GHENT UNIVERSITY

FACULTY OF PHARMACEUTICAL SCIENCES

Department of Pharmaceutics

Laboratory for Medicinal Chemistry

Academic year 2009-2010

ERASMUS RESEARCH PROJECT PERFORMED AT THE UNIVERSITY OF CAMERINO, ITALY

DEPARTMENT OF CHEMICAL SCIENCES

SCHOOL OF PHARMACY

Valérie VANHOORNE

First master of Applied Pharmaceutical Sciences

Promoter

Prof. Dr. S. Van Calenbergh

Commissioners

Prof. Dr. S. De Saeger

Prof. Dr. W. Lambert

SPME ANALYSIS OF DIFFERENT POPULATIONS OF EPHEDRA

NEBRODENSIS TINEO EX GUSS SUBSPECIES NEBRODENSIS

GROWING IN ITALY

GHENT UNIVERSITY

FACULTY OF PHARMACEUTICAL SCIENCES

Department of Pharmaceutics

Laboratory for Medicinal Chemistry

Academic year 2009-2010

ERASMUS RESEARCH PROJECT PERFORMED AT THE UNIVERSITY OF CAMERINO, ITALY

DEPARTMENT OF CHEMICAL SCIENCES

SCHOOL OF PHARMACY

Valérie VANHOORNE

First master of Applied Pharmaceutical Sciences

Promoter

Prof. Dr. S. Van Calenbergh

Commissioners

Prof. Dr. S. De Saeger

Prof. Dr. W. Lambert

SPME ANALYSIS OF DIFFERENT POPULATIONS OF EPHEDRA

NEBRODENSIS TINEO EX GUSS SUBSPECIES NEBRODENSIS

GROWING IN ITALY

COPYRIGHT

“The author and the promotor give the authorisation to consult and to copy parts of this

thesis for personal use only. Any other use is limited by the laws of copyright, especially

concerning the obligation to refer to the source whenever results from this thesis are cited.”

May 19, 2010

Promotor Author

Prof. dr. S. Van Calenbergh Valérie Vanhoorne

ACKNOWLEDGEMENT

I am grateful to many people who contributed to the realisation of this thesis.

First of all, I wish to express my gratitude to my promoter, Prof. dr. S. Van Calenbergh, for

offering the possibility to perform these experiments, for letting me experience the

surroundings of an abroad laboratory and for the support I meanwhile received.

Then, I want to thank Prof. dr. S. Vittori, for the extremely warm welcome and trust, and

Prof. dr. F. Maggi, for supervising the progress of my work. Dr. F. Papa, I also want to thank

for the daily guidance in the laboratory and enjoyable collaboration. Further, I am grateful

for the friendship and encouragement of the PhD and thesis students and technical staff of

the laboratory for food chemistry.

Finally, my thanks also goes to my parents, friend and brother who supported me not only

during this thesis but during my entire studies.

1. INTRODUCTION............................................................................................................. 1

1.1. BOTANICAL ASPECTS OF EPHEDRA NEBRODENSIS TINEO EX GUSS SUBSPECIES

NEBRODENSIS......................................................................................................................... 1

1.1.1. Systematics of the genus Ephedra ......................................................................... 1

1.1.2. The phylum Gnetophyta ......................................................................................... 2

1.1.3. The genus Ephedra .................................................................................................. 2

1.1.4. Use in traditional medicine..................................................................................... 3

1.1.5. Ephedra nebrodensis Tineo ex Guss subspecies nebrodensis................................ 4

1.2. PHYTOCHEMISTRY OF THE GENUS EPHEDRA ................................................................. 6

1.2.1. Alkaloids................................................................................................................... 6

1.2.2. Amino acids and derivatives ................................................................................... 7

1.2.2. Phenolic components .............................................................................................. 8

1.2.4. Volatile components ............................................................................................... 9

1.3. TECHNIQUES.................................................................................................................. 11

1.3.1. Solid phase microextraction ................................................................................. 11

1.3.1.1. Vegetable sample preparation issues ............................................................. 11

1.3.1.2. Principle........................................................................................................... 11

1.3.2. Gas chromatography ............................................................................................. 16

1.3.2.1. Flame ionisation detection.............................................................................. 16

1.3.2.2. Mass spectrometric detection ........................................................................ 17

2. OBJECTIVES................................................................................................................. 18

3. MATERIALS AND METHODS......................................................................................... 19

3.1. PLANT MATERIAL AND SAMPLE PREPARATION ............................................................ 19

3.2. OPTIMISATION OF THE SPME METHOD........................................................................ 20

3.3. IDENTIFICATION OF HEADPSPACE VOLATILES .............................................................. 22

3.4. QUANTIFICATION OF HEADSPACE VOLATILES .............................................................. 24

3.5. STATISTICAL ANALYSIS .................................................................................................. 25

4. RESULTS...................................................................................................................... 26

4.1. OPTIMISATION OF THE SPME METHOD........................................................................ 26

4.2. COMPOSITION OF THE HEADSPACE OF E. NEBRODENSIS............................................. 31

5. DISCUSSION ................................................................................................................ 40

5.1. OPTIMISATION OF THE SPME METHOD........................................................................ 40

5.2. THE HEADSPACE COMPOSITION OF E. NEBRODENSIS .................................................. 43

6. CONCLUSIONS............................................................................................................. 47

7. REFERENCES................................................................................................................ 48

LIST OF USED ABBREVIATIONS

CA: Cluster analysis

CAR: Carboxen

DI-SPME: Direct immersion solid phase microextraction

DVB: Divinylbenzene

FID: Flame ionisation detection

GC: Gas chromatography

HS: Headspace

LC: Liquid chromatography

m/z: Mass over charge

MS: Mass spectrometry, mass spectrometer

PDMS: Polydimethylsiloxane

RF: Response factor

SPME: Solid phase microextraction

1

1. INTRODUCTION

1.1. BOTANICAL ASPECTS OF EPHEDRA NEBRODENSIS TINEO EX GUSS SUBSPECIES

NEBRODENSIS.

1.1.1. Systematics of the genus Ephedra

In the kingdom of plants, the subkingdom Embryophyta (Fig. 1.1.) comprises

multicellular eukaryotic plants differentiating from an embryo to a corpus constituted of

roots, stems and leaves. These can be divided into Bryophytes and Tracheophytes, the latter

comprising Spermatophytes and Pteridophytes. Spermatophytes present a significant

reduction in the gametophyte. They include five phyla, one of which are the angiosperms,

the remaining four phyla are grouped into Gymnosperms (Raven, 2005).

Embryophyta

Tracheophyta Bryophyta

Spermatophyta Pteridophyta

Angiosperm

Gymnosperm

Ginkgophyta

Gnetophyta

Coniferophyta

Cycadophyta

Gnetum

Welwitschia

Ephedra

E. nebrodensis

FIGURE 1.1. SCHEMATIC REPRESENTATION OF THE SYSTEMATICS OF E. NEBRODENSIS.

2

Gymnosperms are plants that produce a particular organ, the seed, exposed in open

structures as cones or sporophylls. They are considered to be less complex than the

flowering angiosperms. The seed contains the embryo, protecting it during unfavourable

periods and offering the nutrients needed for the development of new seedlings. The

gymnosperms cover four living phyla, comprising only 840 species, but they dominate

extensive areas, especially in northern regions. Typically, they are woody trees or shrubs

displaying secondary growth. Their ontogenetic cycles are characterised by an alternation of

heteromorphic generations with dominant sporophytes and highly reduced gametophytes

(Raven, 2005).

1.1.2. The phylum Gnetophyta

The phylum Gnetophyta includes 70 species of unusual Gymnosperms belonging to

only three genera: Gnetum, Ephedra and Welwitschia. The species of Gnetophyta are

dioecious with micro- and megasporangia produced on separate plants. The gnetophytes

display great differences, although they are clearly interrelated and properly situated in the

same group (Raven, 2005).

1.1.3. The genus Ephedra

The genus Ephedra includes worldwide about 35 species of perennial, evergreen and

dioecious shrubs. Less often the Ephedraceae include also lianas, creepers and rarely small

trees (Raven, 2005). Their leaves are reduced to sheaths and grow in opposite pairs of triplex

whorls (Wang et al., 2006). As many morphological characters are constant within the genus

and few gene regions have been investigated so far, phylogenetics and classification in

subgroups have been hampered (Pant et al., 1974; Rydin et al., 2006). They are indigenous in

temperate and subtropical regions of Asia, Europe and America, growing in arid and sunny

habitats; sandy soils, rocks, dry mountain sides, even up to 4000 meters in the Andes and

Himalayas (Wang et al., 2006). In Italy the genus Ephedra is represented by six species: E.

distachya L. subsp. distachya, E. fragilis Desf., E. major, E. helvetica C.A. Mey, E. negrii J.

Nouviant and E. nebrodensis Tineo ex Guss subsp. nebrodensis (Conti et al., 2005).

3

1.1.4. Use in traditional medicine

The herb Ephedra, or Ma Huang as the aerial part of the herb is known in traditional

Chinese medicine, is one of the oldest medicinal herbs mentioned in literature. Many recipes

originating from Shokan-zatsubyo-ron, a classical textbook of traditional Chinese medicine

(220 A.C.), are still used for the treatment of various diseases, e.g. bronchial asthma, cold,

diaphoresis, flu, fever, headache, edema, arthralgia and rheumatism and as stimulant or

diuretic (Abourashed et al., 2003; Hayashi et al., 2010). In China and India E. sinica Staph and

E. gerardiana Wall have been used since old times. They are most commonly administered

as a tea. Their sun dried green stems are cut into pieces and boiled in water for half an hour

(Abourashed et al., 2003).

Although the use in traditional medicine of Ephedra is one of the oldest known to

mankind, its revival in the past decade as dietary supplement in the US and Europe causes

controversy. The identity and origin of the alkaloids in herbal preparations is often dubious,

its indication as performance or weight loss enhancer is unproven in double blind

randomised clinical trials and potential health risks are insufficiently researched (Abourashed

et al., 2003). As these herbal preparations are not subjected to the law on drugs, long-term

studies on safety and efficacy have not been thoroughly performed (Wolfe, 2003). A meta-

analysis of randomised clinical trials demonstrated that psychiatric side effects, including

euphoria, insomnia, neurotic behaviour, agitation, depressed mood, anxiety, giddiness,

irritability are 3.64 times as likely to occur in Ephedra users (Maglione et al., 2005). Other

adverse reactions of Ephedra include hypertension, palpitations, tachycardia, stroke,

seizures, permanent disability and even death (Abourashed et al., 2003). Alarmed by these

adverse reactions, the Food and Drug Administration banned all over the counter drugs

containing ephedrine (Maglione et al., 2005).

The anti-anaphylactic potency anciently accredited to the Ephedra herb was confirmed

in recent studies. The herb successfully increased cyclic adenosine monophosphate levels,

inhibiting immunoglobulin E mediated histamine release from mast cells in rats. However

these effects are not established by ephedrines. Possibly flavonoids or other alkaloids

account for the observed inhibition of anaphylaxis (Saito et al., 2004).

In Japanese traditional herbal medicine (Kampo), E. sinica Staph, E. equisetina Bunge

and E. intermedia Schrenk et C.A. Meyer have been used as antitussive, expectorant,

antipyretic, analgesic and bronchodilator agents (Abourashed et al., 2003; Ichikawa et al.,

4

2003; Hayashi et al., 2010). They are still mentioned in the Japanese Pharmacopeia

(fourteenth edition) and Japanese researchers demonstrated hypoglycaemic activity of five

glycans, Ephedrans A-E, isolated from E. distachya L. (Abourashed et al., 2003). In southwest

America the species E. nevadensis S. Watson and E. trifurca Parry were used to brew

“Mormon tea” to treat allergies and colds and as a stimulant. The use of the Ephedra herb

dropped in European medieval medicine although it was well known during the Roman

Empire (Abourashed et al., 2003). No literature assigns medicinal use to E. nebrodensis

(Tammaro, 1984).

Unlike the aerial parts of the Ephedra herb, the underground parts proved to dispose

of hypotensive and antisudorific activities (Tao et al., 2008; Wang et al., 2010).

1.1.5. Ephedra nebrodensis Tineo ex Guss subspecies nebrodensis

E. nebrodensis Tineo ex Guss is an erect or ascending shrub that can reach a height of 2

m with one trunk-like stem. Twigs are rooted opposite or whorled and have a diameter of

0.7 to 1.2 mm. They are green, striate and rigid. The pith of older twigs is reddish- or

blackish-brown (Fig. 1.2.A.). The leaves are 1 to 3 mm in length, scarious and connate for 1/2

to 3/4. The male inflorescence is subglobose with 2 to 4 pairs of flowers. The female

inflorescence is short-pedunculate with only one flower constituted by 2 or 3 pairs of orange

bracts (Fig. 1.2.B.), the upper pair connating for 1/3 to 3/5. The seeds are 1 to 2 mm longer

than the bracts (Freitag and Maier-Stolte, 1993; Christensen, 1997). Nowadays E.

nebrodensis Tineo ex Guss is taxonomically distinguished from E. major Host by the colour of

the pith of older twigs and the inflorescences of female and male species (Christensen, 1997;

Conti et al., 2005). But in the past, these species were confused (Pignatti, 1982; Freitag and

Maier-Stolte, 1993).

5

A B C

FIGURE 1.2. A, E. NEBRODENSIS WITH BROWN STEMS, COLLECTED AT FORCA DI PENNE,

ABRUZZO, ITALY; B, E. NEBRODENSIS WITH ORANGE BRACTS COLLECTED IN CAMERINO,

MARCHE, ITALY; C, DISTRIBUTION OF E. NEBRODENSIS IN ITALY (Pignatti, 1982).

The species is scattered in the Mediterranean area, Anatolia and the western part of

the Himalayas. Two subspecies are differentiated: E. nebrodensis subspecies nebrodensis,

occurring rarely throughout the Mediterranean area but more rarely in the eastern part and

E. nebrodensis subsp. procera, occurring from Dalmatia and Greece through Anatolia to the

Caucasus and the western Himalayas. In Italy E. nebrodensis is found very rarely (Fig. 1.2.C.),

on rocky places up to 1400 meters above sea level, in the regions Sicily, Calabria, Basilicata

and Puglia. More dense populations are found in the Umbro-Marchigian Apennines, The

National Park of Gran Sasso-Monte della Laga in Abruzzo and in the Gennargentu Mountains

in Sardinia (Christensen, 1997). Both subspecies can be distinguished by the twigs and seeds:

whereas E. nebrodensis subsp. nebrodensis demonstrates papillose twigs and ovoid seeds

less than 1.9 times as long as broad, E. nebrodensis subsp. procera has smooth twigs and

oblong-ovoid seeds 2.0-2.7 times as long as broad.

■ ░ E. nebrodensis

6

1.2. PHYTOCHEMISTRY OF THE GENUS EPHEDRA

Secondary metabolites originating from Ephedra species comprise alkaloids, amino

acids and derivatives, volatiles and phenolic compounds (Abourashed et al., 2003). They are

crucial for the survival of the plant and reflect the plant’s adaptations to its habitat. The

divergence in levels and types is exploited as secondary metabolites are used as source for

drugs, food additives, flavours and other industrial materials (Fan et al., 2010). Regarding the

phytochemistry of E. nebrodensis, the total amount of alkaloids has been reported in 1966

(Cottiglia et al., 2005), but recent investigations have been made of the phenolic and volatile

components, respectively by Cottiglia et al. (2005) and Maggi et al. (2010a, in press).

1.2.1. Alkaloids

The aerial parts of several Ephedra species are constituted for 0.02 up to 3.40% of six

optically active alkaloids: (-)-ephedrine (Fig. 1.3.), (+)-pseudoephedrine (Fig. 1.3.), (-)-N-

methylephedrine, (+)-N-methylpseudoephedrine, (-)-norephedrine and (+)-

norpseudoephedrine (Abourashed et al., 2003). Accounting for 30-90% of the total alkaloid

content (Abourashed et al., 2003), (-)-ephedrine, was isolated from the Ephedra herb by Dr.

N. Nagai in 1887 (Kitani et al., 2009). The ephedrines are derived through the

decarboxylation of phenylalanine. The main pharmacologically active ingredients of the

Ephedra herb are considered to be (-)-ephedrine and (+)-pseudoephedrine. As potent

bronchodilators and vasoconstrictors they are used in modern medicine as chemical drugs

for the treatment of bronchial asthma and common cold through their sympaticomimetic

activity. (-)-Ephedrine has both a direct, acting on both α1- and β1,2,3-adrenergic receptors,

and indirect activity, stimulating the release of norepinephrine from sympathetic neurons.

(+)-Pseudoephedrine is an agonist of α- and β2- receptors. Metabolism of the drugs to

catecholamines may account for the stimulatory effects on the central nervous system,

which include suppression of appetite, high metabolic rate of adipose tissue, increased

alertness and improved performance in sports (Abourashed et al., 2003; Andraws et al.,

2005; Hayashi et al., 2010). The ephedrines can be obtained from stems of most Eurasian

Ephedra species, but are more abundant in the Chinese species (e.g. E. sinica, E. equisetina,

E. intermedia) (Andraws et al., 2005), while most American species are believed to be devoid

of them (Caveney et al., 2001).

7

FIGURE 1.3. STRUCTURE OF (-)-EPHEDRINE (LEFT) AND (+)-PSEUDOEPHEDRINE (RIGHT).

Still, the presence of other nitrogenous secondary compounds with neuroactivity in

both Old and New World Ephedra species may explain its worldwide use in traditional

medicine. These include kynurenates and nonprotein amino acids with cyclopropyl ring

structures (1.2.2) (Caveney et al., 2001). Beside the ephedrine alkaloids, ephedroxane, an

oxazolidone derivative of (-)-ephedrine known to act as anti-inflammatory agent, and

macrocyclic spermidines, have been found in some Eurasian Ephedra species (Abourashed et

al., 2003).

1.2.2. Amino acids and derivatives

Kynurenic acid (Fig. 1.4.A.) and several derivatives (6-hydroxykynurenic acid, 6-

methoxykynurenic acid and 7-methoxykynurenic acid) have been found in stem tissue of

many Ephedra species and result from the catabolic metabolism of tryptophan. Although

other derivatives of tryptophan are known for their role in the chemical defence of plants

and fungi against herbivores, no pharmacological activity has so far been attributed to the

plant kynurenates. Still, kynurenic acid, structurally related to the potent inhibitor of

bacterial DNA-gyrase, quinoline-3-carboxylic acid, was reported to dispose of moderate

antimicrobial activity against several Gram-positive and Gram-negative bacteria. In mammals

they also operate as endogenous antagonists on the N-methyl-D-aspartate-type glutamate

receptor in the central nervous system (Caveney, 2001).

The cyclopropyl amino acids found in Ephedra are analogues of L-glutamate and L-

proline, important components of the cell metabolism. For example, (2S, 3S, 4R)-2-

(carboxycyclopropyl)-glycine (Fig. 1.4.B.) present in some Ephedra species is a potent blocker

of the high-affinity sodium-dependent glutamate uptake receptor in the mammalian central

nervous system (Caveney et al., 2001). Moreover, they are a characteristic feature of the

Ephedraceae, as cyclopropyl amino acids are found in only two more families, belonging to

8

the angiosperms. Several other L-2-(carboxycyclopropyl)glycines and methanoprolines

widely occur in young stems, seeds and bracts. Their neurotoxicity may suggest a defence

function against herbivores or fungal and microbial attack.

A B

FIGURE 1.4. A, KYNURENIC ACID; B, (2S,3S,4R)-2-(CARBOXYCYCLOPROPYL)-GLYCINE.

In addition to the cyclopropyl amino acids, common amino acids (L-glutamate, L-

glutamine, L-serine, L-proline) are present in young stems, seeds and bracts, and L-tyrosine

betaine is present in the roots (Caveney, 2001; Abourashed, 2003).

1.2.3. Phenolic components

Several tannins and their precursors (flavanols) are present in large amounts in

Ephedra stems and roots. However, the hydrolysable tannins, typically present in

angiosperms, are missing. The tannin deposits often induce a brown colour of the stem pith,

which is useful in the identification of the species (Caveney, 2001).

Concerning E. nebrodensis, researchers of the Sardinian University of Cagliari were able

to isolate the phenolic glycosides o-coumaric acid glucoside (Fig. 1.5.), 4-hydroxy-3-(3-

methyl-2-butenyl)phenyl β-D-glucopyranoside and o-coumaric acid β-D-allopyranoside

(Cottiglia et al., 2005).

FIGURE 1.5. O-COUMARIC ACID GLUCOSIDE

9

1.2.4. Volatile components

The volatile constituents of E. sinica, one of the most important medicinal herbs

worldwide, have been studied by means of various extraction techniques including steam

distillation-solid phase microextraction (Tellez et. al, 2004), hydrodistillation (Wang et. al,

2006) and supercritical CO2 fluid extraction (Wang et al., 2010).

The method combing continuous hydrodistillation of aerial parts with concurrent solid-

phase microextraction (SPME), was developed to authenticate the presence of E. sinica in

ground plant material. Studying 21 species, p-vinylanisole (3.3%) (Fig. 1.6.B.) was reported as

a marker compound for E. sinica. The main constituents of the essential oil proved to be α-

terpineol (13.2%) (Fig. 1.6.A.), tetramethylpyrazine (7.4%) (Fig. 1.6.C.) and 3-methyl-2-buten-

1-ol (5.2%) (Tellez et al., 2004). Examination of the hydrodistilled essential oil derived from

aerial parts of six E. sinica species indicated α-terpineol (19.28-52.23%) (Fig. 1.6.A), p-

vinylanisole (0.59-11.64%) (Fig. 1.6.B), 2,3,5,6-tetramethylpyrazine (0.63-8.99%) (Fig. 1.6.C),

3-methyl-2-buten-1-ol (0-5.44%), terpinen-4-ol (1.17-4.37%), α-linalool (1.62-5.15%), phytol

(1.24-15.73%), γ-eudesmol (0-7.77%) and eudesm-7(11)-en-4-ol (0.41-6.13) as main

components (Wang et al., 2006).

A B C

FIGURE 1.6. STRUCTURES OF COMPONENTS OF E. SINICA (TELLEZ ET AL., 2004): A, α-

TERPINEOL; B, p-VINYLANISOLE; C, TETRAMETHYLPYRAZINE.

Finally, also supercritical CO2 fluid extraction has been applied to aerial parts of E.

sinica, revealing n-hexadecanoic acid (24.04%), linolenic acid (21.29%), linoleic acid (10.72%),

3,7,11,15-tetramethyl-2-hexadecen-1-ol (9.72%) and cinnamic acid (6.12%) (Wang et al.,

2010) as the major constituents.

Regarding the Ephedra species growing in Italy, the essential oil steam distilled from E.

distachya, E. fragilis and E. major (Kobaisy et. al, 2005) and more recently also hydrodistilled

from E. nebrodensis were studied (Maggi et al., 2010a, in press), revealing qualitative and

N

N

O

OH

10

quantitative differences (Table 1.1.; Fig. 1.7.) between the Italian species. The abundant

presence of citronellol (Fig. 1.8.) in E. nebrodensis proved to be possibly useful as

chemotaxonomic marker (Maggi et al., 2010a, in press) to distinguish the species from E.

major.

TABLE 1.1. MAJOR COMPONENTS WITH RELATIVE AMOUNTS IN THE ESSENTIAL OILS OF E.

DISTACHYA, E. FRAGILIS AND E. MAJOR (AFTER STEAM DISTILLATION) AND E. NEBRODENSIS

(AFTER HYDRODISTILLATION) (KOBAISY ET AL., 2005; MAGGI ET AL.,2010a, IN PRESS).

E. distachya E. fragilis E. major E. nebrodensis

ethyl benzoate (46.9%) (E)

-phytol (10.1%)

α-terpineol (3.7%) citronellol (29.67%)

benzaldehyde (8.0%) 6,10,14-trimethyl-

2-pentadecanone (5.3%) eugenol (4.3%) ethyl hexadecanoate (9.5%)

cis-calamene (3.6%) pentacosane (5.2%) methyl linoleate (3.5%) (Z)-3-hexenyl benzoate

(4.4%)

cis-thujopsene (3.5%)

α-terpineol (3.0%)

0

5

10

15

20

25

30

35

E. distachya E. fragilis E. major E. nebrodensis

Ephedra species

rela

tiv

e c

on

ten

t o

f te

rpe

ne

s (%

)

monoterpene hydrocarbons

oxygenated monoterpenes

sesquiterpene hydrocarbons

oxygenated sesquiterpenes

diterpene hydrocarbons

oxygenated diterpenes

FIGURE 1.7. TERPENOID PROFILE FOUND IN THE ESSENTIAL OILS OF E. DISTACHYA, E.

FRAGILIS AND E. MAJOR (AFTER STEAM DISTILLATION) AND E. NEBRODENSIS (AFTER

HYDRODISTILLATION) (KOBAISY ET AL., 2005; MAGGI ET AL., 2010a, IN PRESS).

FIGURE 1.8. STRUCTURE OF CITRONELLOL, THE MAIN CONSTITUENT FOUND IN ESSENTIAL

OILS OF E. NEBRODENSIS AND USEFUL AS CHEMOTAXONOMIC MARKER (MAGGI ET AL.,

2010a, IN PRESS).

11

1.3. TECHNIQUES

1.3.1. Solid phase microextraction

1.3.1.1. Vegetable sample preparation issues

Most analytical methods developed for the analysis of vegetable matrices require an

appropriate sample preparation as matrix compounds, often present in large quantities, can

influence the analysis or give instrumental problems and must therefore be removed

before the final analysis. Generally, more than 80% of the total analysis time is spent on

sampling, including extraction, concentration, fractionation and isolation of the analytes.

Therefore, the choice of a suitable sample preparation method is crucial. After sample

preparation is executed, the separation and detection of analytes in vegetable matrices is

mostly performed by gas chromatography (GC) or liquid chromatography (LC) in

combination with mass spectrometry (MS) (Kataoka et al., 2000).

Several sample preparation methods, including steam distillation, liquid-liquid

extraction with organic solvents, surfactants and supercritical fluids and solid-phase

extraction have been developed for the analysis of complex vegetable matrices. Some

important disadvantages are inherent to these established methods, such as the

requirement of large volumes of samples and solvents and their time-consuming character

(Kataoka et al., 2000). In comparison to liquid liquid extraction, the amount of solvents used

in solid phase extraction is reduced; still, for most applications, it requires concentration of

the analytes which may result in loss of volatiles and it entails adsorption of the analytes to

the sampling tools. Furthermore, multi-step procedures are susceptible to loss of analytes.

Solvent-free, faster and less laborious sample preparation techniques include headspace

(HS) and purge-and-trap. But these methods are subjected to other possible drawbacks;

direct HS is limited to the analysis of analytes present in higher concentrations whereas

purge-and-trap can result in loss of analytes. In this context, the SPME-technique was

developed by Pawliszyn and co-workers in 1990 (Kataoka et al., 2000).

1.3.1.2. Principle

Solid phase microextraction is a sample preparation technique based on the

establishment of an equilibrium between a fused-silica fibre, coated with an appropriate

stationary phase, and a sample matrix (Kataoka, 2000). As this technique combines

sampling, extraction and concentration, all extracted volatiles are transferred to the

12

analytical system for separation and detection. SPME approaches a hypothetical ideal

extraction method characterised by low detection limits, rapidity, solvent elimination, high

sensitivity, low costs, compatibility with a wide variety of detection methods, automation,

simplicity in use, suitability for on-site analysis and process monitoring. Especially for the

extraction of volatile organic compounds vulnerable to processes of thermal decomposition,

oxidation, photolysis, etc., from environmental, biological and food samples, this method

has proven to be useful in routine analyses, coupled to GC or LC (Kataoka et al., 2000).

The SPME device (Fig. 1.9.) consists of fused silica fibre coated with a polymeric

stationary phase in a fibre attachment tubing. A hollow septum-piercing needle surrounds

this assembly and allows withdrawal of the needle, protecting it when not in use (Mills and

Walker, 2000).

FIGURE 1.9. SPME DEVICE WITH DETAIL OF FIBRE AND PROTECTIVE NEEDLE (KATAOKA ET

AL., 2000).

Two types of fibre SPME techniques can be distinguished: headspace (HS)-SPME and

direct immersion (DI)-SPME. HS-SPME (Fig. 1.10.A.) is applied to gaseous, liquid or solid

samples and the fibre is exposed to the head space above the sample. The fibre has a longer

lifetime as it is not directly exposed to the complex matrix. Taking advantage of their ability

to vaporise either spontaneously or under suitable sampling conditions, it is the most

appropriate mode for the GC-FID and GC-MS analysis of volatiles in complex matrices. DI-

SPME (Fig. 1.10.B.) is more sensitive for analytes present in liquid samples in which the fibre

13

is directly immersed. Coupled to LC-MS, it is suitable for thermally labile compounds or less-

volatile analytes. Interference from compounds in relatively high concentrations is

encountered in both sampling modes (Kataoka et al., 2000).

FIGURE 1.10. EXTRACTION PROCESS BY HEADSPACE (A) AND DIRECT IMMERSION (B) SPME,

WITH DESORPTION SYSTEMS FOR GC AND HPLC ANALYSES (KATAOKA ET AL., 2000).

The analytes are preferentially concentrated on the fibre’s coating by adsorption or

absorption. The extraction is based on absorption when using a polydimethylsiloxane

(PDMS) fibre coating. The analytes dissolve and partition onto the extraction phase and

diffuse into the bulk of the coating (Mills and Walker, 2000). Absorption materials, covering

a wide range of polarities, are to be developed for the recovery of polar components from

solid matrices as for now only apolar PDMS-coatings are routinely used (Bicchi et al., 2008).

The extraction is based on adsorption to the coating’s surface when using solid sorbents with

defined crystalline structures, as divinylbenzene (DVB) or carboxen (CAR), dissolved in PDMS

(Mills and Walker, 2000). Both adsorbent phases contain internal micro- and mesopores,

trapping analytes, and macropores on the surface of the material, generally retaining larger

analytes through hydrogen bonding or Van der Waals interactions. The less polar CAR and

14

more polar DVB phases have a similar surface area, but the CAR coating has a higher

percentage of micropores (http://www.sigmaaldrich.com/analytical-chromatography/

literature.html).

The selectivity of the extraction is determined by the type of fibre; generally, polar

fibres are applied to polar analytes and non-polar fibres to non-polar analytes. The thickness

of the fibre affects both the equilibration time and sensitivity. In general, thick coats require

longer equilibration times but a higher amount of analytes can be extracted, generating a

higher sensitivity (Kataoka et al., 2000).

SPME reaches a maximum sensitivity at equilibrium but, as models to describe mass

transfer in non-equilibrium stages are available, full equilibration should not be essential for

accurate and precise analysis. However, the knowledge of non-equilibrium sampling still

needs to be expanded (Bicchi et al., 2008). The volatiles are distributed in a three-phase

system, including the matrix phase, the fibre phase and the headspace above the matrix,

during HS-SPME extraction. The number of moles extracted by DI-SPME to the coating at

equilibrium (n) is calculated from the following equation, which is limited to the use of

absorbent coatings or adsorbent coatings exposed to low analyte concentrations (Lord and

Pawliszyn, 2000).

sffs

sffs

VVK

CVVKn

+= 0

Where: n: number of moles extracted by the coating (mole)

Kfs: distribution constant between fibre coating and sample matrix

Vf: fibre coating volume (l), Vs: sample volume (l)

C0: initial concentration of one analyte in the sample (mole/l)

Using solid sorbents the equilibrium amount extracted can vary with concentrations of

sample constituents occupying a substantial sorbent area, for only a limited surface is

available for adsorption. This displacement effect can be overcome by non-equilibrium

sampling (Lord and Pawliszyn, 2000). Coupling of optimised non-equilibrium SPME and fast

GC is a promising method for in-vivo monitoring of the volatile fraction of a plant and

clarification of biological phenomena such as reproductive processes, defence against

predators and intra-species communication (Bicchi et al., 2008).

Several parameters have to be rigorously standardised across runs. The extraction

efficiency is not only affected by the distribution constant, the thickness and type of coating,

15

the sample volume, vial size and concentration of the analyte in the sample but also by

extraction time, agitation, addition of salt, pH and temperature. Extraction time is

dependent from the distribution coefficient of the analyte between the sample and the

coating and the conditions used. Agitation, sonication and fibre vibration accelerate the

transport of analytes from the sample matrix to the coated core. Generally, increasing

agitation diminishes equilibration time, but it also tends to diminish the robustness of the

system. Operating in HS mode, addition of soluble salts is preferred to agitation because of

potential damage of the fibre coating during agitation. The observed amelioration of the

extraction time is due to the salting out effect. As pH alters the ionisation, it also affects the

extraction. For SPME of acidic compounds, acids are added to the matrix which is alkalinised

for basic compounds. Temperature significantly influences the extraction by affecting the

vapour pressure. An increase in extraction temperature, and consequent shorter equilibrium

time, also implements a decrease in the distribution constant, resulting in a lower amount of

extracted analyte (Kataoka et al., 2000). In the HS-SPME-mode, heating of the sample and

cooling of the fibre can increase analyte concentration at equilibrium (Lord and Pawliszyn,

2000). In order to attain a more selective extraction and subsequent GC detection,

derivatisation can be implemented in combination with SPME. The analytes, especially polar

compounds of complex matrices that are difficult to extract and to separate with GC, can be

derivatised either in the matrix, on the fibre or in the injector (Lord and Pawliszyn, 2000).

Extraction of the volatiles is immediately followed by desorption of the volatiles from

the fibre by exposure to a GC-injector or a desorption chamber when the separation is

performed with LC. A narrow-bore GC-injector with inside diameter approaching the outside

diameter of the protective shear of the SPME fibre is required. Since the extraction phase

used in SPME is non-volatile and only extracted volatiles are transferred to the GC, the

interface of SPME to a GC is convenient. Splitless injectors are preferred in order to realise a

maximal sensitivity (Mills and Walker, 2000). Parameters influencing the efficiency of

desorption of the analytes include exposure time and injector temperature, thickness of the

fibre coating and injection depth (Lord and Pawliszyn, 2000).

HS-SPME delivers samples that are representative of the headspace volatiles

characterising the complex matrices. An exhaustive extraction is not pursued by SPME. Thus

for absolute quantification purposes calibration is necessary (Mills and Walker, 2000).

16

1.3.2. Gas chromatography

Gas chromatography is an analytical technique based on the partitioning of a

compound between the liquid or solid stationary phase of a column and a mobile gas phase,

the stationary and mobile phases being not miscible. Compounds interact variously with the

stationary phase and are separated one from another depending on their relative vapour

pressures and affinities for the stationary phase. This distribution is described by the

distribution coefficient KD, defined as the ratio of concentration of solute in the stationary

phase to the concentration in the mobile phase. KD is an equilibrium constant dependent on

the solute, stationary phase and temperature. The solutes can only migrate through the

column when present in the gaseous mobile phase and are thus separated (Wittkowski and

Mattisek, 1990). A gas chromatograph consists of a heated injector volatilising the sample, a

column separating the compounds of a mixture and a detector. After injection in the inlet

port, the sample is transferred through the column and into the detector by means of an

inert carrier gas under pressure. Both universal (e.g. flame ionisation detector), and selective

detectors (e.g. MS) can be hyphenated to the GC (Kitson et al., 1996). GC analysis can be

applied to compounds that are sufficiently volatile and do not decompose at the imposed

column temperature. Both qualitative and quantitative information can be provided. Great

advantages of this technique include rapid separation of complex mixtures, high resolution,

robustness, high sensitivity and accuracy (Mc Nair and Miller, 1998; Grob and Barry, 2004).

1.3.2.1. Flame ionisation detection

The flame ionisation detector (FID) is the most widely used detector system with GC.

The column effluent passes through a jet with an air/H2 flame burning at the end generating

a mixture of ions from organic constituents of the effluent. The ions are attracted to a

collector electrode and the resulting current is amplified and fed to the potentiometric

recorder. The FID is a mass-sensitive and universal detector, but responds only to organic

compounds that burn in the flame. The response factors per carbon atom are constant for

hydrocarbons as the organic solute is converted to methane in the FID combustion process

(Fowlis, 1995). The FID-response decreases in the presence of heteroatoms and halogens.

FID is not sensitive to compounds that contain no organic carbons such as water, carbon

monoxide, formaldehyde etc. This detection technique is suitable for accurate quantitative

17

analysis as it disposes of a wide linear dynamic range and low limits of detection (Grob and

Barry, 2004).

1.3.2.2. Mass spectrometric detection

GC is an excellent analytical technique for the separation of volatiles but,

unfortunately, GC data alone do not allow revealing the identity of the separated

compounds. Retention times are related to the distribution coefficient but are not unique.

MS, on the other hand, provides data for both identification and quantification to ppb level.

As such it is often hyphenated to GC (Mc Nair and Miller, 1998). MS analysis is performed

under high vacuum conditions and consists of ionisation and fragmentation of the

molecules, separation according to the ratio of mass over charge (m/z), detection of the ions

and their abundance by an ion multiplier and processing of the data to supply a mass

spectrum. Since the same requirements (volatile and thermally stable compounds) are

needed as for compounds to be amenable to GC, the ionisation and fragmentation was

performed in electron impact mode in our study. This ionisation technique consists of a

heated wire filament producing electrons. These are accelerated towards an anode and

collide with the sample introduced in a direction perpendicular to the electron beam. The

sample is ionised by the beam of electrons and additionally fragmented if the energy of the

electron beam is greater than the ionisation energy of the sample. For 10 eV is sufficient to

ionise most organic molecules, extensive fragmentation is generated by the excess of

energy. Separation of the generated ions can be performed with a single quadrupole

consisting of four parallel rods of circular section. The rods are positioned symmetrically

around the passage of the ions and the opposite rods are connected electrically. The rods

dispose of opposite potentials resultant of a constant and radiofrequent voltage. They are

varied so to keep the ratio of continuous to alternating voltage constant. Ions disposing of a

wave synchronous to the radiofrequency at a given ratio of radiofrequent and constant

voltage, will reach the electron multiplier for detection (de Hoffmann and Stroobant, 2007).

The mass spectrum can be considered as the fingerprint of a molecule as the derived

ions and their abundance characterise the analyte’s structure, thus making MS a nearly

indispensable technique for identification and structure elucidation (Grob and Barry, 2004).

18

2. OBJECTIVES

Three main objectives were defined in this work: the development of an analytical

method based on SPME analysis of volatile compounds that may be used for further support

of the botanical classification of E. nebrodensis, the application of the method to volatiles

originating from six different Italian populations of E. nebrodensis to gain knowledge of the

metabolomic differences, and the search for possible pharmaceutically and cosmetically

interesting volatile compounds.

In the past, E. nebrodensis and E. major occurring in Italy were confused (Pignatti,

1982; Freitag and Maier-Stolte, 1993), but nowadays these species are taxonomically

distinguished (Christensen, 1997; Conti et al., 2005). Recent work by Maggi et al. (2010a, in

press) chemically supported this botanical classification by comparison of the essential oil

composition of E. nebrodensis with that of E. major, previously reported by Kobaisy et al.

(2005). Continuing the efforts of Maggi et al. (2010a, in press), a SPME method coupled to

GC-FID and GC-MS analysis was developed, applied to E. nebrodensis, in an attempt to

further contribute to the classification of the species. Classification of a plant matrix can be

performed using the SPME volatile profile if provided with an appropriate statistical analysis

(e.g. principal component analysis) (Bicchi et al., 2008) and has been executed by Miller et al.

(1996) to establish the botanical origin of commercially available cinnamon. In comparison

with other extraction techniques, SPME permits not only to collect considerably smaller

amounts of this plant, threatened by extinction in Italy, but may also allow obtaining a

volatile profile more closely approaching the real composition of the plant as the sample is

not exposed to thermal and oxidative stress during the extraction process.

Subsequently, the developed method was used to examine if the volatile profile of E.

nebrodensis is influenced by its geographic origin through qualitative and quantitative

comparison of the volatiles of six populations living in three regions and covering almost the

entire Italian areal of the species (Fig. 1.6.) (Pignatti, 1982).

As mentioned above, E. nebrodensis is threatened by extinction. Thus, a thorough

chemical analysis of this plant should be undertaken straight away. Other members of the

Ephedra genus have been used in Chinese traditional medicine (Abourashed et al., 2003) and

the alkaloid content of E. nebrodensis has already been studied (Cottiglia et al., 2005), but

using SPME-GC-MS other highly interesting phytochemicals may be identified among the

volatile fraction.

19

3. MATERIALS AND METHODS

3.1. PLANT MATERIAL AND SAMPLE PREPARATION

Aerial parts of E. nebrodensis subsp. nebrodensis, including young green stems with

leaves reduced up to sheaths, were collected in May, June and September 2007 and May

2008 in six different places (Fig. 3.1.) constituted by cliffy lime stones and rocks belonging to

Marche, Abruzzo and Sardinia and covering the Italian areal of the species (Pignatti, 1982).

All plants were growing at an altitude ranging from 600 to 1100 m above the sea level (Table

3.1.). Voucher specimens were morphologically identified using available literature and

deposited in the Herbarium Camerinensis (CAME) (Fig. 3.2.) and in the Herbarium of Centro

Ricerche Floristiche dell’Appennino (APP), both included in the online edition of Index

Herbariorum: http://sweetgum.nybg.org/ih/) (Holmgren and Holmgren, 1998) of University

of Camerino, Italy. Sardinian specimens were authenticated and deposited in the Herbarium

of Botanical Sciences of the University of Cagliari, Italy.

TABLE 3.1. GEOGRAPHIC AND BOTANICAL CHARACTERSTICS OF THE E. NEBRODENSIS

SAMPLES STUDIED.

Sample Locality Region of

collection

Collection

date

GPS

coordinates

Altitude

(m)

Plant

material

(g)

Voucher

codesa

1

Camerino,

Madonna di Val

Povera

Marche 05/06/2007 43°06’33” N

13°00’06” E 851 0.933 CAME 9586

2 Visso, Val Nerina Marche 09/09/2007 42°56’06” N

13°05’43”E 721 5.163 CAME 23633

3

Pietra Fracida,

Monte scarafano,

Forca di Penne

Abruzzo 16/05/2008 42°17’34” N

13°49’59” E 1100 5.083 APP 37431

4 Ofena, Monte la

Serra Abruzzo 16/05/2008

42°19’42” N

13°45’48” E 620 0.763 APP 25033

5 Orgosolo Sardinia 02/05/2007 40°11’54” N

9°20’47” E 692 0.293 CAGL

6 Gola Gorropu Sardinia 02/05/2007 40°10’59” N

9°29’59” E 621 0.783 CAGL

aAccession number in: CAME, Herbarium Camerinensis, School of Environmental Sciences, University of Camerino,

Camerino, Italy; APP, Herbarium of Centro Ricerche Floristiche dell’Appennino, Barisciano, Italy; CAGL, Herbarium of

University of Cagliari, accession numbers are unknown.

20

FIGURE 3.1. DISTRIBUTION OF THE SIX

DIFFERENT POPULATIONS OF E.

NEBRODENSIS INVESTIGATED (SEE TABLE

3.1. FOR DESCRIPTION OF THE

NUMBERED SITES).

FIGURE 3.2. VOUCHER SPECIMEN

REPRESENTING ONE MARCHIGIAN

POPULATION OF EPHEDRA

NEBRODENSIS STUDIED.

Because E. nebrodensis lives only in impervious places constituted by rocks and cliffy

limestones, therefore threatened of reduction in number and density of population, only a

0,293 to 5,163 grams of plant material were collected in each collection site, taking into

consideration the high capacity of SPME to analyse considerably smaller amounts of plant

material than other extraction techniques. The plant material was stored at ambient room

temperature in the dark until completely dry, then was ground using a blender MFC model

DCFH 48 IKA-WERK (Staufen, Germany) equipped with sieves of 1 mm of exclusion limit. A

sample of 30.0 mg was accurately weighted on a E425-B Gibertini balance (Novate, Italy),

then put in a 4 ml vial closed with a polypropylene cap and PTFE/silicone septum (Supelco,

Bellefonte, PA, USA).

3.2. OPTIMISATION OF THE SPME METHOD

Sample 1 was used for the optimisation. The following SPME parameters were

optimised with GC-FID in order to select the best ones in terms of reproducibility and

1 2

3 4

5 6

21

extraction efficiency of volatiles of E. nebrodensis: fibre coating, sample amount,

temperature, extraction time and added amount of water. All analyses were executed in

triplicate. A blank analysis was performed at the beginning of each day to assure that the

fibre was free of impurities and residues as well as after each run to check for carry-over.

A B C

FIGURE 3.3. SPME (A) WITH DETAIL OF THE EXPOSED FIBRE (B) AND DESORPTION IN

THE INJECTOR OF THE GC (C).

The extraction was performed by piercing the septum of the vial with a needle,

followed by inserting the protective shear of the SPME fibre in the aperture and lowering the

SPME fibre into the vial, 1 cm above the powdered plant material. The fibres, fibre

assemblies and manual SPME holder were supplied by Supelco (Bellefonte, PA, USA). The

emplacement of the SPME extraction is depicted in Fig. 3.3.A. and Fig. 3.3.B.

The vial was immersed in a water or oil bath (Heidolph, Schwabach, Germany), whose

temperature was monitored with a contact thermometer. The volatiles were extracted for a

well defined time by exposing the SPME fibre to the headspace of the sampling vial.

Consequently, the SPME fibre was withdrawn in its protective needle to exit the vial. The

desorption was performed immediately afterwards by inserting the fibre in the septum of

the GC injector with a SPME inlet liner (0.75 mm internal diameter) (Supelco, Bellefonte, PA,

USA) in splitless mode. Exposure of the fibre to the injector temperature of 250 °C for 3 min

desorbed the extracted compounds into the column (Fig. 3.3.C.).

Three fibres varying in polarity and retention capacity, i.e. polydimethylsiloxane

(PDMS, 100 µm), carboxenTM

-polydimethylsiloxane (CAR/PDMS, 75 µm) and

polydimethylsiloxane-divinylbenzene (PDMS/DVB, 65 µm) were evaluated. The coating of all

fibres was 1 cm long. The fibre screening was conducted under the following experimental

conditions: extraction temperature: 60°C; extraction time: 30 min; amount of plant material:

22

30.0 mg. Analysis of samples extracted with the PDMS fibre at 20°C, 40°C, 60°C and 80°C,

with an extraction time of 30 min and sample amount of 30.0 mg, was performed to choose

the optimal extraction temperature. Extraction time screening included exposure of the

PDMS fibre to 30.0 mg of sample during 10, 20, 30 and 60 min in an oil bath at 60°C.

Exposure of the PDMS fibre to 10.0 mg, 30.0 mg and 60.0 mg of sample under the following

experimental conditions: extraction temperature: 60°C; extraction time: 30 min, was

executed to select the appropriate sample amount. Finally, the influence of the addition of

water to the sample was evaluated as water has previously proven to aid the extraction of

analytes from the matrix (Lord and Pawliszyn, 2000). Thus, 20, 40 and 60 μl of distilled water

were added (obtained from a Milli-Q SP Reagent Water System, Millipore, Bedford, MA,

USA) with a micropipette (Nichiryo, Koshigaya-City, Japan) to 30.0 mg of dry plant material

and extracting for 30 min in an oil bath at 60°C with the PDMS fibre.

Separation and detection of volatiles were performed with a gas chromatograph

Agilent 4890D (Agilent Technologies, Santa Clara, CA, USA) coupled with a flame ionization

detector (Agilent Technologies, Santa Clara, CA, USA) and equipped with a 25 m long HP-5

capillary column (5% phenylmethylpolysiloxane, 95% methylpolysiloxane) with an internal

diameter of 0.32 mm and 0.17 µm film thickness (J & W Scientific, Folsom, CA, USA). The

oven temperature program was set up as follows: 3 min at 60°C, subsequently raised with

10°C/min up to 220°C and finally with 20°C/min up to 280°C and held for 20 min. The

detector temperature was set at 280°C. The run time was 42 min. The carrier gas used was

helium with a flow of 1.96 ml/min. The data were collected by using HP3398A GC

Chemstation software (Hewlett Packard, Rev. A.01.01).

3.3. IDENTIFICATION OF HEADPSPACE VOLATILES

The above described (3.2.) SPME conditions with PDMS fibre were adopted for all six

Ephedra samples: extraction time, 30 min; temperature, 60°C; sample amount, 30.0 mg. The

desorption and detection was performed on an Agilent 6890N (Agilent Technologies, Santa

Clara, CA, USA) gas chromatograph coupled to a 5973N mass spectrometer (Agilent

Technologies, Santa Clara, CA, USA). This instrument has an electron impact source with

single quadrupole and electron multiplier detection. The GC-column was a 30 m long HP5-

MS capillary column with an internal diameter of 0.25 mm and 0.1 μm of film thickness (J &

W Scientific, Folsom, CA, USA). The following temperature program was set up: 3 min at

23

60°C, subsequently raised with 10°C/min up to 220°C and finally with 20°C/min up to 280°C,

held for 20 min. Injector and detector temperatures were respectively 250°C and 280°C. The

carrier gas used was helium with a flow of 1.0 ml/min in splitless mode. The ionisation

voltage was set at 70 eV. A blank run was performed between each analysis.

A mixture of linear alkanes (C7-C30) (standards bought from Sigma Aldrich S.r.l., Milan,

Italy) diluted in hexane at 25 mg/ml was prepared. A vial, filled with 10 μl of the standard

mixture, was evaporated under a N2 flow for a couple of seconds. The mixture was

consequently loaded onto the SPME fibre and injected into the GC-MS injector under the

above conditions to calculate the retention indexes (as Kovats indexes) of each extracted

compound.

The Kovats indexes for n-alkanes are defined as 100 times the number of carbon atoms

they comprise. The Kovats index for the unknown compound was calculated according to the

following equation:

(n)t(N)t

(n)t(A)tnI

rr

rr

−−+= 100100

Where: I: Kovats index for compound A,

n, number of carbon atoms of the n-alkane that precedes compound A,

A, compound with unknown retention index,

tr(A), tr(n), tr(N): retention times of respectively the compound with unknown retention index, the preceding n-

alkane and the following n-alkane

The peak identification was based on computer matching with the WILEY 275, NIST 08

and home-made (based on the analyses of reference oils and commercially available

standards) libraries, and ADAMS (2007) library, taking into account the coherence of the

retention indexes and mass spectra of the analysed compounds with those reported in the

libraries. The MS-spectra included in the reference libraries NIST 08, WILEY 275 and Adams

have been recorded under different operative conditions, consequently the identification of

a component was carefully considered. The Adams library (2007) includes MS-spectra of

common constituents of plant essential oils. Our chromatographic data were analysed by

using MSD ChemStation software, Version G1701DA D.01.00 (Agilent Technologies, Santa

Clara, CA, USA).

24

3.4. QUANTIFICATION OF HEADSPACE VOLATILES

The SPME and GC-FID detection were performed in triplicate for all six samples as

described above (3.2.). The extraction with PDMS fibre was characterised by the following

experimental conditions: extraction time: 30 min; temperature: 60°C; sample amount: 30.0

mg. A blank analysis was performed at the beginning of each day and between samples of

different collection sites. The obtained data were collected and processed (integration was

performed manually) with HP3398A GC Chemstation software (Hewlett Packard, Rev.

A.01.01).

The relative amounts of volatile components, expressed as percentages, were

obtained by FID peak area internal normalisation. Due to the complexity of the volatile

mixture and the impossibility (unavailability from one side, price of reference compounds

from another) to purchase commercial standards for all the volatile components released

from the matrix, it was impossible to run standards for all identified peaks in order to

calculate the individual response factors (RF) needed for internal normalisation. Therefore, a

method previously used for calculation of the RF for FID of ten chemical classes (Table 3.2.)

was used (Maggi et al., 2010b) and is here briefly described.

The individual compounds belonging to the same class, were presumed to have a

comparable RF, estimated by running a standard for each class, or if possible multiple

standards, to ensure sufficient reliability. These standards were purchased from Sigma

Aldrich (Milan, Italy). Each standard was dissolved in hexane (Carlo Erba, Milan, Italy) and

spiked with the internal standards octane and octadecane (Sigma Aldrich, Milan, Italy) at the

same concentration (0.16 mg/ml). The following concentrations of standards were used:

0.04 mg/ml, 0.08 mg/ml, 0.16 mg/ml and 0.40 mg/ml. Subsequently, aliquots of 1 µl of these

mixtures were directly injected in the GC-FID with the following temperature program: 5

min at 60 °C, subsequently raised with 4 °C/min up to 220 °C and finally with 11 °C/min up to

280 °C and held for 15 min. Regression lines were constructed with the ratio of the FID peak

area of the internal standard to that of the representative standard as a function of the ratio

of the concentration of the internal standard to that of the representative standard. The

slopes of these regression lines were defined as the RF of the representative standard. For

each class, the RF was chosen as the mean of the slopes of the different representative

standards and used for the FID peak area internal normalisation. Table 3.2. summarises the

25

RF of the distinguished chemical classes and the representative compounds used to calculate

them.

A RF equal to one was used for the quantification of the unidentified compounds and

those that could not be classified in one of the distinguished classes.

TABLE 3.2. SUMMARY OF THE RF AND THE REPRESENTATIVE COMPOUNDS OF THE

CHEMICAL CLASSES

chemical class representative compounds RF

alcohols 1-octen-3-ol 1.5

aldehydes and ketones dodecanal, octanal 1.7

alkanes octane, octadecane 1.2

aromatics benzaldehyde, 4-methoxystyrene 1.7

esters bornyl-acetate, bornyl-valerate 1.5

monoterpene hydrocarbons β-pinene, limonene, p-cimene and γ-terpinene 1.1

oxygenated monoterpenes nerol, linalool, carvone, verbenone, terpin-4-ol, camphora, 1,8-cineol 1.5

sesquiterpene hydrocarbons (E)-caryophyllene and α-humulene 1.1

oxygenated sesquiterpenes caryophyllene oxide 1.3

3.5. STATISTICAL ANALYSIS

The multivariate chemometric technique, hierarchical cluster analysis (CA) was applied

to the obtained SPME-GC-FID data, using STATISTICA 7.1 (Stat Soft Italia srl, www.statsoft.it),

in order to interpret the volatile profiles statistically and discriminate between the plant

samples collected in six different places. Hierarchical CA is an unsupervised chemometric

method disclosing the groupings between samples, characterised by a dataset, so that

similar samples are in the same group. The percentage composition of the identified

compounds of the six samples was included in the dataset of the software program as

handling data. An unknown oxygenated sesquiterpene and a sesquiterpene hydrocarbon

found in the samples 2 and 4 were also included, as they accounted for, respectively, 10.1

and 24.5% of the volatiles detected in the sample 4. Data with values under 0.1% or missing

data were substituted for the purpose of statistic analyses by 0.01%.

26

4. RESULTS

4.1. OPTIMISATION OF THE SPME METHOD

Both the total peak area of all obtained compounds and the individual peak areas of six

marker compounds were considered to evaluate the influence of the SPME parameters:

temperature, extraction time, sample amount and amount of added water, on the extraction

efficiency of three different fibres.

The marker compounds were selected to represent the various chemical classes of the

identified compounds having a different chromatographic behaviour. They included

compounds with retention times ranging from 7.31 to 14.29 min, as illustrated in Figure 4.1.

They were identified afterwards using GC-MS as cis-rose oxide (1), citronellol (2), β-maaliene

(3), α-isocomene (4), α-acoradiene (5) and caryophyllene oxide (6).

0

3.103

6.103

8 11 14

sig

na

l(p

A)

time (min)

0

3.103

6.103

8 11 14

sig

na

l(p

A)

time (min)

cis-rose oxide

citronellol

β-maaliene

α-isocomene

α-acoradiene

caryophyllene oxide

FIGURE 4.1.: SPME-GC-FID CHROMATOGRAM OF VISSO SAMPLE BY USING OPTIMISED

EXTRACTION PARAMETERS.

From Figure 4.2., showing the peak area of marker compounds and total volatiles

versus the fibre coating, it can be clearly observed that the CAR/PDMS fibre achieved an

almost three and ten times higher extraction efficiency than the PDMS and DVB/PDMS

fibres, respectively.

27

0

5000

10000

15000

20000

25000

PDMS DVB/PDMS CAR/PDMS

fibre coating

pe

ak

are

a (

pA

.s)

1

2

3

4

5

6

total area/10

FIGURE 4.2.: UPTAKE OF MARKER COMPOUNDS AND TOTAL VOLATILES BY THREE TYPES OF

SPME FIBRE COATING UNDER THE FOLLOWING ANALYTICAL CONDITIONS: EXTRACTION

TEMPERATURE, 60°C; EXTRACTION TIME, 30 MIN; PARTICLE SIZE, 1 MM; SAMPLE AMOUNT,

30 MG; DESORPTION TIME, 3 MIN. DATA OBTAINED BY GC-FID ANALYSIS. THE TOTAL AREA IS

REPRESENTED ON THE GRAPH DIVIDED BY TEN, TO IMPROVE THE READABILITY.

The repeatability of the extraction as a function of the fibre coating was evaluated by

performing the analyses in triplicate and calculation of the relative standard deviation (RSD).

RSD values of the individual peak areas of the marker compounds and the total peak area

are summarised in Table 4.1. Analysis with PDMS and DVB/PDMS coatings provided a good

repeatability with most RSD values not exceeding 10%, whereas analysis with the CAR/PDMS

coating gave rise to RSD values between 13.3 and 33.4%.

TABLE 4.1.: RELATIVE STANDARD DEVIATION (RSD %) VALUES (N=3) OBTAINED FOR TOTAL

VOLATILES AND MARKER COMPOUNDS BY USING THREE DIFFERENT SPME FIBRES.

marker compounds fibre coating

1 2 3 4 5 6

total

peak area

PDMS 20.1 4.9 2.7 1.9 1.2 7.5 5.9

DVB/PDMS 3.0 3.9 0.4 7.5 1.3 10.1 6.2

CAR/PDMS 14.9 27.0 18.1 15.2 13.3 33.4 18.5

Running of blank samples between analyses revealed residual compounds on the

CAR/PDMS fibre. This probably accounted for the observed higher RSD values. Considering

the retention capability, repeatability and avoiding of time-consuming extra cleaning steps,

the PDMS fibre was judged most favourable for the extraction of the volatiles in E.

nebrodensis.

28

0

2000

4000

6000

8000

10000

12000

14000

20 30 40 50 60 70 80

extraction temperature (°C)

pe

ak

are

a (

pA

.s)

1

2

3

4

5

6

total a rea /10

A

0

2000

4000

6000

8000

10000

12000

14000

10 20 30 40 50 60

extraction time (min)

pe

ak

are

a (

pA

.s)

1

2

3

4

5

6

total area/10

B

0

2000

4000

6000

8000

10000

12000

14000

10,0 20,0 30,0 40,0 50,0 60,0

sample amount (mg)

pe

ak a

rea

(pA

.s)

1

2

3

4

5

6

total area /10

C

0

2000

4000

6000

8000

10000

12000

14000

0 20 40 60

amount of added water (μl)

pe

ak

are

a (

pA

.s)

1

2

3

4

5

6

total area/10

D

FIGURE 4.3. EFFECT OF TEMPERATURE (A), EXTRACTION TIME (B), SAMPLE AMOUNT (C)

AND AMOUNT OF ADDED WATER (D) ON THE PEAK AREA OF THE MARKERS AND TOTAL

VOLATILES CAPTURED BY THE PDMS FIBRE. THE EXPERIMENTAL CONDITIONS ARE

INCLUDED IN CHAPTER 3. PEAK IDENTIFICATION: 1. CIS-ROSE OXIDE, 2. CITRONELLOL, 3.

β-MAALIENE, 4. α-ISOCOMENE, 5. α-ACORDIENE, 6. CARYOPHYLLENE OXIDE. THE TOTAL

AREA IS REPRESENTED ON THE GRAPH DIVIDED BY TEN, TO IMPROVE THE READABILITY.

29

Figure 4.3.A. shows the effect of the extraction temperature on the extraction of total

volatiles and marker compounds by using the PDMS coating. Markers 2 and 6 could not be

detected at 20 °C. It was found that except for marker 1, all peak areas increase steadily

from 20°C to 60°C. The peak areas of markers 2 and 6 increase from 60 °C to 80 °C whereas

the peak area of markers 1, 5, 3, 4 decreases. The total peak area is observed to remain

approximately constant comparing extraction temperatures of 60 °C and 80 °C.

Table 4.2. summarises the RSD values of peak areas of marker compounds and total

volatiles at different extraction temperatures. It can be clearly observed that extraction at

60°C gave very good RSD values. Because of good retention behaviour and low RSD values,

60°C was used for the evaluation of the other extraction parameters.

TABLE 4.2.: RELATIVE STANDARD DEVIATION (RSD %) VALUES (N=3) OBTAINED FOR TOTAL

VOLATILES AND MARKER COMPOUNDS BY USING DIFFERENT EXTRACTION TEMPERATURES.

aNo data available due to absence of the marker compound.

The results of the total peak area and individual peak areas varying with the extraction

time are shown in Figure 4.3.B. A steadily increasing tendency can be observed from 10 to

30 minutes. Extension of the extraction time to 60 minutes can still slightly improve the

extraction efficiency but the increase in peak areas is too small to allow doubling of the

extraction time.

The data used for the evaluation of the repeatability as a function of the extraction

time are summarised in Table 4.3. An unacceptable high RSD value is observed for the peak

areas of compounds 2 and 6 after being extracted for 10 min. The RSD values for compounds

extracted during 20, 30 and 60 min proved sufficient repeatability, with RSD values generally

not exceeding 20%. Taking into consideration the repeatability of the method, the extraction

efficiency and the total time of analysis, an extraction time of 30 min was chosen for further

analyses.

marker compounds temperature (°C)

1 2 3 4 5 6

total

peak area

20 37.4 -a 9.9 9.4 15.2 -

a 13.0

40 51.4 87.5 17.0 16.1 7.8 105.9 16.0

60 20.1 4.9 2.7 1.9 1.2 7.5 5.9

80 173.2 40.0 22.4 23.6 23.7 31.6 28.4

30

TABLE 4.3.: RELATIVE STANDARD DEVIATION (RSD %) VALUES (N=3) OBTAINED FOR TOTAL

VOLATILES AND MARKER COMPOUNDS BY USING DIFFERENT EXTRACTION TIMES.

marker compounds time (min)

1 2 3 4 5 6

total

peak area

10 13.4 58.4 1.9 2.2 1.0 54.2 8.0

20 12.4 11.7 13.4 13.7 13.5 23.1 15.4

30 20.1 4.9 2.7 1.9 1.2 3.1 5.9

60 20.3 10.6 6.1 6.0 5.6 9.3 4.7

Figure 4.3.C. visualises the influence of the sample amount on the extraction

efficiency. Generally, a maximal GC-FID response can be observed after extraction of 30.0

mg of plant material. Peak areas representing compounds 1 and 6 display a slightly aberrant

trend, both reaching the maximal observed peak area after extraction of 60.0 mg of plant

material.

The RSD values in Table 4.4. demonstrate that repeatability of the method is not highly

affected by varying the extracted sample amount for compounds 3, 4, 5, 6 and the total area

of all peaks. Still considerably lower RSD values were obtained for compounds 1 and 2 with a

sample amount of 30.0 mg. Taking into account the above data and the small amount of

sample material available, 30.0 mg of plant material was chosen as the sample amount to

perform the extractions.

TABLE 4.4.: RELATIVE STANDARD DEVIATION (RSD %) VALUES (N=3) OBTAINED FOR TOTAL

VOLATILES AND MARKER COMPOUNDS BY USING THREE DIFFERENT SAMPLE AMOUNTS.

marker compounds sample amount

(mg) 1 2 3 4 5 6

total

peak area

10.0 26.8 19.2 4.3 5.6 3.9 17.4 5.8

30.0 20.1 4.9 2.7 1.9 1.2 7.5 5.9

60.0 41.2 28.2 11.8 12.0 10.2 11.0 5.6

The influence of the amount of water added to the dry sample appeared to be

unpredictable as no similar pattern (Figure 4.3.D.) can be revealed for all marker

compounds. On the one hand, a clear negative effect on the peak area was seen for marker

compounds 3 and 4. On the other hand, the peak areas of markers 1, 2 and 5 were

fluctuating as a function of the amount of added water, while marker 6 seems to be

independent of it. In accordance with the RSD values of the experiments (Table 4.5.), the

total peak area was chosen as the most relevant parameter to evaluate the amount of added

31

water and thus further experiments were conducted without adding water, as this gave rise

to a maximal GC-FID response for the total peak area.

TABLE 4.5.: RELATIVE STANDARD DEVIATION (RSD %) VALUES (N=3) OBTAINED FOR TOTAL

VOLATILES AND MARKER COMPOUNDS BY DIFFERENT AMOUNTS OF ADDED WATER. marker compounds added amount of

water (μl) 1 2 3 4 5 6

total

peak area

0 20.1 4.9 2.7 1.9 1.2 7.5 5.9

20 32.7 39.1 20.0 18.0 17.8 29.1 23.7

40 26.1 15.3 23.2 25.5 14.3 32.0 19.5

60 51.3 47.0 33.5 20.3 18.8 40.5 34.3

4.2. COMPOSITION OF THE HEADSPACE OF E. NEBRODENSIS

The headspace volatiles of six different populations of E. nebrodensis are reported in

Table 4.6. A total of one hundred and nineteen volatiles were identified in the different

samples, accounting for 63.4-100.0% of the total volatiles. The identification of four

compounds was only based on comparison of the obtained mass spectrum with the mass

spectrum reported by NIST 08 (6S-2,3,8,8-tetramethyltricyclo[5,2,2,0(1,6)]undec-2-ene; β-

clovene; Z-1,6-tridecadiene) or WILEY 275 (longiborn-8-ene).

Sample 1 (Fig. 4.4.) was the richest, with 80 identified components, whilst sample 3 the

poorest, with 46 identified components.

0

3.103

6.103

sig

na

l(p

A)

time (min)

6 12 18

β-maaliene

β-patchoulene

α-isocomene

α-acoradiene

FIGURE 4.4. REPRESENTATIVE CHROMATOGRAM OF SAMPLE 1 OBTAINED BY GC-FID

ANALYSIS UNDER THE CONDITIONS DESCRIBED IN CHAPTER 3, REPRESENTING THE MAIN

CONSTITUENTS.

32

A great variability was found in the qualitative composition of the headspace of the six

different samples, since only 19 components were in common among all populations.

However, the volatile fraction of all samples was dominated by sesquiterpene hydrocarbons

(52.6-87.9%) (Fig. 4.5.), with β-maaliene (absent-7.5%), β-patchoulene (absent-11.3%), β-

panasinsene (absent-7.3%), α-isocomene (absent-31.2%), α-trans-bergamotene (traces-

7.0%), allo-aromadendrene (absent-33.0%), α-acoradiene (absent-9.4%), γ-muurolene (0.6-

16.3%) being the most representatives. Their chemical structures are reported in Figure 4.6.

0

10

20

30

40

50

60

70

80

90

Visso Camerino Forca di

Penne

Monte la

Serra

Orgosolo Gola Gorropu

collection sites

co

nte

nt

of

gro

up

ed

co

mp

ou

nd

s (%

)

ALK

ARO

MH

MO

SH

SO

NOR

FIGURE 4.5. PERCENTAGES OF GROUPED COMPOUNDS OCCURRING IN THE HEADSPACE OF

E. NEBRODENSIS SAMPLES. ALK: ALKANES, ARO: AROMATICS; MH: MONOTERPENE

HYDROCARBONS; MO: OXYGENATED MONOTERPENES; SH: SESQUITERPENE

HYDROCARBONS; SO: OXYGENATED SESQUITERPENES; NOR: NORISOPRENOIDS.

33

TABLE 4.6. QUANTATIVE (%) COMPOSITION OF THE VOLATILE FRACTION OF EPHEDRA NEBRODENSIS OBTAINED BY HS-SPME.

RI literature Samples N° Component

a RI

b

Adamsc NIST 08

d 1

g 2 3 4 5 6

IDe

1. hexanal 797 802 799 tr 0.1 (44.6) tr 0.2 (4.0) tr tr Std

2. (2E)-hexenal 850 855 847 - tr - - - - MS, RI

3. heptanal 903 902 903 - 0.1 (3.6) tr tr tr tr MS, RI

4. 6-methyl-2-heptanone 955 955 955 0.6 (45.9) 0.3 (27.8) tr tr 0.3 (25.0) 0.1 (8.9) MS, RI

5. benzaldehyde 962 960 959 tr tr 1.2 (55.3) 0.2 (67.4) - tr Std

6. 6-methyl-5-hepten-2-one 989 985 tr tr - - - - MS, RI

7. octanal 1005 998 1006 - tr - - - - Std

8. p-cymene 1024 1024 1025 tr 0.1 (5.1) tr 0.2 (17.7) tr tr Std

9. (2E)-octen-1-al 1059 1054 tr tr - - - - MS, RI

10. undecane 1099 1100 1100 tr - - - - - Std

11. nonanal 1104 1100 1105 tr 0.3 (21.7) 2.8 (9.2) 0.3 (7.9) 0.3 (20.8) 0.2 (29.3) MS, RI

12. 2,6-dimethylcyclohexanol 1107 1100 1110 tr - - - - - MS, RI

13. cis-rose oxide 1110 1106 1111 0.5 (21.6) 0.3 (7.3) tr tr tr tr MS, RI

14. trans-rose oxide 1126 1127 1127 0.3 (16.7) tr - - - - MS, RI

15. 4-keto-isophorone 1144 1152 tr 0.2 (27.4) 2.1 (4.2) 0.4 (22.1) - 0.8 (7.9) MS, RI

16. citronellal 1153 1153 1153 tr - - - - - MS, RI

17. ethyl benzoate 1170 1173 1170 - tr tr tr tr tr MS, RI

18. α-terpineol 1189 1188 1189 - tr - - - - Std

19. methyl salicylate 1192 1191 1191 tr - - - - - MS, RI

20. safranal 1196 1196 1197 tr - - - - - MS, RI

21. n-dodecane 1198 1200 1200 tr - tr tr - tr Std

22. decanal 1203 1201 1203 tr 0.5 (13.7) tr 0.2 (26.5) 0.2 (20.2) 0.3 (26.8) MS, RI

23. α-citronellol 1216 1214 tr - - - - - MS, RI

24. β-cyclocitral 1218 1219 0.1 (26.8) 0.1 (16.0) tr tr - - MS, RI

25. citronellol 1226 1225 1227 4.2 (5.0) 2.7 (5.9) - 0.3 (7.6) 0.4 (5.0) 1.4 (15.9) Std

26. thymol methyl ether 1233 1235 1233 2.8 (9.6) 1.9 (5.9) tr 1.3 (7.6) 1.7 (11.7) 0.6 (5.5) MS, RI

27. citronellyl formate 1273 1271 1275 tr tr - - - - MS, RI

28. safrole 1285 1287 1287 - - - - tr 0.1 (24.9) MS, RI

29. n-tridecane 1298 1300 1300 tr tr - tr tr tr Std

30. undecanal 1304 1306 1305 tr tr tr tr tr 0.2 (5.0) MS, RI

31. (3Z)-hexenyl tiglate 1322 1321 tr 0.6 (6.4) - 0.6 (14.2) - - MS, RI

34

TABLE 4.6. QUANTATIVE (%) COMPOSITION OF THE VOLATILE FRACTION OF EPHEDRA NEBRODENSIS OBTAINED BY HS-SPME (CONTINUED).

32.

6S-2,3,8,8-

tetramethyltricyclo[5,2,2,

0(1,6)]undec-2-ene

1327 0.6 (23.8) - - 0.6 (7.8) - - MS

33. β-clovene 1345 0.7 (4.6) - - 0.3 (14.1) 0.9 (13.1) 1.0 (2.2) MS

34. α-longipinene 1347 1352 1348 - 0.5 (2.3) - - - - Std

35. citronellyl acetate 1351 1352 1351 0.3 (4.6) - - - - - MS, RI

36. cyclosativene 1361 1374 1363 - 0.2 (45.7) - - - - MS, RI

37. β-maaliene 1363 1359 7.3 (2.9) - 2.0 (33.1) 2.0 (7.1) 6.9 (6.2) 7.5 (1.8) MS, RI

38. α-ylangene 1368 1375 1368 - 7.0 (4.5) - - - - MS, RI

39. α-copaene 1372 1376 1372 - 0.5 (10.6) 0.2 (13.2) 0.8 (8.6) - 1.9 (3.1) Std

40. isoledene 1375 1376 1373 - 1.2 (14.3) - - - - MS, RI

41. β-panasinsene 1376 1382 4.5 (1.1) - 2.3 (15.1) 1.9 (6.5) 6.8 (5.7) 7.3 (2.6) MS, RI

42. 2-epi-alpha-funebrene 1377 1382 1386 - tr - - - - MS, RI

43. β-patchoulene 1378 1379 11.3 (2.6) - 4.3 (15.7) 2.4 (7.1) 9.4 (5.3) 7.8 (2.6) MS, RI

44. sativene 1386 1391 1396 tr 0.3 (7.6) - - - - MS, RI

45. α-isocomene 1387 1388 22.8 (1.0) - 8.2 (12.9) 6.9 (7.7) 31.2 (5.2) 24.5 (3.4) MS, RI

46. longiborn-8-ene 1391 3.5 (8.5) - tr - 3.9 (7.2) 4.1 (4.1) MS

47. unknown sesquiterpene

hydrocarbonh

1396 - tr - 24.5 (8.6) - -

48. n-tetradecane 1398 1400 1400 - - 0.8 (15.1) - - - Std

49. α-chamipinene 1397 1396 tr 0.7 (9.3) - - - - MS, RI

50. longifolene 1400 1407 1400 1.9 (1.6) 0.4 (13.5) - 0.9 (10.4) 1.7 (8.3) 1.8 (9.8) Std

51. dodecanal 1403 1408 1405 tr - 1.0 (13.7) 3.2 (8.9) tr 1.6 (1.3) MS, RI

52. α-gurjunene 1405 1409 1404 - 1.8 (6.3) - - - - Std

53. α-cedrene 1407 1414 1408 1.1 (3.1) 0.4 (19.6) 2.7 (13.7) - 0.1 (21.9) 0.2 (8.2) Std

54. acora-3,7(14)-diene 1410 - 0.4 (19.6) - - - - MS

55. 1,7-dimethylnaphtalene 1415 1418 1419 2.5 (1.2) - 1.4 (17.5) - 1.6 (4.5) - MS, RI

56. (E)-caryophyllene 1415 1419 1415 1.6 (1.2) 2.8 (4.0) - 5.1 (3.4) 0.9 (4.3) 4.7 (3.9) Std

57. β-copaene 1423 1432 - 0.4 (24.4) - tr - - MS, RI

58. α-trans-bergamotene 1432 1434 1433 0.9 (3.9) 0.3 (13.8) 7.0 (12.8) tr 1.8 (5.5) 0.2 (8.6) MS, RI

59. coumarin 1434 1432 1432 tr - tr 0.4 (9.4) - 0.3 (15.0) Std

60.

tricyclo[6,3,0,0(2,4)]undec

-8-ene,3,3,7,11-

tetramethyl-

1437 1440 - 2.0 (2.1) - 0.9 (9.4) - - MS, RI

35

TABLE 4.6. QUANTATIVE (%) COMPOSITION OF THE VOLATILE FRACTION OF EPHEDRA NEBRODENSIS OBTAINED BY HS-SPME (CONTINUED).

61. α-himachalene 1447 1451 0.8 (2.6) 3.7 (2.0) - - 4.9 (5.4) 4.6 (7.4) MS, RI

62. α-neoclovene 1447 1454 1451 2.3 (2.6) - 1.5 (19.5) 2.1 (5.9) - - MS, RI

63. α-humulene 1447 1454 1447 - - - - 1.0 (5.4) 2.0 (7.4) Std

64. geranyl acetone 1447 1455 1446 2.1 (2.6) - 4.1 (19.5) 0.2 (2.7) - - MS, RI

65. allo-aromadendrene 1458 1460 1458 0.7 (4.2) 33.0 (2.4) - 15.6 (7.1) - - Std

66. α-acoradiene 1462 1466 1459 6.2 (1.6) 0.8 (2.4) - - 9.4 (4.9) 8.6 (7.1) MS, RI

67. 2,6-di-tert-butylquinone 1464 1472 1458 - - 2.5 (16.9) 1.8 (6.5) - - MS, RI

68. (E)-β-farnesene 1465 1456 - - - - 1.2 (4.9) - MS, RI

69. β-neoclovene 1468 1475 tr - - 0.4 (6.0) 0.9 (10.4) 1.0 (9.4) MS, RI

70. β-acoradiene 1471 1470 1483 2.5 (1.3) 1.3 (4.3) - - - - MS, RI

71. γ-selinene 1474 1473 - - - 0.5 (20.0) - 1.0 (9.4) MS, RI

72. γ-muurolene 1474 1479 0.6 (2.5) 3.8 (4.3) 16.3 (8,6) 1.1 (20.1) 2.2 (4.7) 1.0 (9.4) MS, RI

73. α-amorphene 1478 1484 - 3.3 (5.7) - 0.4 (7.4) - - MS, RI

74. 11-alphaH-himachala-1,4-

diene 1479 1486 0.7 (2.5) - - tr 0.9 (10.0) 0.2 (95.9) MS, RI

75. (E)-β-ionone 1483 1487 3.2 (7.1) 1.5 (16.2) 8.2 (16.4) 2.0 (5.6) 0.5 (7.4) 0.8 (4.8) Std

76. β-selinene 1483 1490 1483 - - - 0.7 (5.6) - 0.8 (4.8) MS, RI

77. δ-selinene 1488 1492 - - - - - tr MS, RI

78. 10,11-epoxy-calamenene 1489 1492 0.8 (25.8) 0.2 (58.5) - - tr - MS, RI

79. viridiflorene 1492 1496 1493 - 2.4 (14.7) - - - - MS, RI

80. α-selinene 1492 1498 0.4 (28.4) - - 1.6 (8.5) - 1.3 (39.7) MS, RI

81. n-pentadecane 1495 1500 tr - 5.8 (30.5) - 1.5 (53.5) 0.8 (1.3) Std

82. α-muurolene 1498 1500 - 3.2 (5.1) 0.6 (20.4) 0.6 (6.0) - 1.8 (6.4) MS, RI

83. β-himachalene 1498 1500 1499 tr - - - 1.6 (8.4) - MS, RI

84. tridecanal 1506 1510 1510 - 0.4 (27.6) 2.2 (10.1) 0.5 (5.9) 0.2 (17.9) tr MS, RI

85. δ-amorphene 1504 1512 - 0.3 (27.6) - - tr - MS, RI

86. γ-cadinene 1511 1513 - 0.7 (6.5) - tr - 0.2 (5.0) MS, RI

87. α-dehydro-ar-

himachalene 1511 1517 0.2 (35.1) 0.3 (6.5) - - tr - MS, RI

88. trans-cycloisolongifol-5-ol 1517 1513 - tr - - - - MS, RI

89. trans-calamenene 1520 1522 1520 0.6 (26.6) 0.1 (3.8) 2.1 (36.4) 0.9 (7.5) 0.4 (20.5) 2.2 (7.2) MS, RI

90. zonarene 1520 1529 - - - - - 0.2 (7.2) MS, RI

91. δ-cadinene 1521 1523 - 2.8 (3.8) 4.3 (36.4) tr - - MS, RI

92. γ-dehydro-ar-himachalene 1527 1532 tr 0.4 (25.0) - - 0.1 (12.4) tr MS, RI

36

TABLE 4.6. QUANTATIVE (%) COMPOSITION OF THE VOLATILE FRACTION OF EPHEDRA NEBRODENSIS OBTAINED BY HS-SPME (CONTINUED).

93. diihydroactinidiolide 1527 1525 0.3 (3.4) tr 2.2 (17.5) 1.3 (17.7) - tr MS, RI

94. α-cadinene 1535 1538 0.5 (27.4) 1.0 (14.5) - tr - - MS, RI

95. α-calacorene 1540 1545 0.8 (5.0) 4.9 (1.9) tr tr 0.6 (10.5) 0.3 (42.0) MS, RI

96. β-calacorene 1560 1565 tr 0.4 (3.6) - - tr - MS, RI

97. palustrol 1566 1568 1567 - tr - - - - MS, RI

98. (3Z)-hexenyl benzoate 1566 1566 tr 0.2 (25.2) tr 0.8 (20.6) 0.1 (32.4) 1.0 (6.1) MS, RI

99. n-hexyl benzoate 1572 1580 1576 tr 0.6 (9.9) - tr - - MS, RI

100. caryophyllene oxide 1580 1583 1580 1.4 (7.5) 1.3 (36.4) - 2.0 (14.8) - - Std

101. unknown oxygenated

sesquiterpenei

1591 - tr - 10.1

(12.2) - -

102. 1-hexadecene 1583 1587 tr - - - - - MS, RI

103. cubeban-11-ol 1590 1595 - 1.3 (11.1) - - - - MS, RI

104. n-hexadecane 1593 1600 - tr 1.4 (26.4) 0.7 (12.2) tr 0.1 (25.4) MS, RI

105. tetradecanal 1606 1612 1606 0.7 (28.9) tr 0.3 (30.2) tr tr 0.8 (9.2) MS, RI

106. β-himachalene oxide 1609 1616 0.5 (11.1) 1.0 (10.0) - - - - MS, RI

107. α-corocalene 1618 1623 0.3 (8.8) 0.5 (6.9) - - 0.5 (5.8) - MS, RI

108. α-muurolol 1643 1646 1643 tr - 3.9 (21.9) - - - MS, RI

109. cis-methyl

dihydrojasmonate 1651 1655 tr - - - - - MS, RI

110. 1-hexadecyne 1665 1664 tr - - - - - MS, RI

111. Z-1,6-tridecadiene 1665 - - - - 0.4 (14.1) - MS

112. cadalene 1674 1676 1674 0.8 (16.5) 0.6 (7.9) 0.9 (47.1) tr 0.4 (5.4) 0.9 (3.0) MS, RI

113. n-heptadecane 1695 1700 tr tr 6.4 (18.8) 0.6 (14.7) 0.8 (28.7) 1.1 (24.5) Std

114. n-octadecane 1799 1800 0.3 (44.2) 0.5 (50.5) 0.5 (30.4) tr 0.1 (7.1) 0.2 (16.4) Std

115. isopropyl myristate 1826 1827 0.4 (54.7) - - - - - MS, RI

116. 6,10,14-trimethyl-2-

pentadecanone 1845 1845 0.8 (38.0) - - - - - MS, RI

117. n-nonadecane 1897 1900 0.2 (34.0) tr 0.4 (10.7) tr tr 0.1 (6.7) Std

118. n-eicosane 1999 2000 0.4 (21.0) - - - - - Std

119. hexadecanoic acid 1963 1960 1963 - - - tr - - Std

37

TABLE 4.6. QUANTATIVE (%) COMPOSITION OF THE VOLATILE FRACTION OF EPHEDRA NEBRODENSIS OBTAINED BY HS-SPME (CONTINUED).

Total identified (%) 97.0 (1.0) 96.7 (1.3) 100.0 (4.2) 63.4 (2.3) 96.2 (2.2) 97.6 (1.4)

Identified compounds 80 74 46 61 52 56

Grouped compounds (%)

Aliphatics alcohols tr - - - - -

Alkanes 1.1 (16.3) 0.5 (50.5) 15.4 (14.2) 1.3 (9.0) 2.8 (29.6) 2.4 (11.3)

Aldehydes and ketones 2.2 (20.9) 1.7 (9.9) 6.4 (6.0) 4.4 (6.5) 1.2 (10.8) 3.3 (3.9)

Esters 0.4 (54.7) 0.6 (6.4) - 0.6 (14.2) - -

Aromatics 6.2 (4.9) 2.7 (4.9) 2.6 (27.4) 2.8 (9.2) 3.4 (6.2) 2.0 (4.4)

Monoterpene hydrocarbons tr 0.1 (5.1) tr 0.2 (17.7) tr tr

Oxygenated monoterpenes 7.5 (3.4) 3.0 (5.3) 4.1 (19.5) 0.5 (4.5) 0.4 (5.3) 1.4 (15.4)

Sesquiterpene hydrocarbons 73.3 (0.9) 82.4 (1.3) 52.6 (5.5) 70.5 (3.5) 87.9 (2.2) 86.9 (1.5)

Oxygenated sesquiterpenes 5.9 (5.5) 3.8 (13.5) 3.9 (21.9) 12.1 (10.4) tr -

Norisoprenoids 0.4 (8.4) 1.7 (14.2) 10.4 (13.4) 3.4 (7.7) 0.5 (7.4) 0.8 (4.8)

Others tr 0.2 (27.4) 4.6 (9.3) 2.2 (6.6) - 0.8 (7.9)

a Compounds are listed in order of their elution from a HP-5 column; percentage values are means of three determinations; they were obtained at FID by peak area

normalization calculating the relative response factor. The relative standard deviation is reported between brackets. b

Retention index on MS with HP-5 column, experimentally determined using homologous series of C7-C30 alkanes. c Relative retention index taken from Adams (2007).

d Relative retention index taken from NIST 08 (2008).

e Identification methods: MS, by comparison of the mass spectrum with those of the computer mass libraries Wiley, Adams (2007) and NIST 08 (2008); RI, by comparison of RI

with those reported in literature (Adams, 2007; NIST, 2008); std, by comparison of the retention time, index and mass spectrum of available authentic standard.

f tr, traces (mean value below 0.1%).

gThe samples are numbered 1 to 6 representing their collecting sites: 1. Visso, 2. Camerino, 3. Forca di Penne, 4. Monte la Serra, 5. Orgosolo, 6. Gola Gorropu.

hUnknown 1, m/z of the 10 largest peaks (the abundance reported between brackets): 189 (999), 175 (908), 81 (897), 107 (878), 79 (856), 123 (853), 93 (844), 91 (808), 105

(747), 121 (603). iUnknown 2, m/z of the 10 largest peaks (the abundance is reported between brackets): 107 (999), 105 (668), 43 (615), 95 (612), 121 (545), 91 (538), 205 (530), 177 (479), 187

(455), 93 (435)

38

A B

H

C D

E F

H

H

G H

FIGURE 4.6. CHEMICAL STRUCTURES OF THE MAJOR COMPOUNDS DETECTED IN THE

HEADSPACE OF E. NEBRODENSIS: A, β-MAALIENE; B, β-PATCHOULENE; C, β-PANASINSENE; D,

α-ISOCOMENE; E, α-TRANS-BERGAMOTENE; F, ALLO-AROMADENDRENE; G, α-ACORADIENE;

H, γ-MUUROLENE.

39

The percentage value of the total identified volatiles released from sample 4 was very low

(63.4%) owing to the occurrence of two unknown compounds, one sesquiterpene

hydrocarbon (24.5%, RI: 1396) and one oxygenated sesquiterpene (10.1%, RI: 1591), whose

mass spectra, lacking in the MS commercial libraries used, are reported in Figure 4.7.

FIGURE 4.7. MASS SPECTRA OF THE UNKNOWN SESQUITERPENE HYDROCARBON (TOP) AND

UNKNOWN OXYGENATED SESQUITERPENE (BOTTOM) DETECTED IN SAMPLE 4.

Minor contributions were given by aromatics (2.7-6.2%) and oxygenated monoterpenes (3.0-

7.5%) in samples 1 and 2, alkanes (15.4%) and norisoprenoids (10.4%) in sample 3 and

oxygenated sesquiterpenes (12.1%) in sample 4. The most abundant representatives of

these classes were citronellol (2.7-4.2%), thymol methyl ether (1.9-2.8%), n-pentadecane

(5.8%) and n-heptadecane (6.4%), and (E)-β-ionone (8.2%), respectively.

20 100 200 0

10

E+

05

2

0E

+0

5

Ab

un

da

nce

189 175 81 107 123 93

161 147 133

41 67 55

204

29

107

43 95 121 205 177

187 135 147

81 55 220 67 162

29

0

30

E+

05

6

0E

+0

5

20 100 200 m/z

40

5. DISCUSSION

There were three aims in this work: the development of an SPME-GC-FID method to

aid the botanical classification of Italian populations of E. nebrodensis, determining whether

its volatile profile was influenced by the geographic origin of the samples and screening for

potential useful pharmaceutical and cosmetic substances.

5.1. OPTIMISATION OF THE SPME METHOD

Concerning the design of the method optimisation, all parameters were optimised

individually by comparing the GC-FID responses and relative standard deviations of total

volatiles and six markers obtained from the chromatograms with different extraction

settings. This one-factor-at-a-time design is prone to false conclusions due to neglect of the

interactions between parameters. A full design taking into account three parameters at four

levels (extraction temperature: 20, 40, 60 and 80 °C; extraction time: 10, 20, 30 and 60 min;

amount of added water: 0, 20, 40 and 60 μl) and two parameters at three levels (fibre

coating: PDMS, DVB/PDMS and CAR/PDMS; sample amount: 10.0, 30.0 and 60.0 mg) was not

realistic as 1728 experiments and 57.6 grams of plant material were necessary. A fractional

factorial design is recommended in order to account for interactions between parameters in

spite of the small plant amount that was available. For example for the optimisation of

extraction temperature and time, both influencing the distribution coefficient between fibre

coating and sample, a multivariate optimisation design is recommended. Through affection

of the distribution coefficient, elevation of the extraction temperature produces a shorter

equilibrium time but lowers the amount extracted at equilibrium (Lord and Pawliszyn, 2000).

Not only extraction time and temperature influence each other, therefore multivariate

optimisation for the development of the SPME method is advisable.

A second improvement to the optimisation could be made by inclusion of other

extraction parameters such as fibre thickness, vial size, desorption conditions and particle

size. The thickness of fibre coating was not optimised as SPME fibres coated with thinner

films were not disposable in the laboratory. Usually the thinnest film supplying sufficient

sensitivity is employed in order to reduce extraction times and to allow for the use of less

sample amount (Mills and Walker, 2000). In particular for the rare species E. nebrodensis,

the amount of plant material collected could be diminished by the use of fibres with thinner

coats and inclusion of this parameter in the optimisation design. It must be taken into

41

account that the sample size should then be carefully optimised together with the coating

thickness to avoid fibre overloading as well as it should be sufficiently large to allow accurate

weighting and secure the repeatability. The vial size can contribute significantly to the

extraction through the determination of the internal pressure as high pressures can result in

the expulsion of a significant amount of volatiles at penetration of the septum with the

SPME needle (Miller et al., 1996). As for the desorption conditions, both temperature and

time influence the recovery of the analytes and should be optimised together (Mills and

Walker, 2000). Taking into consideration that the optimal desorption temperature should be

approximately equal to the boiling point of the least volatile analyte and the maximal

temperature the fibre can be exposed to, the desorption temperature was fixed at 250 °C.

Setting the desorption time, it should be considered that longer desorption can shorten the

fibre’s lifetime. Finally, concerning the particle size, no sieves with an exclusion limit of less

than 1 mm, were available in the laboratory. Moreover severe static effects could be

encountered at smaller particle sizes (Zang et al., 2007).

Finally, beside inclusion of the above extraction parameters in a multivariate method

design, the optimisation could be further improved by using GC-MS, to provide information

regarding the identity of the extracted analytes as for now marker peaks were selected

without verifying their identity in the different chromatograms. GC-MS analysis could

especially be useful in the selection of the fibre coating as the type of fibre affects the

selectivity of the extraction (Mills and Walker, 2000). Another example of the support of

mass-spectrometric analysis to the optimisation can be found in the influence of the

addition of water as hydrolytic and enzymatic degradation reactions may occur (Maggi et al.,

2010c, in press).

Regarding the obtained results for fibre coating, it was clear that the CAR/PDMS fibre

coating gave the highest retention capability, but also the lowest repeatability and required

extra, time-consuming cleaning steps. The observed carry-over is due to the porous, bipolar

coating that, in spite of higher relative signal responses of low molecular mass volatiles (C2-

C12), retained the larger molecules (>C12) on the surface of the carboxen particles. As a

consequence, those higher molecular mass volatiles were difficult to desorb (Perera et al.,

2002). Owing to the lowest retention ability of the more polar DVB/PDMS fibre, the non-

polar PDMS fibre was selected. Moreover, it was reported in literature that the PDMS fibre

can still be applied successfully to more polar compounds, in particular after optimisation of

42

the SPME parameters (Pawliszyn, 1997). As for extraction temperature a compromise was

made between high collection efficiency, as at lower temperatures the distribution

coefficient is higher, and rapid extraction of the less volatile compounds, at higher

temperatures. This phenomena can be observed from the obtained data since at lower

temperatures a higher peak area is obtained for the most volatile marker (marker 1) and at

higher temperatures increasing GC-FID responses are found for the least volatile marker

(marker 6).

Concerning the duration of the extraction, generally, a steady increase of the amount

of extracted volatiles was attained after extraction for 10 up to 30 min. When the extraction

time was doubled to 60 min, only a smooth increase, too small to allow doubling of the

extraction time, was observed for some markers. Thus, it can be stated that the distribution

equilibrium for 30.0 mg of sample is almost reached after 30 minutes of extraction at a

temperature of 60 °C.

For the determination of the amount of plant material subjected to SPME the limited

amount of plant material was taken into account. We optimised the above parameters with

30.0 mg of sample as this amount could cover the surface of the vial and all plant material

was directly exposed to the headspace. After choosing the parameters, increase of the

sample amount could not improve the GC-FID response anymore since at equilibrium the

amount of analyte is independent of the volume of the sample when the sample is very large

(Lord and Pawliszyn, 2000).

Finally, the addition of water gave variable results with a low repeatability. The

increase in peak area of some markers by adding an amount of water could be accounted for

by enzymatic or hydrolytic activity, whereas lower peak areas can be caused by a lower

evaporation from the plant matrix (Maggi et al., 2010c, in press).

In the future, the optimised SPME method could be applied to E. major, and by

extension even to other species of the genus Ephedra, in order to compare its composition

with that of E. nebrodensis, giving support to the botanical classification. It should however

be noted that this method could be optimised further by multivariate method design and

adding additional parameters as well as using GC-MS for the identification of the selected

peaks.

43

5.2. THE HEADSPACE COMPOSITION OF E. NEBRODENSIS

In this work the optimised SPME method was applied to the dry aerial parts of five

other E. nebrodensis specimen, originating from different geographical regions to examine

the intraspecies metabolomic variability and screen for compounds with pharmaceutical

potential.

Identification of at least 96.2% of the total volatiles was accomplished for five samples.

In spite of our attempts, the sample originating from Monte la Serra was only identified for

63.4%. The remaining area to be identified is almost exclusively constituted by only two

unknown compounds that were also found in trace amounts in the sample from Camerino: a

sesquiterpene hydrocarbon (24.5%) and an oxygenated sesquiterpene (10.1%) (mass spectra

are reported in Fig. 4.7.). Further analysis is necessary to identify these abundant, unknown

compounds.

Absolute quantification after headspace sampling of the solid plant matrix is

problematic and inherent to the chosen extraction method for no reference sample is

available and one with a known composition is hard to compose as it concerns a biological

matrix. Though absolute quantification may be attained through composition of a reference

sample, spiking the sample with internal standards (preferentially isotope labelled) or

determination of the recovery by performing a preliminary exhaustive extraction, using for

example liquid liquid extraction (Bicchi et al., 2008). Synthesis of a reference sample has

been described for Cinnamomum burmanii Blume by adding known amounts of reference

solutions to bulk sample from which all measurable amounts of volatiles had been extracted,

evaporating the solvent and thorough mixing (Miller et al., 1996). However these procedures

are time-consuming and not essential to accomplish the goals set at the beginning of this

study. Therefore the relative amounts of the volatiles, expressed as percentages, were

calculated by peak area internal normalisation.

As it was not possible to calculate response factors for each individual compound, it

was done for substances representing the most abundant chemical classes present. These

classes are broad and the classification of the compounds into these classes is not always

clear. The compounds were preferentially classified as mono- or sesquiterpene hydrocarbon,

oxygenated mono- or sesquiterpene or alkane, then as aromatic. If they could not be

included in one of these classes, they were classified as aldehydes, ketones, alcohols or

esters. Moreover, the standard deviations that are reported on the content of the identified

44

compounds in Table 4.6. were calculated without taking into account the standard

deviations on the response factors. This approach was suggested by my promoter since we

did not dispose of standard deviations for all response factors.

The acquired SPME-GC-FID data of the sample collected in Camerino allowed

comparison with the volatile profile of its essential oil, as previously reported by Maggi et al.

(2010a, in press), and revealed great differences (Fig. 5.1.). The most abundant compound

occurring in the essential oil, the oxygenated monoterpene citronellol (29.7%) was only

found in a small amount (2.7%) after SPME analysis. Oxygenated monoterpenes dominated

the essential oil composition and sesquiterpene hydrocarbons constituted only a minor

fraction, while the opposite pattern was observed after SPME analysis. Esters constituted

the second most abundant group (11.5%) in the essential oil with ethylhexadecanoate (9.5%)

as the main representative, whereas SPME revealed only scanting amounts of esters and the

absence of ethylhexadecanoate. Interestingly, the unknown compounds detected in the

samples originating from Camerino and Monte la Serra, were absent in the essential oil

derived from E. nebrodensis. Globally, more compounds were identified after SPME (74) in

comparison to extraction by means of hydrodistillation (59 identified compounds).

0

10

20

30

40

50

60

70

80

90

hydrodisti llation SPME

volati le extraction method

co

nte

nt

gro

up

ed

co

mp

ou

nd

s (%

)

ALK

ARO

EST

MH

MO

SH

SO

NOR

FIGURE 5.1. DISTRIBUTION OF THE MAIN GROUPED COMPOUNDS IN THE ESSENTIAL OIL

(MAGGI ET AL., 2010a, IN PRESS) AND HEADSPACE OF E. NEBRODENSIS (SAMPLE 2). ALK:

ALKANES, ARO: AROMATICS; EST: ESTERS. MH: MONOTERPENE HYDROCARBONS; MO:

OXYGENATED MONOTERPENES; SH: SESQUITERPENE HYDROCARBONS; SO: OXYGENATED

SESQUITERPENES; NOR: NORISOPRENOIDS.

45

Beside sharing only 20 common constituents, these different profiles revealed a

different sensitivity of the extraction techniques. The observed differences are essentially

due to the fact that using HS-SPME the volatiles are not directly extracted from the sample

but from the headspace, containing volatiles characterising the plant matrix (Bicchi et al.,

2008). Still some differences may be accounted for by the invasive character of

hydrodistillation. Unlike SPME, hydrodistillation can entail artefacts caused by high

extraction temperatures, oxidations and hydrolysis of the plant matrix or the volatiles

themselves (Kataoka et al., 2000). For example, phytol, an oxygenated diterpene and

degradation product of chlorophyll (Gossauer and Engel, 1996) was detected in the essential

oil while it proved to be absent after SPME. Concerning the inversed relationship of

oxygenated monoterpenes and sesquiterpene hydrocarbons, the high amount of

oxygenated monoterpenes in the essential oil could be caused by oxidation and hydrolysis

reactions occurring during hydrodistillation (Boutekedjiret et al., 2003). On the other hand,

the abundant presence of sesquiterpenes, that are less volatile than monoterpenes, in the

headspace of E. nebrodensis in comparison to the essential oil composition may be

accounted for by the fact that the SPME analysis was performed six months later than the

hydrodistillation.

To interpret the obtained SPME-GC-FID data of six populations of E. nebrodensis on a

statistical basis, they were subjected to hierarchical CA. The dendrogram relative to the CA

with the Euclidian distance as dissimilarity coefficient is reported in Figure 5.2.

Three main groups could be delineated: a first group, formed by one Marchigian

sample and both Sardinian samples; a second group formed by only one Abruzzian sample; a

third group formed by an Abruzzian and Marchigian sample. These results show that the

volatile composition of E. nebrodensis is highly variable and, with exception of the Sardinian

samples, it seems to be independent of its geographic origin. Although the Sardinian

samples are categorised in the same group, the SPME-GC-FID data did not permit to

thoroughly characterise the peninsular samples with respect to the ones originating from

Sardinia, as one Marchigian population demonstrates a similar volatile profile. However

more plant material should be collected and examined to consider the samples as

representative for the entire population of a region and eventually to deduce a correlation

between the origin of the sample and its volatile composition.

46

4 2 3 6 5 15

10

15

20

25

30

35

40

45

Sim

ilarit

y

FIGURE 5.2. DENDROGRAM OBTAINED BY CA OF THE PERCENTAGE COMPOSITION OF

VOLATILES OF EPHEDRA NEBRODENSIS, WITH THE EUCLIDEAN DISTANCE AS DISSIMILARITY

COEFFICIENT. NUMBERS REPRESENT THE COLLECTING SITES AS DEFINED IN CHAPTER 3.

Future SPME analysis of other Italian Ephedra species, could, in spite of the observed

highly variable volatile profile within E. nebrodensis, prove if the method can be successfully

used in the support of the botanical classification of the species.

Regarding the third aim, screening for interesting substances from pharmaceutical

point of view, we can report the presence of β-maaliene (absent-7.5%), α-isocomene

(absent-31.2%) and (E)-β-ionone (0.5-8.2%). Isolated from spikenard, β-maaliene was

reported in the context of aromatherapy to act as sedative agent in mice after inhalation

(Takemoto et al., 2009).

Extracted from Leontopodium alpinum, α-isocomene previously proved to increase the

extracellular level of acetylcholine significantly and amplify cholinergic transmission in the

brain of rats. Consequently the compound may have potential as antidementia agent in

brain diseases caused by cholinergic deficiency (Hornick et al., 2008).

The norisoprenoid (E)-β-ionone was stated to dispose of antitumor activities in rats

(Liu et al., 2008). Furthermore, this compound is one of the main contributors to the aroma

of roses. Also noteworthy is the presence of several other volatiles useful as perfuming

agents in cosmetics and pharmaceutical preparations. Examples of these include cis rose-

oxide, citronellol, β-patchoulene, n-heptadecane and n-pentadecane (http://ec.europa.eu/

enterprise/sectors/cosmetics/index_en.htm).

47

6. CONCLUSIONS

A simple, non-invasive method requiring only a small amount of plant material has

been developed to evaluate the volatile composition of E. nebrodensis, for the first time by

using HS-SPME coupled to GC-FID and GC-MS. However, the method is susceptible to further

improvements including a multivariate optimisation design, inclusion of more extraction

parameters and application of GC-MS in optimisation. Future analysis of E. major is

necessary to determine if the optimised method is applicable to aid the actual botanical

classification of the species.

Consequently the method was applied to six Italian samples of E. nebrodensis revealing

a high intraspecies volatile variability as no significant correlation was found between the

volatile profile and the geographical distribution of the six samples. CA applied to the SPME-

GC-FID data suggested the presence of at least three different chemotypes among the six

samples. This hypothesis needs to be confirmed as the examined plant material cannot be

considered as representative for the entire Italian distribution area of E. nebrodensis.

Finally, several interesting phytochemicals were abundantly present in the headspace

of E. nebrodensis. Beside substances useful as fragrances, metabolites with pharmaceutical

potential such as α-isocomene, β-maaliene and (E)-β-ionone, as well as unknown

compounds that need to be successfully identified, were detected.

48

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