addition, the absorption bands

could be assigned to specific functional groups due to the higher degree of resolution of MIR spectra (55).

The fingerprint region (1200-700░cm1) of MIR spectra contains abundant structural information, and

unique absorption bands of protein, polysaccharides, carotenoids, and lipids can be found within this

region (56). Fourier transform infrared (FTIR) spectrometer is a commonly used device to produce such

spectra, and it circumvented obvious defects of traditional dispersive IR spectroscopy technique (57).

Up to present, most quantitative spectroscopy has been developed on the basis of NIR spectroscopy,

the application of MIR technique for quantitative determination is still under development and not as

mature as for NIR (58).

Infrared hyperspectral imaging

As a branch of photography and spectroscopy, spectral imaging has been applied as a powerful technique

for astrophysics, remote sensing, satellite imaging, and terrestrial military applications (59, 60). In recent

years, the application of this relatively new and non-destructive method has expanded into more and more

areas such as agriculture, biology, archaeology, pharmaceuticals (36, 60).

Generally, spectral imaging can be classified as multispectral imaging (MSI) and hyperspectral

imaging (HSI) mainly according to the number of spectral bands and spectral resolution (60, 61). MSI

collects data within only several spaced spectral bands, while HSI continuously measures reflected energy

in more numerous bands (tens to hundreds). As a consequence, HIS generates dataset that contains more

abundant spectral information and is highly sensitive to even subtle variation in the spectra (61).

Hyperspectral imaging collects three dimensional information, including wavelength (z) and spatial (x,

y) dimensions (36, 61). The acquired data is presented as a three-way data matrix (x, y, z) which is

typically mentioned as hypercube (36). There are four distinct strategies for the acquisition of

hyperspectral datacube, including point-scanning spectrometry, pushbroom spectrometry, wavelength-

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scanning spectrometry, and snapshot imaging spectrometry (61). The pushbroom strategy is regarded as

the most widely used in food industry since its readiness facilitates the on-line analysis purpose (62).

Different regions of the electromagnetic spectrum can be employed by the HSI system, and the visible

(400-700░nm) and NIR (700-2500░nm) spectral range are more frequently used (60). By using the

conventional NIR technique, only a mean spectrum that contained absorption values at corresponding

wavelengths can be obtained. As a result of combining digital imaging with NIR spectroscopy, the near-

infrared HSI can provide both NIR spectral data and spatial information simultaneously, which allows

visualizing chemical characteristics and determining chemical constituents of specific samples at the same

time (36, 60). There are also several disadvantages of hyperspectral imaging, for example, more sensitive

detector and high-performance computer are needed to meet the requirement of data acquisition and

processing, and the complex hypercube usually need greater storage capacity and more professional data

analysis techniques (36).

Synchrotron infrared microspectroscopy

The joint application of IR spectroscopy and microscopy are referred as IR microspectroscopy, which has

become a highly sensitive, label free, and intact analytical tool for a variety of biological and medical

research since the mid-20th century (63, 64). IR microspectroscopy allows scientist to explore molecular

information at a cellular or subcellular level, which is necessary to the study of bio-chemical properties of

materials of interest (64, 65). Up to now, FTIR microspectroscopy has been widely applied to different

research fields (biology, materialogy, art restoration, and forensics). However, due to the intrinsic

brightness of conventional thermal IR light source, the signal-to-noise ratio (S/N) of FTIR

microspectroscopy is still unsatisfied (66). Synchrotron light, which is characterized by its high brightness

(100-1000 times brighter than a conventional thermal light source), can deliver the whole IR wavelength

range (from NIR to FIR) and is considered as the only “white” source (67, 68). By using synchrotron

radiation as light source, the spectral resolution and the S/N of FTIR microspectroscopy have been greatly

improved, abundant spectral and spatial information could be obtained, which allowing the in-depth

exploration of individual cells and tissue sub-structures (66, 68). The high quality spectral data obtained

makes it possible for researchers to perform more reliable analysis (36). In addition, no induced damage

to the samples has been detected since the brightness advantage of synchrotron light is not because it

generates higher power than other conventional thermal IR source (66). Despite the above advantages, it

should be noted that the availability of synchrotron light sources is still limited around the world and they

are mainly for research purposes at present.

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Common chemometrics techniques for qualitative and quantitative analysis

Due to the large amount of variables contained in a spectrum, IR spectral data is multivariate in nature. To

effectively extract the useful chemical information from the complex spectra that would correlate with

parameters of interest, chemometrics techniques are highly needed (30). A diversity of views on the word

chemometrics and its definition could be found from the literature (69). The International Chemometrics

Society defined chemometrics as “the science of relating measurements made on chemical system or

process to the state of the system via application of mathematical or statistical methods” (70). Designing

and optimizing experimental procedure and extracting maximum chemical information from analytical

data have been regarded as some of the major areas of chemometrics (71). To date, chemometric has been

used as routine technique for chemical researchers, and multivariate analysis of chemical data is always a

critical part of this interdisciplinary field (72). By the application of chemometrics, scientists are able to

handle typical chemistry and spectroscopy problems more efficiently, such as monitoring the process

status, exploring the structural characteristics of unknown organic compound, predicting the activity or

property of a chemical group, quantifying components in complex compounds, and classifying samples

into corresponding groups, etc (30, 35, 72).

Spectral preprocessing

The spectral data obtained from spectrometer contains not only sample information, but also noise and

back-ground information. Preprocessing of IR spectra to remove possible spectral variations unrelated to

chemical composition is regarded as an essential procedure before multivariate calibration (33, 35, 36).

Mean centering, baseline offset, derivatives, detrending, multiplicative scattering correction (MSC),

standard normal variate (SNV), and wavelet transform are commonly used preprocessing methods for

spectral data (35, 73, 74). Both the individual mathematical preprocessing methods and proper

combinations of them could be used to reduce various types of physical variations, chemical interference,

and negative effect of spectral side information (35)

Generally, SNV and MSC are regarded as the most widely used techniques for reducing light scattering

(commonly found in samples with different particle size distribution) and correcting baseline offsets;

derivatives (applied with a smoothing step) could reduce random-noise influence, highlight subtle band

shape, improve the resolution of overlapping bands, and preserve the relative band-intensity information;

detrending method is usually used for correcting baseline shift and curvilinearity of densely packed or

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powered samples, and follows SNV to reduce additional variations in baseline shift and curve linearity

(33, 35, 36). By subtracting the average value from each variable, mean centering could substantially

enhance differences between samples in terms of both concentration and spectral response (35, 75).

Wavelet transform utilizes variable window size to analyze signals at different resolutions or scales,

breaks up the signal into shifted and scaled versions of the original wavelet function (74). Many previous

studies suggest that wavelet transform could efficiently eliminate the background noise from IR spectral

data, and the denoising performance could be affected by factors of wavelet function, decomposition

level, and threshold (74, 76). Figure 2 shows the raw (A) and second derivative (B) spectrum of ground

wheat sample in the NIR region (680-2500░nm).

All the pretreatment techniques aim to remove un-modeled variability in spectral data, optimize the

signal from useful information, and facilitate subsequent calibration and exploratory study (35, 73).

However, it should be noted that the valuable information related to parameters of interest could be lost

by the application of a wrong or too severe preprocessing method (73).

Wavelength selection

Infrared spectra usually contain vast quantities of information related to molecular physicochemical

properties that can be used for analytical purpose (30, 77). However, considerable amounts of information

that irrelevant to the properties of interest (e. g. interactions, variabilities, noise, etc) also contained in the

spectra which may bring detrimental effects on the modeling process. Therefore, the spectral regions

employed might have substantial influence on the performance of classification and regression models.

Multivariate model constructed with the whole wavelengths does not always yield good prediction

performance since some of the wavelengths contain unrelated information which may distort the models

(35). Spectroscopic model developed with selected important wavelengths might achieve better predictive

performance and helps to speed up the computational speed of analyzing new samples. Wavelength

selection is especially important for three-way data matrix that obtained by HSI or microspectroscopy

techniques since the whole wavelength data sets always contain massive information and need more

computing time (62).

Up to now, it is still challenging to select the most informative wavelength variables from whole

spectra for calibration. There are many variable selection algorithms available to researchers. Variable

selection methods could generally be classified into three groups: filter methods, wrapper methods, and

embedded methods (78-80). In brief, filter approaches will calculate the variable importance ranking

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criteria and then remove the unwanted variable by applying a filtering rule (e.g. threshold cut-off);

wrapper methods wrap around an appropriate learning machine, which is applied as the evaluation

criterion, such as classification or prediction error; embedded approaches are similar to wrapper methods,

except that variable selection is achieved simultaneously with the training process. Detailed information

of advantage and disadvantage of these methods could be found in Liu et al (2014) (79).

Successive projections algorithm (SPA) has been reported as one of the popular filter methods (81). As

a forward variable selection technique, SPA focuses on the selection of effective wavelength variables

that containing minimum redundant information to reduce of detrimental effects of colinearity (82, 83).

This algorithm begins with one variable and merges another new variable iteratively till a certain number

of wavelengths are obtained. When there are large amount of variables, SPA is more effective to reduce

the workload of computation than GA technique since it uses simple vector projection operations (82).

Regression coefficient analysis (RCA), uninformative variable elimination (UVE) and Genetic

algorithms (GA) has been considered as commonly used wrapper methods for variables selection and

have been modified for important wavelengths selection purpose (79, 81, 84). Based on the PLS

algorithm, RCA is one of the popular filter methods for the determination of sensitive wavelengths (52,

81, 84). Regression coefficients (RC) could be used to reveal which wavelength variables could affect the

dependent variable in a greater degree (83). A RCA plot for PLS model to predict crude protein content in

wheat samples is shown in Figure 3.Wavelengths with high absolute RC values indicated that they were

the most informative wavelengths for the specific models. When applying this method, the setup of

specific threshold value of RC is suggested to facilitate the picking up of informative variables (85). UVE

assessing the significance of the variables by comparing their reliability index, a function of the

regression coefficients, with the ones of artificial random variables (78). It has been employed in

wavelength selections in IR spectroscopy and achieved good prediction performance (79). However,

latent variables are still required to be used for modeling since the quantity of variables selected by UVE

is still too large, therefore UVE has been combined with other algorithms in some studies for spectral

variable selection (86). Based on guided random search techniques, GA allow the efficiently exploration

of solution space and facilitate the implementation of parallel processing (82). Overfitting is usually

considered as the major concern when applying GA for variable selection (87). Another disadvantage of

GA is that the selection of important variables could be difficult when the spectra contain too many

wavelengths (large size of search domain) (84). Some researchers reported that complex spectra (e.g. with

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a large quantity of wavelengths and/or narrow peaks) could be handled effectively by the sequential

application of interval PLS algorithms and GA techniques (84).

Support vector machine (SVM) has been reported as one of the commonly used embedded methods

(79, 88). However, SVM method in standard formulation does not assess the significance of variables and

is therefore inappropriate for wavelength selection (89). As a variant of the standard L2-norm SVM, L1-

norm SVM constrains the L1-norm of the fitted coefficients and has the ability of automatically selecting

variables, not shared by the standard L2-norm SVM (90). The L1-norm SVM may have obvious advantage

over the L2-norm SVM especially when there are redundant noise variables. However, it is reported that

L1-norm SVM is unable to select groups of highly correlated variables and the number of selected

variables could be bounded by the size of the training data (90).

Regression methods

The relationship between reference values of chemical ingredients to be evaluated and the specific

absorption bands contained in the spectra could be revealed by the application of appropriate regression

methods (30, 34, 36). There are many regression algorithms available for developing quantitative IR

models, among which multiple linear regression (MLR), Principal component regression (PCR), partial

least squares regression (PLSR), SVM, and artificial neural networks (ANNs) are most commonly used in

agricultural research (33, 91, 92).

MLR is the easiest linear regression technique which could be used to relate the variations of a

dependent variable (Y) to the variations of several independent variables (X) based on ordinary least

squares regression (36). MLR works well only when there are a limited number of independent variables

(usually less than the number of samples) which are non-collinear and have a well understood relationship

to the dependent variables (72, 93). However, MLR is usually inappropriate or inefficient for spectral data

analysis since they usually contain more than hundreds of wavelength variables which might be highly

collinear (93).

The PCR and PLSR work well when the number of X-variables is much greater than the number of

samples and they are not affected by colinearity effects (94, 95). In PCR analysis, principal component

analysis (PCA) is performed firstly to decompose the X-matrix to obtain principal components (PCs), and

then these PCs will be applied as alternatives to original X-variables for fitting the MLR model (94). In

PCR, the multi-colinearity problem could be solved effectively, but the selection of optimum subset of

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predictors is still unachievable and some noise will remain in the PCs that selected for calibration (94,

96).

The PLSR is a well-know statistical technique for exploring the relation between a large number of

independent variables and dependent variables based on reducing the dimensionality of data set (95). It is

particularly powerful in constructing linear calibration models with IR spectral data since the negative

effect of irrelevant spectral variations could be reduced by this algorithm (97). To a certain extent, PLSR

could be considered as the effective combination of PCA and MLR. In contrast to PCR, PLSR

decomposes X-matrix and Y-matrix simultaneously, takes both of predictor variables and response

variables into account, and orders the PCs based on their relevance for the prediction of dependent

variables (94). As a consequent, PLSR model usually requires smaller number of PC than PCR model for

achieving comparable predictive ability (75). In non-linear situation, the modified versions of PLS such as

spline-PLS and polynomial-PLS, which replacing the initial linear function with spline or polynomial

function, could be applied to develop regression models (98).

The SVM is a supervised learning model with associated learning algorithms that can efficiently

conduct multivariate analysis (99). The function kernel in SVM technique allows implicitly mapping

sample space to a high-dimensional eigenspace, thus the non-linear problems could be transformed into

linear ones (100, 101). It has been widely employed in different areas, especially in food quality

monitoring (91, 102). As an optimized version of standard SVM, least-squares SVM (LS-SVM) uses

least-squares linear system as the loss function and can simplify the calculations of classical SVM and

investigate both nonlinear and linear relationships between response variables and spectral characteristics

more rapidly (91, 102, 103).

Artificial neural networks are widely used artificial intelligence techniques that can perform non-linear

modeling (104). ANNs employ algorithms designed to analysis and process information in a similar way

as the human brain and have the ability to learn from experience by themselves to solve a variety of

problems (105). The multiple layers contained in ANNs can perform different functions including

receiving information, processing the information by non-linear algorithm, and solving specific problems

(104). ANNs have shown great potential in solving complex problems (e.g. pattern recognition,

categorization, and prediction) in multiple research fields in recent years, and are getting more and more

attention in food research (106, 107).

Classification methods

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As fundamental chemometric techniques, multivariate classification methods are designed to construct

models that can be used for recognizing unknown objects to proper groups based on calculation (108).

For the qualitative analysis by IR spectroscopy, both supervised and unsupervised classification methods

are available to researchers (36). When applying supervised methods, the categories/classes of samples

for developing classification model are known in advance (104). For unsupervised methods, however, the

information regarding to sample categories/classes is unavailable (36). PCA and hierarchical cluster

analysis (HCA) are widely used unsupervised classification method and usually applied to investigate the

potential relationships between samples (77, 109). Commonly used supervised classification methods

including soft independent modeling of class analogy (SIMCA), linear discriminant analysis (LDA), PLS

discriminant analysis (PLS-DA), SVM, and ANN (108, 110).

Based on feature reduction algorithm, PCA forms the basis of many multivariate analysis techniques

and is mostly applied to explore the structure of data set and investigate potential relations between

samples at the beginning of data analysis (36, 110). PCA could reduce the dimensionality of original data

sets by deriving a new data set which composed of PCs, and then a score (eigenvector) is assigned to each

spectrum to define its relationship to the specific principal component (109). A two or three-dimensional

scatter plot could be created using the eigenvector to show the similarity between the spectra. It is worth

noting that only gross variability can be identified by PCA, and the variability within a group and among

groups can’t be distinguished by it (111).

HCA aims to build a hierarchy of clusters and reveal relatively homogeneous groups of variables

according to a predefined metric (112). HCA uses an iterative procedure to explore the possible structure

of samples: classifying spectra that with high similarity into a cluster, calculating the distance between

clusters, and incorporating the most similar pairs into new clusters (109, 113). Either Euclidean distance

or Mahalanobis distance can be used for distance calculation, and the later is more popular (77, 114).

HCA usually serves as an exploration tool in the early stage of data mining.

SIMCA is a widely used supervised classification method that developed based on the modeling

properties of PCA (104, 110). In SIMCA, A PCA is applied for each class of the samples and distinct

confidence regions can be created around different classes. An unknown sample is projected in each PCs

space that depicts a specific class to assess if it belongs to the class or not (115).

Like the PCA, LDA is another widely used dimension reduction and feature extraction technique (116).

In LDA, the input data is projected onto a vector space with lower-dimension thus the ratio of the within-

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group distance and between-group distance is maximized (111, 116). The Mahalanobis distances are

calculated for assigning objects to the nearest categories (77).

Partial least squares algorithm (PLS1 deals with one response variable situation and PLS2 handles

multiple response problems) is originally developed to deal with the problem of overdetermined

regression, while scientists in the field of application have discovered its potentiality to perform as a

discrimination and classification technique and further modified it for those purpose (111). PLS-DA is the

classification technique based on modeling the differences between different groups with PLSR (117).

With this technique, spectral data and reference group membership information of training samples could

be used to construct calibration models. PLS1-DA aims to solve binary classification problem, while

PLS2-DA could be applied when there are several classes need to be modeled (77, 111).

The SVM and ANN techniques mentioned in the previous section could be also used for classification

purpose. SVM technique originally aims to solve binary classification problems, and has been extended to

deal with multiclass situations in recent years (118). A variety of ANN learning strategies and

architectures which could handle unsupervised or supervised classification problems have been developed

(108). The selection of proper ANN strategy should be based on the nature of problems.

It is worth noting that the hypercubes (three dimensional data) obtained by hyperspectral imaging

systems could not be applied for qualitative and quantitative multivariate analysis directly. Appropriate

multivariate image analysis techniques are needed to transform the data from hypercube form to two

dimensional data before applying aforementioned multivariate spectral data analysis (36).

Current status of applying infrared spectroscopy in ergot research

Previous ergot studies with infrared spectroscopy

As one of the prevalent ergopeptine alkaloids, ergovaline is frequently detected in endophyte-infected

grasses and is associated with some severe livestock maladies (119). Roberts et al. (1997) established a

spectroscopic method based on NIR technique for the quantification of ergovaline content in tall fescue

(120). All samples were freeze-dried and ground prior to analysis. The HPLC method was applied to

obtain reference values of ergovaline and a NIR spectrometer (1110-2490░nm) was used to collect the

spectra of 99 samples. The prediction model was constructed by using PLSR technique. Important

wavelength ranges for the quantification of ergovaline content in tall fescue has been detected. The

determination coefficients and standard errors were 0.93 and 35 μg/kg (DM basis) for calibration and 0.88

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and 44 μg/kg (DM basis) for cross-validation. Although this model could only be applied to a limited set

of samples, it demonstrated the possibility of using NIR technique for detecting micro constituents in

samples. However, due to the absence of an independent prediction sample set for external validation, the

results obtained in this study could be overoptimistic.

Gislum et al. (2007) conducted a study to explore the possibility of constructing a chemometric model for

classifying Danish grass samples contaminated with ergovaline (121). Reference values of ergovaline in

167 plant samples were obtained by HPLC method, and the reflectance spectra were collected by using a

NIR spectrometer (1100-2500░nm). PCA was performed based on the MSC pre-processed spectra.

Nevertheless, their results showed that PCA technique was unable to distinguish between contaminated

and uncontaminated grass samples, possibly because the limited sensitivity of conventional NIR

technique is not enough to detect the small concentrations of ergovaline in grass. Moreover, improved

discrimination performance between contaminated and uncontaminated samples might be achieved by

coupling appropriate wavelength selection techniques to PCA since PCA algorithm models the total

variance in the data.

In another study, Roberts et al. (2005) developed an empirical prediction equation to measure total

ergot alkaloids content in tall fescue by NIR technique (122). A diverse population of tall fescue samples

(N = 84) were collected and tested for total ergot alkaloids content by ELISA method. NIR spectra (1110-

2490░nm) of these samples were generated, preprocessed by second-derivative transformation, and

regressed with ELISA reference data using modified partial least squares regression. The final calibration

(omitted toxic endophyte contaminated stockpiled samples) had a 1-variance ratio of 0.89, with a mean

and standard error of cross validation of 0.682 0.11. They concluded that NIR technique could be used

for quantifying total ergot alkaloid content in tall fescue.

Vermeulen et al. (2012) developed a method based on HSI technique and chemometrics for the online

determination of ergot bodies in cereals (6). Seven wheat samples with differing content of ergot bodies

(0-10000░mg/kg) were prepared for the construction of models and an additional sample was used for

validation of the established models. Hyperspectral images were collected with a near infrared HSI

system (1100-2400░nm) and then subjected to multivariate image and multivariate spectral analysis

(PLSDA and SVM). The correlation between reference values and predicted values yielded by PLSD and

SVM models was higher than 0.99. The limits of quantification and detection were 341░mg/kg and

145░mg/kg, respectively. This study showed that the HSI technique has the potential to be used as a rapid

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and accurate inspection tool in processing industry. In subsequent research, Vermeulen et al. (2013)

investigated the transferability of the established HSI procedure (1). In this study, an NIR hyperspectral

line scan camera was employed to collect images for developing the models for determining ergot bodies

in cereals. Discrimination models were constructed by PLSDA (without spectral preprocessing) and

SIMCA (spectra preprocessed by mean centering and SNV) techniques and the wavelength regions

between 1573 and 2399░nm were selected. The models were successfully transferred to commercial HSI

systems and the correlation between reference values that obtained by official stereo-microscopic method

and predicted values offered by multivariate models was higher than 0.94. This study showed the

potential of using HSI technique to screen ergot contaminated cereals automatically at industrial level.

Problems and challenges

Up to now, very few studies on the qualitative or quantitative analysis of ergot or EA in food or feed

based on IR spectroscopy techniques have been reported as compared with other wet chemistry method.

In addition, the handful of available studies employ spectra in the NIR region and mainly focus on the

detection of ergot bodies in grain samples or total EA/ ergovaline in grasses. No published research, to the

authors’ knowledge, focuses on exploring the possibility of using IR based techniques for the rapid

determination of major six EA or other EA in cereals and forages so far.

As a secondary analytical technique, the performance of IR spectroscopy method relies heavily on the

robustness of multivariate models which are developed based on calibration samples. The performance of

multivariate models could be affected by many factors. For instance, low accuracy and poor

reproducibility of reference values obtained with wet chemistry method usually leads to reduced

robustness of IR model; the variability of reference value range has substantial influence on the predictive

ability of IR model, while building a calibration set with a broad range of variability could be a tough

task; sample heterogeneity has negative effects on calibration especially when the spectra are scanned

from intact samples (36, 123).

No study on the limit of direct detection of EA in food and feed samples by IR based techniques has been

reported yet. The concentrations of EA (at ppb or ppm levels) in cereals or grasses are generally several

orders than the content of main chemical components (e.g. protein, fiber, fat, etc.). Therefore, the

difference in IR spectra of contaminated and uncontaminated samples might be caused mainly by changes

in major chemical constituents rather than by variations of mycotoxin content (124). The limited

sensitivity of conventional NIR or MIR methods could be incompetent for the direct determination of EA

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in complex matrixes (40). For instance, the sensitivity limit of conventional NIR spectroscopy for

analyzing most constituents is about 0.1% (125). Thus, it is speculated that the discrimination and

quantitation models for EA as well as other mycotoxins in previous studies might be constructed mainly

based on specific changes in chemical components or other physical alterations which resulted from

mycotoxigenic fungi infection or mycotoxin contamination.

Reliable calibration results only come from appropriately constructed calibration sample sets. The

acquisition of proper samples prior to calibration stage could always be a tough task. The concentration

ranges of EA in feed and food are too large, and the distribution of calibration samples usually unfit for

developing reliable models. In an ongoing mycotoxin project conducted in University of Saskatchewan,

more than 600 feed and food samples were collected and analysed for major EA concentration

(unpublished data). However, it is still unable to obtain a proper sample set for calibration. Take the

wheat sample set for example, 56 of the 80 samples were contaminated by EA with the range of total EA

from below 1 to above 21000 ppb. In addition, 45 samples were co-contaminated with more than one EA.

The histogram plot (Figure 4) of total EA concentration shows the undesirable distribution of these

samples, in which only a handful of samples at higher concentration while most of the samples at lower

concentration. With this highly skewed data set, it might be difficult to obtain reliable calibration results.

Feed and food samples usually could be infected by more than one mycotoxigenic fungi and

contaminated with multiple EA as well as other mycotoxins (10, 11, 13). Under this situation, changes in

chemical constituents and other physical characteristics that related to EA content are even more complex

and the calibration will be more challenging. Besides, the samples itself is another challenging factor

since the small quantity of EA/ergot bodies might spread unevenly in samples.

The development of HSI and microspectroscopy techniques has the potential to improve the detection

sensitivity of IR based methods considerably. While the processing of the complex data hypercube is still

a challenging task (36). Errors related to multiple factors (e.g. sample shape, camera, etc.) could always

be found in images which need to be removed by multivariate image analysis techniques. What’s more,

the reference concentration value for each spectrum at each pixel in an image is almost impossible to be

measured by currently available wet chemistry techniques. In this case, regression models have to be

constructed based on the average value of whole sample.

Outlook and Conclusions

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As a secondary analytical method, IR spectroscopy relies on accurate reference values and the subsequent

calibration models. The establishment of appropriate calibration and validation sample sets is of vital

importance to the chemometrics model construction (126, 127). Most of the previous mycotoxin studies

focus on demonstrating the feasibility of IR based techniques and usually conducted with relatively small

calibration sample sizes and narrow ranges of concentration. Therefore it is important to obtain proper

sample sets that with sufficient sample size, representative concentration, and proper distributions prior to

the calibration step.

Appropriate techniques for spectral outlier detection, preprocessing, and wavelength selection are

extremely important for extracting maximum amount of relevant information for modeling. During the

calibration stage, it is recommended to employ both linear and non-linear regression/classification

techniques for the exploration of all possibilities since the relationships between spectral characteristics

and EA content may be non-linear. It is essential to evaluate the performance of calibration models with a

completely independent validation set (external prediction set) which has not participated in the

calibration step, otherwise the predictive ability of established model might be overestimated (36).

Unfortunately, many previous IR-based calibration studies have been reported without independent

prediction set. To facilitate other researchers to evaluate the accuracy and repeatability of established

multivariate models objectively, statistics of calibration, cross-validation, and external prediction should

be reported in a proper way.

The ergot issues will continue to be great challenges to the health of humans and animals. More

comprehensive efforts should be made to reduce their formation, decrease their toxicity, and minimize

their detrimental effects. Effective strategies could be made only based on the accurate and timely

detection of EA. To make the determination of EA faster, safer and greener, more studies focus on

exploring the feasibility of analyzing EA in food and feed matrixes by different IR techniques, especially

advance molecular spectroscopy techniques, are highly expected in the near future.

Acknowledgements

The authors thank Brian Chelack and Warren Schwab at Prairie Diagnostic Services Inc., College of

Veterinary Medicine, the University of Saskatchewan, Canada, who provided feed samples to the current

project, and also thank Zhiyuan Niu and Na Liu (University of Saskatchewan) for their technical support.

The Ministry of Agriculture Strategic Research Chair (PY) Programs have supported by various grants

from the Natural Sciences and Engineering Research Council of Canada (NSERC-Individual Discovery

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Grant and NSERC-CRD Grant), Saskatchewan Agriculture Strategic Research Program Fund,

Agricultural Development Fund (ADF), SaskMilk, Saskatchewan Forage Network (SNK), Western Grain

Research Foundation (WGRF), SaskPulse Growers, SaskCanola, Prairie Oat Grower Association

(POGA), etc.

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