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: [email protected] 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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References
1. Gottlieb OR. In Micromolecular Evolution, Systematics and Ecology, Springer-Verlag:
Berlin, 1982.
2. Zdero C, Bohlmann F. Plant Syst. Evol. 1990; 171: 1-14.
3. Bremer K. In Asteraceae: Cladistics and classification. Timber Press: Oregon, 1994.
4. Ling YR. Bull. Bot. Res. 1992; 12: 1-108.
5. Ling YR. In The New World Artemisia L., Advances in Compositae Systematics. Hind
DJN, Jeffrey C, Pope GV (eds). Royal Botanic Gardens: Kew, 1995; 255-281.
6. Torrel M, Garcia-Jacas N, Susanna A, Valles J. Taxon 1999; 48: 721-736.
7. Kornkven AM, Watson LE, Estes JR. Syst. Bot. 1999; 24: 69-84.
8. Seaman C. Bot. Rev. 1982; 48: 121-595.
9. Ferreira ZS, Gottlieb OR. Biochem. Syst. Ecol. 1982; 10: 155-160.
10. Emerenciano VP, Ferreira ZS, Kaplan MAC, Gottlieb OR. Phytochemistry 1987; 26:
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,
2003.
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)
15
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
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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
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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
1
2
3 4 5
6
7 8
9
10
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36 37 39
41
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47
-3,0 -2,5 -2,0 -1,5 -1,0 -0,5 0,0 0,5 1,0 1,5
FACTOR1
-3
-2
-1
0
1
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3
4
FAC
TOR
2
Figure 2
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5 6
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10 11
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-3 -2 -1 0 1 2 3 4
FACTOR2
-3
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0
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FAC
TOR
3
Figure 3
23
1
2 3
4
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15 17
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39 41
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46 47
-3,0 -2,5 -2,0 -1,5 -1,0 -0,5 0,0 0,5 1,0 1,5
FACTOR1
-4
-3
-2
-1
0
1
2
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
300
400
500
600
700
Link
age
Dis
tanc
e
Figure 5
24