CHEMOTAXONOMIC STUDY OF ARTEMISIA. AN APPROACH BASED ON

MULTIVARIATE STATISTICS OF SKELETAL TYPES RETRIEVED FROM

ESSENTIAL OILS

Corinne Depegea, Louisette Lizzani-Cuveliera, Michel Loiseaua, Daniel Cabrol-Bassa

Marcelo J.P. Ferreirab, Antônio J.C. Brantb, Júlio S.L.T. Militãoc, Vicente P. Emerencianob,*

a Laboratoire Arômes, Synthèses Interactions, Université de Nice –Sophia Antipolis, F 06108

Nice Cedex 2 France

b Instituto de Química – Universidade de São Paulo, CEP 05513-970, CP 26077, São Paulo,

SP, Brazil

c Laboratório de Química – Universidade Federal de Rondônia, Km 12 Br 364, Porto Velho,

RO, Brazil

_____________________________________________________________________

* Corresponding author: E-mail address: vdpemere@quim.iq.usp.br Fax: +55-11-38155579

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Abstract: This work describes the study of essential oils of some species of Artemisia based

on statistical methods. The composition of the essential oils of 48 plant species have been

classified on the basis of their content with respect to the carbon skeletons of their

constituents. Statistical techniques such as multiple linear regression, partial least square,

principal component analysis and cluster analysis were used in the attempt of finding

relationship correlations between the composition of the oils and the sections of the genus

according to Ling’s classification.

Key Word index: Asteraceae, Anthemideae, Artemisia, Essential oils, Multivariate Analysis.

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1. Introduction

The use of secondary metabolites as chemical markers in vegetable and

microorganism taxa has been accepted by experienced taxonomists and chemists1,2. These

researchers may also use chemical characters with macromolecular features for identification

of relationships among living organisms at any hierarchical level, especially in evolutionary

studies focusing on phylogenetic and infrataxonomic variations.

The Asteraceae is one of the most important family of plants in the world. More than

23000 species from about 1300 genera have been identified3. Known as the sunflower family,

once the Compositae or Asteraceae is known by the genus Helianthus = sunflower, it’s

distributed all over the planet in various different ecosystems. Its diversity is associated with

open areas, mostly temperate. This angiosperm family mainly consists of annual and

perennial herbs, but also includes many shrubs and a few arborescent species.

Both the chemical composition and botanical aspects of the genus Artemisia have been

intensively studied. This genus has about 350 species grouped in various sections4,5. A large

number of species of the genus (about 280) are originally from the Old World. Cladistic

studies based on internal transcribed spacers (ITS-1 and ITS-2) of nuclear ribosomal DNA

have been employed to explain the phylogeny of the genus and classify five main groups6.

Another study involving randomly amplified polymorph DNA (RAPD) discusses the

relationships between the genera Artemisia and Tanacetum. A chloroplast DNA restriction

site was also used to examine phylogeny of section Tridentatae7.

Secondary metabolite studies as a way to establish phylogeny in the Asteraceae are not

a recent subject. In 1982, Seaman published an excellent review on this topic8; since then,

several more specific studies based on polyacetylenic compounds9, sesquiterpene lactones and

flavonoids10,11 have been published. At the familial level, numerous chemotaxonomic works

published, chiefly on flavonoids and sesquiterpene lactones in plant families. A

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chemotaxonomic study encompassing nine classes of secondary metabolites at the tribal and

subtribal levels of the Asteraceae using multivariate statistics, makes possible the prediction

of coumarins based on the occurrence of other metabolites, such as sesquiterpenes, diterpenes

and flavonoids12 in the family. At genera levels, a non exhaustive list may include

benzofurans13, sesquiterepene lactones14, acetophenones15 for chemotaxonomic and

phylogenetic studies.

In the present work, we have attempted to examine whether the essential oil

composition from 48 Artemisia species could be used as chemical markers. The production of

these substances is related to their biological activity16. Therefore, the objective of this paper

is to evaluate if the production of a determined skeleton group is influenced by others

skeleton groups and to find correlations between the composition of the oils and the sections

of the genus according to Ling’s classification.

2. Methods

From the database ESO (database of Analyses of Essential Oils)17, 48 Artemisia

species from the Asteraceae family have been taken. For each essential oil, the respective

composition is described. However, for each oil remains a percentage in unknown compounds

which is comprised between 0.01 and 40%. It is noteworthy point out here that information

about the exact origin of the oils (place, date of taking, etc.) is not given. From this database,

a total of 332 different compounds were identified in the 48 essential oils (from 8 to 61

constituents per oil). Most previous works in chemotaxonomic studies of essential oils are

based on the analyses of the constituentsREFS, the processes are always similar, i.e., the results

obtained by GC-FID or GC-MS analysis and sometimes by 13C-NMR of the essential oils

from individual plants of different geographic origin are analysed by Principal Component

Analysis and Cluster Analysis, showing the presence of several types of oils based on their

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chemical composition. In order to discuss the relationships between the composition of the

essential oils and chemotaxonomy it seems more judicious to consider the similarity among

the different constituents involved and to take into account the common skeletons. Therefore,

the 332 constituents were grouped into 86 different skeletal types: 83 well-known structures

and 2 generic structures (for alkanes and for sesquiterpene alcohols).

To build the data matrix for the multivariate analysis, we proceeded from:

- 48 essential oil files (one file per oil containing the name of each constituent and

its percentage). These files have been taken from the commercial ESO database17.

- 1 compound file giving the correspondence between the compound name and the

skeleton code.

These files were processed by in-house program that checks the data errors (syntax

errors in the name of the compounds, invalid sum of percentages, missing skeleton codes,

absent compound in a file). After the correction of all errors, the program creates the raw data

matrix that consists of 48 essential oils (cases) × 85 skeletons (variables).

An accurate examination of the matrix revealed that some columns contained a large

number of zeros or very small values since some skeletons were not represented above a

significant level. So, we decided to remove the skeletons with percentages lower than 1%,

reducing the number of columns of the data matrix to only 11 skeletons. Figure 1 shows the

retained skeletons.

Some essential oils are very specific because they contain a very high level of one

particular skeleton:

- oil N°16 (Artemisia desertorum, China), which mainly contains skeleton N° 1;

- oil N°30 (Artemisia monosperma), which mainly contains skeletons N° 12;

- oil N°38 (Artemisia pubescens, China), which mainly contains skeleton N° 12;

- oil N°48 (Artemisia waltonii, China), which mainly contains skeleton N° 11.

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Thus, the essential oils N°16, 30, 38, 48 were considered as special cases (singletons).

Similarly skeleton N° 12, which is present at a significant level only in oils N° 30 and 38, was

also removed, affording a reduced data matrix that consisted of 44 essential oils × 11

skeletons. The resulting reduced data matrix is given in Table 1. These data are analysed

through Multivariate statistical analysis (Principal Component Analysis-PCA and Cluster

Analysis-CA) using statistical packages18,19.

3. Results and Discussion

A - Correlation between the composition of the essential oils with respect to the major

skeletons

The correlation matrix given in Table 2 shows a fairly high positive correlation

between the content in skeleton 7 and 8 (r7/8 = 0.77) and between the triplet 2 / 3 / 10 (r2/3 =

0.56, r2/10 = 0.55, r3/10 = 0.40). The menthane skeleton, variable 6, and its cyclic derivative,

skeleton 9, are the most negatively correlated (r6/9 = -0.46), whereas the pair 7/9 is the second

one (r7/9 = -0.35). Skeleton 11 is negatively correlated with almost all other skeletons with the

exception of skeleton 5. This result suggests that the content in skeletons of the essential oils

is not independent and calls for more detailed analysis as follows below.

The search for correlations between these eleven skeletons was done by multiple linear

regression (MLR). The best correlations were found between skeletons 6 and 9, with high F

values in MRL and good values between calibration and validation with PLS120. Another way

to explore these data matrices was the stepwise analysis of the percentages between the

skeletons (Tables 3-b and 3-c).

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B - Principal components analysis

The fairly high correlations observed between the variables justify the use of PCA in

order to have a more precise view of the dimensionality of the chemical space. The values of

the variance associated with each principal component are given in Table 4 and show that six

principal components are necessary to explain 84% of the total variance.

The results lead to the conclusion that the chemical space is rather complex and one

cannot expect to obtain simple clusters from the observation of the score plots. As a matter of

fact, rapid examination of the score plots for the 4 first principal components (PC2 versus

PC1, Figure 2; PC3 versus PC2, Figure 3; PC4 versus PC3, Figure 4) does not lead to a clear

identification of clusters of essential oils with the possible exception of a group 17, 28, 33,

36, 39, 41, 44 visible in the lower left quarter of the PC2/PC3 score plot (Figure 3). Another

exception is the cluster formed by the four A. hebraica species (22-25) in the PC1/PC2 plot.

However, these score plots will be reconsidered below, after application of clustering

methods.

Examination of the loadings of the successive principal components leads to the

following observations :

- PC1 is not clearly determined by a specific skeleton.

- PC2 is mainly positively determined by the skeletons 7 and 8 and negatively by the

skeletons 2 and 3.

- It is worth to note that the skeleton 9 in opposition to the bulk of the other skeletons

negatively determines PC3.

- Skeleton 6 in relation to other skeletons has the higher positive loading for both PC3

and PC4.

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C - Cluster analysis

The cluster analysis of the data in a viewpoint from essential oil composition and

section of genus is important. According to Ling4, the sections of the genus represented in the

essential oils extracted from the database are the folowing: Artanacetum (Art, 9 cases);

Absinthium (Abs, 17 cases); Dracunculus (Dra, 3 cases); Abrotanum (Abr, 5 cases);

Visicidipudes (Vis, 1 case) and 8 cases being not classified.

Hierachical cluster analysis (HCA) using Euclidean distance measure was performed

with different amalgamation rules: single linkage, complete linkage, weighted pair-group

centroid, weighted pair-group average or Ward’s method. The last one leads to better results

and a dendrogram (Figure 5) which is easier to interpret.

Three clusters appear clearly as separate from the other oils :

- cluster A constituted of 7 species : n°17, 28, 33, 36, 39, 41 and 44;

- cluster B constituted of 10 species : n°2, 3, 7, 8, 15, 23, 27, 29, 42 and 43;

- cluster C constituted of 26 species : n°1, 4, 5, 6, 9, 10, 11, 12, 13, 14, 18, 19, 20, 21,

22, 24, 25, 26, 31, 32, 34, 35, 37, 45, 46, 47.

Among these clusters, some oils have a singular behaviour: for example, oil n°2 in

cluster B with a high level of skeleton 1 and oil n°46 of cluster C with a high level of

skeletons 3 and 6.

Further subdivision of cluster C into 3 (D, E, F) or 4 clusters may be considered and

will be examined by the k-mean clustering method (KMC), which is non-hierarchical, but

allows the user to choose a priori the number of classes. The results obtained by KMC for 3,

5 and 6 clusters are consistent with those obtained by HCA tree clustering. These results are

summarized in Table 5.

With the results of clustering analysis in mind, one can have a second view to the PCA

score plots. From score plots involving PC1 on X axis (Figure 2), one can note that oils

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belonging to clusters D and E obtained by HCA (corresponding to cluster N°3 and N° 4

KMC/5, Table 5) are completely separated from the other oils having PC1 scores < -0.8. This

is to be related respectively to their high level of content in skeletons 7 and 10 which both

contribute by a high negative value to the loading of PC1.

Separation between clusters HCA D (N°3 KMC/5) and E (N° 4 KMC/5) is easily

achieved by PC2. Oils belonging to cluster D have positive PC2 scores due to a positive

loading of skeleton 7, while oils belonging to cluster E have negative PC2 scores due to a

negative loading of skeleton 10. As a consequence, the separation between these two clusters

is also easily observed on the PC2/PC3 score plot (Figure 3). The effect of secondary content

in skeleton 6 for cluster N°4 in KMC is only minor and is noticeable on PC3.

Cluster A (corresponding to cluster N°1 in KMC) is discriminated from the others by a

value of PC3 score < -1 (see figures 3 and 4). Again this is easily related to the high content in

skeleton 314, which has the most negative loading for factor 3.

Discrimination between the other clusters B (N°3 KMC) and F (5 and 6 in KMC/6) is

not so easily achieved by a single PC. They all contain a fairly large amount of skeleton 6 that

leads to more positive values on PC3 and PC4. On the other hand, the higher content in

skeleton 5 for cluster 6 which has a positive loading for PC1 leads us to look at PC1/PC3 and

PC1/PC4 score plots. As a matter of fact, close examination of these plots shows that oils

belonging to these clusters can be separated by a non-linear boundary in PC1/PC3 and in

PC1/PC4 score plots.

Convergence between HCA and KMC with PCA leads to the conclusion that the

resulting “chemical clusters” are robust, but no simple correspondence with the accepted

botanical classification could be put forward. The major species group is from Absintihum

section (17 cases), obviously because the genus has 90 species among the 280 from the Old

World. Few species from Abrotanum section are represented (5 cases), but they appear in the

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obtained diagram (Figure 5) in compatible position related with their essential oil

composition. Abrotanum species are in a complicated cluster with A. anethoides, A. molinieri

and A. afra. These three species belong to section Absinthium. The cluster also includes A.

subulata species of not classified section.

It is interesting to observe that the principal components from the essential oils from

Abrotanum section (the 4 A. judaica) that are described in Table 6 are quite different. In spite

of the different composition of the oils from the same species, the methodology based on

skeletal type clustering is strong, original and places them together.

4. Conclusion

Hierarchical and K-mean cluster analysis shows that essential oils of Artemisia may be

grouped in robust clusters on the basis of their composition of characteristic skeletons which

overlap only partially with botanical classification.

As indicated by several authors, “the quantitative expression of most monoterpenes

chemistry (and other components) is mainly under genetic control. Other variables, such as

environmental medium, seasonality, geographical position, etc. can play important roles in

metabolite production at lower hierarchical levels”. With this technique, we have concluded

that skeletons are not correlated one to one and that they exhibit an excellent multiple

correlation, mainly in the cases of menthane (6) and thujane (9). The forward stepwise

method allow this phenomenon visualization in two experiments of the MLR technique, in

which the gradual increase of R and F values are useful to demonstrate the equilibrium of

skeleton productions. The gradual introduction of the variable with increased F value shows

the dependence of each variable in the model and may be used to predict new essential oils

composition. The sampling from a commercial database17 mostly has essential oils classified

by Ling as Artemisia from the Old World and belonging to section Absinthium of the genus.

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An infrageneric taxonomic analysis more detailed and based on results above shows a

large variability in essential oil composition in a same section of the genus, as shown in

section Abrotanum. Using standard and widely accepted molecular systematic methods, Ling

places section Abrotanum as basal and Absithium, Dracunculus and Seriphifidium sections as

derived. Essential oil chemistry is insufficient to corroborate these statements, nevertheless

opens a way for other secondary metabolites to be joined in a wider study to collaborate with

explanations on the classification and evolution of the genus.

Acknowledgements

We gratefully thank the FAPESP (Fundação de Amparo à Pesquisa do Estado de São

Paulo), the Universisty of Nice-Sophia Antipolis for a fellowship as visiting Professor (V.P.

Emerenciano) and the CNPq (Conselho Nacional de Desenvolvimento Científico e

Tecnológico, A.J.C. Brant and V.P. Emerenciano).

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3103-3115.

11. Alvarenga SAV, Ferreira MJP, Emerenciano VP, Cabrol-Bass D. Chemom. Intell. Lab.

Syst. 2001; 56: 27-37.

12. Brant AJC. In Coumarins, flavonoids and benzofurans as chemical markers in the

Asteraceae, sensu Bremer, Master in Sciences Dissertation, São Paulo University, Brazil,

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13. Proksch P, Rodriguez E. Phytochemistry 1983; 22: 2335-2355.

14. Kelsey RG, Shafizadeh F. Phytochemistry 1979; 18: 1591-1611.

15. Proksch P, Kunze A. In Chemosystematic evidence from prenylated acetophenones –

conclusions at the tribal, inter – and intrageneric level, Hind DJN, Beentje HJ (eds).

Procedings of the International Compositae Conference: Kew, 1994.

16. Stojanovic G, Palic R, Mitrovic J, Djokovic D. J. Essent. Oil Res. 2000; 12: 621-624.

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17. ESO 99, Database of Essential oils. Boelens Aroma Chemical Service, 1999.

18. Statistica AXD 6.0, Statsoft Inc. Tulsa: USA, 2002.

19. The Unscrambler 6.1 CAMO ASA, Oslo, Norway, 1996.

20. Geladi P, Kowalski BR. Anal. Chim. Acta 1986; 185: 1-17.

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Table 1. Reduced data matrix: Percentage of principal skeletal types present in Artemisia oils

Skeletons

Essential Oils Section 1 2 3 4 5 6 7 8 9 10 11

1 – A. abaensis Art 0.47 0.00 0.00 1.28 7.33 11.04 18.62 0.00 2.08 0.00 0.21

2 – A. abrotanum Abr 18.28 4.00 0.02 0.04 0.04 66.25 3.32 0.03 0.85 2.89 0.00

3 – A. afra Willd. Abs 0.00 0.09 0.58 0.04 0.00 81.10 5.32 0.12 1.15 1.27 0.00

4 – A. aksaiensis Abs 0.00 0.00 0.00 0.00 1.31 28.53 16.16 0.00 5.86 0.00 0.00

5 – A. alba (B) Abs 0.00 0.50 0.01 0.00 0.00 6.22 21.30 4.10 0.01 46.41 0.00

6 – A. alba (I) Abs 0.02 1.70 1.70 0.31 0.61 18.12 43.50 4.42 0.71 19.92 0.00

7 – A. anethifolia Abs 0.00 0.00 0.20 0.00 0.00 49.68 3.66 0.00 17.10 1.91 0.00

8 – A. anethoides Abs 0.00 0.00 0.00 0.73 1.04 72.79 1.29 0.00 0.00 0.00 0.00

9 – A. arborescens Abs 0.00 0.00 0.43 0.00 0.00 22.58 17.39 4.61 3.68 6.18 0.00

10 – A. argentea Abs 0.00 0.90 9.80 8.90 0.00 36.75 0.00 0.00 1.13 20.70 0.00

11 – A. atrovirens Vis 5.31 0.00 2.43 1.14 20.97 11.53 3.90 0.00 0.00 0.93 0.00

12 – A. austriaca Abs 0.00 0.00 0.90 1.21 0.00 39.50 45.80 6.60 0.30 3.90 0.01

13 – A. campestris1 Dra 2.70 9.30 18.21 0.83 0.04 19.97 0.03 1.11 1.16 34.84 0.00

14 – A. campestris2 Dra 0.02 18.70 5.71 0.33 0.04 28.67 4.01 0.62 1.35 37.03 0.00

15 – A. chamaemelifolia Abs 6.00 3.40 0.60 0.13 0.04 35.14 0.12 0.03 2.60 0.97 0.00

16 – A. desertorum* 69.92 0.00 0.00 0.81 0.00 0.00 0.00 0.00 0.44 0.00 0.00

17 – A. genepi Abs 0.02 0.80 0.02 0.13 0.04 5.46 0.21 0.03 90.86 2.35 0.00

18 – A. glacialis Abs 0.02 9.30 4.60 0.23 0.62 28.92 32.51 7.71 2.94 6.94 0.00

19 – A. gmelini Abs 6.44 0.00 1.34 0.00 29.58 23.03 3.54 0.00 11.99 0.79 0.00

20 – A. herba-alba 0.00 0.00 0.80 0.00 0.10 21.30 11.10 1.80 44.10 6.65 0.00

21 – A. indica Art 0.00 0.00 0.00 0.00 37.27 21.23 6.67 0.00 0.00 0.00 0.00

22 – A. judaica (E1) Abr 0.00 0.00 0.00 0.05 18.44 18.36 10.80 1.45 0.07 8.38 10.67

23 – A. judaica (E2) Abr 0.00 0.04 0.00 0.35 0.00 38.95 15.15 0.15 0.00 7.23 18.72

24 – A. judaica (I1) Abr 0.00 0.03 0.00 0.49 26.19 17.31 7.10 0.03 0.00 1.19 23.46

25 – A. judaica (I2) Abr 0.00 0.03 0.00 0.85 25.65 17.87 13.90 0.60 0.10 7.80 9.70

26 – A. kawakamii 0.64 0.00 0.00 1.00 0.00 5.71 33.01 0.00 22.17 0.32 1.32

27 – A. macrocephala Abs 0.00 0.00 0.00 11.27 0.00 45.60 8.03 0.00 0.12 0.00 0.00

28 – A. maritima 0.00 0.00 1.00 0.00 0.00 9.42 0.00 0.00 72.04 0.54 0.00

29 – A. molinieri Abs 0.28 0.00 0.00 0.04 0.00 88.01 0.00 0.08 0.26 0.06 0.20

30 – A. monosperma* 0.00 0.00 0.00 0.50 0.50 0.00 0.10 0.00 0.00 0.00 0.00

31 – A. moorcroftiana Art 0.00 0.96 0.34 0.34 14.50 16.26 11.08 1.80 22.01 16.78 0.00

32 – A. nilagirica vulgaris1 Art 0.00 1.92 0.31 2.30 3.41 9.93 10.64 2.59 1.12 1.76 0.00

33 – A. nilagirica vulgaris2 Art 0.00 0.01 1.51 2.68 0.00 6.05 3.03 0.45 63.81 2.48 0.92

34 – A. occi-sichaunensis Art 0.85 0.00 5.02 4.16 0.64 15.19 14.94 0.00 5.85 0.65 0.20

35 – A. persica Abs 3.20 0.19 0.00 0.00 31.70 19.45 0.40 0.00 28.96 0.69 0.00

36 – A. petrosa 0.02 4.10 0.31 0.13 0.04 3.16 0.22 0.62 87.51 1.86 0.00

37 – A. princeps Art 1.22 1.41 11.18 0.89 2.37 9.17 9.21 0.00 0.00 3.64 0.72

38 – A. pubescens* 1.20 0.00 0.00 0.45 0.00 2.96 2.03 0.00 0.00 0.00 0.00

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Table 1. Continued

Skeletons

Essential Oils Section 1 2 3 4 5 6 7 8 9 10 11

39 – A. roxburghiana Art 0.00 0.11 2.90 0.03 0.00 4.33 0.00 0.00 74.01 0.02 0.01

40 – A. scoparia* Dra 0.00 0.00 0.10 0.05 0.00 0.05 0.00 0.00 0.00 0.10 30.05

41 – A. speciosa 0.25 0.00 0.00 0.00 1.40 1.19 3.26 0.00 78.10 0.00 0.00

42 – A. spicigera 0.00 0.00 0.20 1.70 0.00 63.80 20.80 4.90 1.40 1.80 0.00

43 – A. subulata 0.00 0.00 0.79 0.00 0.00 57.04 27.82 0.00 0.00 0.00 0.23

44 – A. umbelliformis Abs 0.02 0.10 0.90 0.13 0.03 6.36 0.42 0.03 89.68 1.46 0.00

45 – A. vallesiaca 0.02 0.50 0.40 0.13 0.04 17.55 68.39 7.42 0.63 4.64 0.00

46 – A. verlotiorum Abr 0.02 9.20 21.23 3.83 0.04 21.10 28.46 3.72 0.86 10.37 0.00

47 – A. vestita Abr 6.85 0.00 2.35 0.47 10.91 8.96 1.98 0.52 5.21 2.81 0.00

48 – A. waltonii* 0.22 0.00 0.00 0.30 0.00 6.84 1.13 0.00 0.00 0.00 65.70 * Removed from the data matrix before analysis (see text)

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Table 2. Correlation matrix

Skeletons 1 2 3 4 5 6 7 8 9 10 11

1 1.00 0.08 -0.01 -0.12 0.14 0.16 -0.23 -0.20 -0.16 -0.10 -0.12

2 0.08 1.00 0.56 -0.01 -0.21 -0.01 -0.00 0.19 -0.13 0.55 -0.13

3 -0.01 0.56 1.00 0.29 -0.18 -0.12 0.00 0.09 -0.17 0.40 -0.14

4 -0.12 -0.01 0.29 1.00 -0.16 0.08 -0.04 -0.07 -0.18 0.03 -0.08

5 0.14 -0.21 -0.18 -0.16 1.00 -0.20 -0.19 -0.22 -0.17 -0.15 0.33

6 0.16 -0.01 -0.12 0.08 -0.20 1.00 -0.03 -0.02 -0.46 -0.14 -0.04

7 -0.23 -0.00 0.00 -0.04 -0.19 -0.03 1.00 0.77 -0.35 0.09 -0.02

8 -0.20 0.19 0.09 -0.07 -0.22 -0.02 0.77 1.00 -0.25 0.26 -0.13

9 -0.16 -0.13 -0.17 -0.18 -0.17 -0.46 -0.35 -0.25 1.00 -0.23 -0.17

10 -0.10 0.55 0.40 0.03 -0.15 -0.14 0.09 0.26 -0.23 1.00 -0.04

11 -0.12 -0.13 -0.14 -0.08 0.33 -0.04 -0.02 -0.13 -0.17 -0.04 1.00

Significant values are italicized.

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Table 3-a. Multiple linear regression correlation among skeletal types from essential oils of

Artemisia Dependent

variable Independent variables(a)

Multiple R, Calibration(b)

Multiple R, Validation(b)

Adjusted R2

F PLS1, Calibration(b)

PLS1, Validation(b)

Skeleton 1 All-Ske1 0.695 0.262 0.304 2.715 0.273 -0.009 Skeleton 2 All-Ske2 0.729 0.296 0.370 3.578 0.668 0.456 Skeleton 3 All-Ske3 0.797 0.494 0.510 5.232 0.761 0.517 Skeleton 4 All-Ske4 0.624 0.279 0.180 1.631 0.208 -0.017 Skeleton 5 All-Ske5 0.897 0.803 0.737 12.398 0.894 0.824 Skeleton 6 All-Ske6 0.967 0.923 0.913 44.275 0.956 0.938 Skeleton 7 All-Ske7 0.840 0.712 0.820 18.889 0.923 0.881 Skeleton 8 All-Ske8 0.848 0.618 0.623 7.706 0.816 0.765 Skeleton 9 All-Ske9 0.980 0.954 0.948 72.323 0.974 0.963

Skeleton 10 All-Ske10 0.902 0.787 0.750 12.432 0.897 0.786 Skeleton 11 All-Ske11 0.780 0.450 0.474 10.799 0.732 0.472

(a) Ske: Skeleton; (b) Obtained with Unscrambler

Table 3-b. A selected example of a forward stepwise regression with skeleton 6

Step Variables R F 1 Skeleton 09 0.434 9.735 2 Skeleton 10 0.714 13.944 3 Skeleton 07 0.787 15.869 4 Skeleton 05 0.886 27.709 5 Skeleton 03 0.920 33.795 6 Skeleton 11 0.943 41.016 7 Skeleton 01 0.957 44.036 8 Skeleton 04 0.959 43.357 9 Skeleton 08 0.966 45.638 10 Skeleton 02 0.967 42.020(a)

(a) Not important in analysis Table 3-c. A selected example of a forward stepwise regression with skeleton 9

Step Variables R F 1 Skeleton 6 0.434 9.735 2 Skeleton 10 0.739 16.023 3 Skeleton 7 0.840 23.409 4 Skeleton 5 0.917 40.067 5 Skeleton 3 0.942 48.947 6 Skeleton 11 0.960 59.843 7 Skeleton 1 0.970 70.721 8 Skeleton 4 0.975 73.624 9 Skeleton 8 0.979 77.817 10 Skeleton 9 0.980 72.323

17

Table 4. Eigenvalues obtained by principal component analysis (PCA) Principal components 1 2 3 4 5 6 Eigenvalue 2.523 1.772 1.570 1.381 1.167 0.832 Variance (%) 22.94 16.11 14.27 12.56 10.61 7.56 Cumulative Variance (%) 22.94 39.05 53.32 65.88 76.49 84.05

18

Table 5. Clusters obtained by application of the K-mean cluster (KMC) method.

Clusters Oil n° Essential Oil name Principal skeleton n°

Secondary skeleton n°

1

17 20 28 33 36 39 41 44

A. genepi (Italy) A. herba-alba (Morocco) A. maritima (Himalaya) A. nilagirica vulgaris (India 2) art A. petrosa (Italy) A. roxburghiana (Himalaya) art A. speciosa (China) A. umbelliformis (Italy) abs

9

2

2 3 7 8

27 29 42 43

A. abrotanum (Italy) abr A. afra Willd (Kenya) abs A. anethifolia (China) abs A. anethoides (China) abs A. macrocephala (China) abs A. molinieri abs A. spicigera (Turkey) A. subulata (China)

6

3/5 & 3/6

6 12 18 26 45 46

A. alba (Italy) abs A. austriaca (Turkey) abs A. glacialis (Italy) abs A. kawakamii (China) A. vallesiaca (Italy) A. verlotiorum (Italy) abr

7

4/5 & 4/6

5 10 13 14

A. alba (Belgium) A. argentea (Madeira) abs A. campestris (Italy 1) dra A. campestris (Italy 2) dra

10 6

5/6

11 19 21 22 25 31 35 47

A. atrovirens (China) vis A. gmelini (Himalaya) abs A. indica (China) art A. judaica (Egypt 1) abr A. judaica (Israel 2) abr A. moorcroftiana art A. persica (India) abs A. vestita (India) abs

5 6

3/3

5/5

6/6

1 4 9

15 23 24 32 34 37

A. abaensis (China) art A. aksaiensis (China) abs A. arborescens (U.S.A.) abs A. chamaemelifolia (Italy) abs A. judaica (Egypt 2) abr A. judaica (Israel 1) abr A. nilagirica vulgaris (India 1) art A. occi-sichaunensis (China) art A. princeps (China) art

6 7

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Table 6. Different compositions of essential oils from A. judaica (Abrotanum section)

Taxon 1 2 3 4 5 6 7 8

Artemisia judaica (E1) 13.98 7.80 3.01 20.96 5.76 0.00 0.00 4.54

Artemisia judaica (E2) 6.50 2.50 7.50 13.50 10.50 0.00 0.00 0.00

Artemisia judaica (I1) 5.56 0.39 3.25 14.42 9.52 6.68 17.53 2.86

Artemisia judaica (I2) 13.68 4.44 11.53 0.00 11.53 36.98 6.84 1.85

Components: 1. (E)-ethylcinnamate; 2. (Z)-ethylcinnamate; 3. artemisia alcohol; 4. artemisia ketone;

5. camphor; 6. piperitone; 7. chrysanthenone; 8. 3,5,5-trimethyl-2-cyclohexen-1-one

20

21

Figure captions

Figure 1. Skeletons retained for analysis

Figure 2. Score plot Factor1 / Factor2

Figure 3. Score plot Factor2 / Factor3

Figure 4. Score plot Factor1 / Factor4

Figure 5. Dendrogram for 43 essential oils from Artemisia species, obtained by the Ward’s

method and Euclidean distance measure.

22

1 2 3 4 5

6 7 8 9 10 11

Figure 1

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-3,0 -2,5 -2,0 -1,5 -1,0 -0,5 0,0 0,5 1,0 1,5

FACTOR1

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FAC

TOR

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Figure 2

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-3 -2 -1 0 1 2 3 4

FACTOR2

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Figure 3

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-3,0 -2,5 -2,0 -1,5 -1,0 -0,5 0,0 0,5 1,0 1,5

FACTOR1

-4

-3

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-1

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1

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3

FAC

TOR

4

Figure 4

Ward`s method

Euclidean distances

33 41 39 28 36 44 17 43 42 23 27 15 7 8 29 3 2 45 12 46 18 6 14 13 10 5 35 21 19 24 25 22 47 11 26 31 20 9 4 37 34 32 10

100

200

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Link

age

Dis

tanc

e

Figure 5

24