research article comparative analysis of the...

Hindawi Publishing CorporationJournal of Analytical Methods in ChemistryVolume 2013 Article ID 246986 9 pageshttpdxdoiorg1011552013246986

Research ArticleComparative Analysis of the Volatile Components ofAgrimonia eupatoria from Leaves and Roots by GasChromatography-Mass Spectrometry and MultivariateCurve Resolution

Xiao-Liang Feng1 Yun-biao He2 Yi-Zeng Liang3 Yu-Lin Wang1

Lan-Fang Huang1 and Jian-Wei Xie1

1 School of Chemical and Material Engineering Quzhou College Quzhou 324000 China2 Changde Institute for Food and Durg Control Changde 415000 China3 College of Chemistry and Chemical Engineering Central South University Changsha 410083 China

Correspondence should be addressed to Lan-Fang Huang fq18guoyahoocomcn

Received 10 June 2013 Revised 8 August 2013 Accepted 20 August 2013

Academic Editor Shao-Nong Chen

Copyright copy 2013 Xiao-Liang Feng et al This is an open access article distributed under the Creative Commons AttributionLicense which permits unrestricted use distribution and reproduction in any medium provided the original work is properlycited

Gas chromatography-mass spectrometry and multivariate curve resolution were applied to the differential analysis of the volatilecomponents inAgrimonia eupatoria specimens fromdifferent plant parts After extractedwithwater distillationmethod the volatilecomponents in Agrimonia eupatoria from leaves and roots were detected by GC-MSThen the qualitative and quantitative analysisof the volatile components in the main root of Agrimonia eupatoria was completed with the help of subwindow factor analysisresolving two-dimensional original data into mass spectra and chromatograms 68 of 87 separated constituents in the total ionchromatogram of the volatile components were identified and quantified accounting for about 8703 of the total content Thenthe common peaks in leaf were extracted with orthogonal projection resolution method Among the components determinedthere were 52 components coexisting in the studied samples although the relative content of each component showed difference tosome extent The results showed a fair consistency in their GC-MS fingerprint It was the first time to apply orthogonal projectionmethod to compare different plant parts of Agrimonia eupatoria and it reduced the burden of qualitative analysis as well as thesubjectivityThe obtained results proved the combined approach powerful for the analysis of complexAgrimonia eupatoria samplesThe developed method can be used to further study and quality control of Agrimonia eupatoria

1 Introduction

Agrimonia eupatoria one of Rosaceae plant family mainlylocates in Zhejiang Yunnan Guangdong Guangxi and otherplaces of China [1] As a traditional Chinesemedicine (TCM)listed in the Chinese Pharmacopoeia Agrimonia eupatoriahas long been used to cure many diseases such as tumorsMenierersquos syndrome and trichomonas vaginitis [2 3] It alsohas the function of antitumor antidiabetic hemostatic andantibacterial [4ndash6] Although Agrimonia eupatoria containsup to tens or even hundreds of compounds only a limitednumber of compounds such as agrimony agrimony lactone

tannin flavonoids glycosides and the volatile componentsmight be the main active components which are responsiblefor pharmaceutical or toxic effects [7 8] To ensure thereliability and repeatability of pharmacological and clinicalresearch and understand their bioactivities and possible sideeffects of active compounds it is necessary to study all of thephytochemical constituents of botanical extracts and developa method for quality control of Agrimonia eupatoria Forexample the volatile constituents are known to exhibitpharmacological and biological activity and it is used forsterilization and antibacterial active role [8]Thus the analysis

2 Journal of Analytical Methods in Chemistry

of the volatile ingredients in Agrimonia eupatoria is veryimportant

As for the analysis of the volatile compounds in Agri-monia eupatoria only a few reports have been seen in theliterature [9 10] They are usually performed with gas chro-matography (GC) and gas chromatography-mass spectrom-etry (GC-MS) which are based on gas chromatographicretention indices or MS for qualitative and quantitative anal-ysis However because the composition of Agrimonia eupa-toria is very complicated and the contents of many impor-tant volatile components in Agrimonia eupatoria are verylow suitable sample-preparing methods are necessary beforedetection by GC-MS such as steam distillation [9] Althoughpreparing methods are used for the analysis process of thecomplicated Agrimonia eupatoria samples it is still impossi-ble to obtain complete separation of all the volatile chemicalconstituents of Agrimonia eupatoria In these general GCor GC-MS reports it is difficult to assess the purity ofchromatographic peaks and the peak inspected as one com-ponent may be a mixture of several components The resultsobtained by thesemethodswhich have beenmentioned abovewould be questionable Fortunately with the developmentof hyphenated instruments multidimensional data revealingthe compositions of samples can be obtained from GC-MSHPLC-DAD and so onThen many associated chemometricmethods [11ndash18] which can be used to resolve multidimen-sional data have been developedThus more information forchemical analysis both in chromatographic separation andin spectral identification can be obtained which makes itpossible to interpret these complex systems

On the other hand there may be some sameness and dif-ferences to exist in Agrimonia eupatoria from different plantparts To find the pharmacological active components thatexist in essential oils exactly it is important that the methodfor the detailed study of the components in Agrimonia eupa-toria from different plant parts such as the root and leaf wasestablished

In this paper two chemometrics methods subwindowfactor analysis [13] and orthogonal projection resolution(OPR) [14] were used to analyze the volatile constituents ofAgrimonia eupatoria from different plant parts for the firsttime The volatile components of Agrimonia eupatoria fromtwo different plant parts were extractedwithwater distillationand subjected to GC-MS analysis Firstly the qualitative andquantitative analysis of volatile components in the main rootof Agrimonia eupatoria was completed with the help of sub-window factor analysis Secondly the common peaks inleaf of Agrimonia eupatoria were extracted with orthogonalprojectionmethodAt last to those constituents in leaf whichwere not identified withOPRmethod the qualitative analysiswas also performed with subwindow factor analysis Thena simple and reliable combined approach for the systematicstudy of the volatile constituents in the main root and leafAgrimonia eupatoria was developed Not only more infor-mation was obtained but also the reliability of componentswas improved The obtained results can provide foundationfor further development of fingerprint and quality control ofAgrimonia eupatoria

2 Theory and Method

21 Subwindow Factor Analysis (SFA) The detailed processof SFA has been described in the literature [13] here only abrief depiction of the method is given

According to the Lambert-Beer Law a two-dimensionaldata X

119898times119899produced by hyphenated instruments can be

expressed as the product of two matrices as follows

X119898times119899= C119898times119901

S119879119899times119901+ E (1)

where X119898times119899

denotes response matrix representing 119901 com-ponents of 119898 spectra measured at regular time intervals andat 119899 different wavelengths or mass-to-charge ratios Matrix Cis the pure composed of 119901 columns each one describing thechromatographic concentration profile of a pure chemicalspecies Similarly the matrix S119879 consists of 119901 rows corre-sponding to pure spectra of the chemical species Matrix Edenotes measurement noiseThe superscript119879 represents thetranspose of matrix

It is crucial to identify left and right subwindows of SFAIn the former an interfering compound starts to elute beforethe analyte appears in a chromatogram to the left of the ana-lyte and in the latter another interference continues to eluteafter the analyte has stopped eluting The rank analysis canprovide the number of chemical components of the left andright ones say 119898

1and 119898

2 respectively And the number of

components in the combination of left and right ones is1198981+ 1198982minus 1 since the analyte is common to both One may

then find an orthogonal basis g1 g2 g1198981 spanning the

spectral subspace of the left subwindow and a similar basisf1 f2 f1198982 spanning that of the right subwindow cor-

responding to matrices G and F by means of singular-value decomposition The common spectral vector v to bothsubspaces can be written as linear combinations of both setsof basis for an ideal case

v = Ga

v = Fb(2)

Under the conditions a119879a = b119879b = 1 In reality Ga and Fbare not identical on account of interference from noise andbackground and so forth And we search for vectors a and bwhich minimize the squared norm

119873 = Ga minus Fb2 = a119879G119879Ga + b119879F119879Fb minus 2a119879G119879Fb (3)

SinceG119879G = I1198981andF119879F = I119898

2(unitmatrices of dimension

1198981times 1198981 and119898

2times 1198982 resp) we obtain

119873 = 2 minus 2a119879G119879Fb (4)

Here if a and b are the left and right singular vectorsrespectively associated with the first largest singular value 119889

1

of the matrixG119879F inserting this result in (3) we again obtain

119873 = 2 (1 minus 1198891) (5)

The singular values 119889119894of the matrix G119879F are in the range

0 le 119889119894le 1 and the larger the value of 119889

1is the closer

Journal of Analytical Methods in Chemistry 3

the agreement between Ga and Fb is Thus it makes aspectrum control possible that only vector v is common forthe left and right two windows Therefore one can directlyobtain component spectra If all the pure spectra are availablethe concentration profiles could be achieved by using priorinformation of spectra and linear regression

C = XS(S119879S)minus1

(6)

It is worth noting that if there is no common vector the largestsingular value 119889

1will be significantly less than 1 On the other

hand if there are two or more common vectors the secondsingular value 119889

2 even the third one or more will also be

close to 1 In both cases one lacks information for the uniqueidentification of the spectral vector v

22 Orthogonal Projection Resolution (OPR) Because it hasbeen described in detail in the literature [14] a brief depictionof orthogonal projection resolution was given as follows

The orthogonal projection matrix P119894on to the comple-

mentary subspace119883119879119894is defined as

P119894= 119868 minus X119879

119894(X119879119894)

+

(7)

where the superscript + denotes the Moore-Penrose pseu-doinverse and I designates the identity matrix119883119879

119894represents

different submatrices which are a series of fixed size windowmatrices moving along the chromatographic direction

Assume that the subspace spanned by themixture spectrain119883119879119894isM The residue vector r

119894is given by

r119894= P119894k119886 (8)

where k119886denotes the spectrum of certain component that

is resolved by the SFA and r119894is the projection of k

119886on the

orthogonal complementary subspace of M Therefore onehas the length of the residue vector

re119894=

1003817100381710038171003817

r119894

1003817100381710038171003817

2

(119894 = 1 2 119898 minus 119908 + 1) (9)

where r119894 designates the Euclidean norm of the vector 119898 is

the number of measured chromatographic points and 119908 isthe size of window

Plotting the valve of re119894versus the index 119894 one can obtain

a graph Here we call it spectrum projection graph which cantell us whether the component is present or absent and wherethe component elutes Suppose that the submatrixX

119894contains

component 119886 Then the spectrum of component 119886 is in thesubspaceM spanned by the mixture spectra in119883119879

119894 hence the

length of the residue vector will be close to zero Otherwiseif the component a is not in the submatrix 119883119879

119894 then re

119894will

have a relatively large valve

3 Experimental

31 Instruments GC-MS was performed with ShimadzuGCMS-QP2010 instrument The volatile constituents in boththe main root and leaf ofAgrimonia eupatoriawere separatedon a 30m times 025mm ID fused silica capillary column coatedwith 025120583m film OV-1

4

3

2

1

010 20 30 40 50

Retention time (min)

Inte

nsity

(times107)

C

Figure 1 The chromatograms of the volatile components in mainroot of Agrimonia eupatoria

32Materials and Regents Themain root and leaf ofAgrimo-nia eupatoria were obtained from a Zhejiang herbs nurseryand were identified by a researcher from Institute of MateriaMedica Hunan Academy of Traditional Chinese Medicineand Materia Medica Ether and anhydrous sodium sulfatewere of analytical grade

33 Extraction of the Volatile Components The main rootand leaf of Agrimonia eupatoria were dried at 40∘C for about40min Some 400 g dried Agrimonia eupatoria and 1200mLdistilled water were premixed then placing them into astandard extractor The mixture was allowed to stand for30min at room temperature before extracting the essentialoil Essential oil was obtained by the standard extractingmethod for essential oil in TCMs according to the ChinesePharmacopoeia [19] Effluent was extracted with ether andthe ether was removed by blowing with nitrogen under lowtemperature The obtained essential oils were dried withanhydrous sodium sulfate and stored in the refrigerator at 4∘Cprior to analysis

34 Detection of Essential Oil GC-MS was used to obtainchromatograms of essential oils The oven was held at 70∘Cfor 1min during injection then temperature programmedat 3∘Cminminus1 to a final temperature of 210∘C and held for5min Inlet temperature was kept at 270∘C all the time 10 120583Lvolume of essential oil was injected into the GC Heliumcarrier gas at a constant flow-rate of 10mLsdotminminus1 and a 5 1split ratio were used simultaneously Mass spectrometer wasoperated in full scan and electron impact (EI+) modes withan electron energy of 70 eV interface temperature 270∘CMS source temperature 230∘C MS quadrupole temperature160∘C In the range of 119898119911 30 to 500 mass spectra wererecorded with 312 ssdotscanminus1 velocity

35 Data Analysis Data analysis was performed on a Pen-tiumbased IBM compatible personal computer All programsof the chemometrical resolution methods were coded inMATLAB 65 for windows The library searches and spectralmatching of the resolved pure components were conductedon theNational Institute of Standards andTechnology (NIST)MS database containing about 107000 compounds


12

10

8

6

4

2

0114 115 116


Inte

nsity

(times105)

(a)

6

5

4

3

2

Log

(eig

enva

lue)

114 115 116Retention time (min)

(b)

Figure 2 The total ion chromatogram (TIC) of the peak cluster C (a) and its rank map

100

80

60

40

20

00 50 100 150

Inte

nsity

Standard

105

133

134

mz

(a)

mz

10080604020

00 50 100 150

Inte

nsity

Component 1

105

133134

(b)

Figure 3 Resolved mass spectrum of component 1 by SFA and standard mass spectrum of 34-dimethylbenzaldehyde

4 Results and Discussion

41 Resolution of the Overlapping Peaks The total ioniccurrent (TIC) chromatogram of the volatile components inmain root of Agrimonia eupatoria was shown in Figure 1(its data matrix was denoted as X

1) The intensities of

the peaks recorded vary greatly Although many chromato-graphic peaks are separated here still some of eluted com-ponents overlapped and the concentrations of some volatilecomponents were very low If directly searched in the NISTmass database incorrect identification of compoundsmay beobtained There were two reasons for this First if the chro-matographic peaks were directly searched with the NIST MSdatabase the similarity indices (SIs) for many of these com-pounds were quite low Sometimes the same component wassearched at different retention time Another reason sincepeaks associated with column background and residual gasesexisted unavoidably in two-dimensional data obtained bymass spectral measurement the component with low con-centration was very difficult to be identified directly withthe NIST mass database However if these overlapped peaks

and the components with low content were resolved intopure spectra and chromatograms the identification of com-ponents can be improved to a reliable extent

The matrix (X1) was divided into many submatrix The

chromatographic segment X within 1136ndash1170min namedpeak cluster C was taken as an exampleThewhole procedureof this approach was demonstrated as follows

Figure 2(a) was an original chromatogram from 1136minto 1170min (peak cluster C) Intuitively therewere two chem-ical components in this overlapping peak However it wasimpossible to get the correct qualitative and accurate quanti-tative results if this overlapping peak was identified directlywith automatic integration and mass similarity matchingprovided by GC-MS workstationThe quantitative analysis ofthis peak cluster was also impossible because the area of eachcomponent cannot be obtained Here SFA [13] was used toresolve this overlapped peak with high efficiency and accept-ed accuracy

First fix-sized moving window evolving factor analysis(FSMWEFA) was used to obtain the rank map after back-ground correction with PCA [11] The eluting sequences of


10080604020

00 20 40 60 80 100 120 140 160 180 200

Inte

nsity

Standard

43

41

95

93105

111

133

134

71

67

mz

(a)

10080604020

00 20 40 60 80 100 120 140 160 180 200

Inte

nsity

Component 2

43

41

95

93

105111

133

134

71

mz

(b)


100

50

00 50 100 200150

Inte

nsity

Standard

39

4191

105

119 161

133

937969

mz

(a)

100

50

00 50 100 200150

Inte

nsity

Component 4

3941

55

91105

119106

161

133

93

79

81

69

mz

(b)

Figure 5 Resolved mass spectrum of component 4 by SFA and standard mass spectrum of 2-cyclopropylidene-177-trimethyl-bicyolo[221]heptane

individual components can be seen from the rank mapwhich was shown in Figure 2(b) A clear insight into peakcluster C was shown in the rank map Then the number ofpure components hidden in the peak cluster and the elutinginformation of each component can be obtained Determina-tion of both left and right subwindows of each component forthe use of SFA also became clear with the informationmentioned above Then the pure spectrum of each com-ponent can be extracted by SFA directly by analyzing thecorrelation of two subwindows without previous resolutionof their concentration profiles The corresponding extractedmass spectrum of components 1 2 and 4 was shown inFigures 3 4 and 5 respectively After all the pure spectra hadbeen obtained the concentration profiles could be generatedby using prior information of spectra and linear regressionC = XS (S119879S)minus1 (see (6) in Section 21) which were shownin Figure 6

42 Qualitative Analysis Identification of the componentsin cluster C can be conducted by similarity searches in theNISTmass database and verifiedwith retention indices wheneach pure spectrum in cluster C was extracted and theresolved chromatographic profiles of these five componentswere obtained with SFA Components 1 2 and 4 may be 34-dimethylbenzaldehyde 24-dimethylbenzaldehyde and 2-cyclopropylidene-177-trimethyl-bicyolo[221] heptane withthe respective match values of 0947 0967 and 0954 (seeFigures 3 4 and 5 resp) The match values of 073 and 068

0

2

4

6

8

10

1140 1145 1150 1160 1165 117011551135Retention time (min)

Inte

nsity

(times105)

1

2

3

4

5

Figure 6 The resolved chromatogram of cluster C

for components 3 and 5 respectively were too low for reliableidentification of their chemical nature

In the same way the spectrum of each component inother segments can be obtained Then the correspondingidentification of all the volatile components in main root ofAgrimonia eupatoria was acquired The qualitative resultswere listed in Table 1 68 of 87 separated constituents in thetotal ion chromatogram of the volatile components in mainroot of Agrimonia eupatoria were identified Comparingthe obtained result with those of the literature [9 10] thiscombined approach was more reliable andmore componentswere identified satisfactorily

43 Quantitative Analysis Quantitative analysis was per-formed with the overall volume of two-way response of each


Table 1 Identification and quantification of the volatile chemical constituents in main root and leaf of Agrimonia eupatoria

Series no Retention time (min) Compound name Molecule structure Relative content ()X1

a X2

b

1 4639 4612 120572-Pinene C10H16

8312 4853 mdash Hexanal C

6H12O 005

3 5172 5123 120573-Pinene C10H16

1274 5368 5322 Camphene C

10H16

3215 5714 5692 3-Octanol C

10H18O 027

6 6032 5971 Cymene C10H14

0187 6354 6289 D-Limonene C

10H16

1298 6632 6617 Eucalyptol C

10H18O 326

9 7290 7195 120572-trans-Ocimene C10H16

05110 7572 7547 Linalool C

10H18O 572

11 8157 8093 120572-Campholenal C10H16O 072

12 8682 8631 L-Camphor C10H16O 211

13 9104 mdash Borneol C10H18O 007

14 10241 10196 4-Terpineol C10H18O 147

15 10473 10432 120572-Terpineol C10H18O 421

16 10761 mdash p-Menth-1-en-4-ol C10H18O 006

17 10941 10906 Pulegone C10H16O 017

18 11417 mdash 34-Dimethylbenzaldehyde C9H10O 041

19 11472 11427 24-Dimethylbenzaldehyde C9H10O 072

20 11576 11512 2-Cyclopropylidene-177-trimethyl-bicyolo [221]heptane C13H20

05221 11712 11621 1-(2-Furyl)-1-hexanone C

10H14O2

48722 11801 11762 Bergamot oil C

12H20O2

14223 12129 mdash Nonanoic acid C

9H18O2

00624 12374 12327 2-Methyl-4-hydroxyacetophenone C

9H20O2

01025 12871 12821 Thymol C

10H14O 082

26 12902 mdash Carvacrol C10H14O 044

27 13914 13865 Anethole C10H12O 007

28 14265 14211 Bornyl acetate C12H20O2

37229 14794 14738 Neryl acetate C

12H20O2

04730 14917 14872 Geraniol acetate C

12H20O2

06131 15504 mdash Furan25-dibutyl- C

12H20O 004

32 15765 15718 Decanoic acid C10H20O2

00633 16020 15951 Eugenol methyl ether C

11H14O2

05234 16812 16762 120572-Cedrene C

15H24

28735 17059 17012 120572-Longipinene C

15H24

14236 17215 17153 Caryophyllene C

15H24

08137 17475 mdash 120573-Cedrene C

15H24

01438 18176 18093 Geranyl acetone C

13H22O 084

39 19721 mdash Copaene C15H24

00540 20305 20242 Longofolene C

15H24

01141 20437 20381 Aromadendrene C

15H24

04242 21530 21477 Curcumene C

15H22

07243 21875 21813 120573-Selinene C

15H24

09244 22041 21872 120572-Selinene C

15H24

04745 23057 22971 120575-Guaiene C

15H24

06146 23285 mdash 120572-Himachalene C

15H24

01347 24561 24510 120572-Bisabolene C

15H24

04248 25527 mdash Acoradiene C

15H24

02349 25673 25615 120591-Cadinene C

15H24

04350 25858 25792 Cuparene C

15H24

037


Table 1 Continued


a X2

b

51 26006 25921 Myristicin C11H12O3

04552 26821 mdash 120572-Guaiene C

15H24

00953 27210 27115 trans-Nerolidol C

15H26O 022

54 27708 27647 e-Cadinene C15H24

09255 28419 mdash Caryophyllene oxide C

15H24O 058

56 29275 17292 120575-Cadinene C15H24

15357 31510 31432 Cedrol C

15H26O 1437

58 31576 31425 epi-Cedrol C15H26O 115

59 32470 mdash Muurolol C15H26O 046

60 33132 33040 120572-Cadinol C15H26O 143

61 33674 33592 Patchoulol C15H26O 217

62 34342 mdash Epiglobulol C15H26O 008

63 35027 34631 Cubenol C15H26O 072

64 37167 37102 Cedryl acetate C17H28O2

07665 39492 39422 Torreyol C

15H26O 038

66 39861 39784 Farnesyl acetate C17H28O2

17367 41216 mdash 120572-Eudesmol C

15H26O 006

68 44875 44793 61014-Trimethyl-2-pentadecanone C18H36O 124

aRepresenting the main root of Agrimonia eupatoriabRepresenting the leaf of Agrimonia eupatoriamdash correlative component is not found in X

2

component and normalization method After all the purechromatographic profile and mass spectrum of each compo-nent in main root of Agrimonia eupatoria were resolved thetotal two-way response of each component can be obtainedfrom the outer product of the concentration vector and thespectrum vector for each component namely C

119894S119879119894 Similar

to the general chromatographic quantitative method withpeak area or height the concentration of each component isproportional to the overall volume of its two-way response(C119894S119879119894) The identified components amounted quantitatively

to 8703 of the total content The final relative quantitativeresults were also listed in Table 1

44 Analysis of Correlative Components Traditional Chinesemedicines (TCM) usually are very complex system Differ-ences maybe exist in the same Chinese herb from differentareas or different growing seasons different plant parts ofthe same herbThus it is very important to develop a reliableapproach to analyze themThe volatile components in leaf ofAgrimonia eupatoria have also been investigated under thesame experimental conditions Curve 1 and curve 2 in Fig-ure 7 were the TIC chromatograms of responseX

1frommain

root and X2from leaf obtained from GC-MS respectively It

was shown from Figure 7 that X2was consistent in eluting

components with X1 but the concentration distribution of

some individuals was a little different Generally one mayanalyze each component in leaf of Agrimonia eupatoria oneby one with relevant resolution method and similarity searchin MS library mentioned above However it was time-consuming to do this The obtained information of X

1may

help to reduce some arduous and unnecessary work when

4

5

3

2

1

00 10 20 30 40

1

2

50Retention time (min)

Inte

nsity

(times107)

C

C998400

Figure 7 The chromatograms of the volatile components in themain root (curve 1) and leaf (curve 2) of Agrimonia eupatoria

we compare the quality of main root and leaf of Agrimoniaeupatoria

Here orthogonal projection resolution (OPR) [14] wasadopted to identify each correlative component directlyinstead of resolving each sample data one by onewith the purecomponent spectra in X

1resolved by SFA projecting onto

sample X2

The chromatographic segment submatrix X from X1

within 1136ndash1170min named peak cluster C was also usedto show the procedure As showed in Section 41 fivecomponents existed in it and the pure spectrum of eachcomponent in it has been extracted with SFA Because theretention time drift was not severe the submatrix X

1015840

of X2

named peak cluster C1015840 can be selected from 1100 to1220min Then the pure spectrum V

1and V4of components


1100 1120 1140 1160 1180 120 1220

06

04

02

0


Leng

th o

f res

idue

vec

tor

(a)

1100 1120 1140 1160 1180 120 1220

06

04

0

02


Leng

th o

f res

idue

vec

tor

(b)

Figure 8 Spectrum projection graphs of component 1 (a) and 4 (b)

Table 2 Qualitative results of some other volatile chemical constituents in leaf of Agrimonia eupatoria

Series no Retention time (min) Compound name Molecule structure1 4812 Prenal C

5H8O

2 6359 1-Hexanol C6H14O

3 10717 Isomenthone C10H18O

4 11397 Carvone C10H14O

5 12105 Phenmethyl acetate C9H10O2

6 12912 4-Hydroxy-3-methylacetophenon C9H10O2

7 14493 44-Dimethyladamantan-2-ol C12H20O

8 17421 Germacrene D C15H24

9 19172 120573-Damascone C13H18O

10 25310 Cadala-1(10)38-triene C15H22

11 28312 Longipinocarvone C15H28O

12 32413 Costunolide C15H20O2

13 34172 cis-7-Tetradecen-1-ol C14H28O

14 34247 Hexahydrofarnesyl acetone C18H36O

1 (34-dimethylbenzaldehyde) and 4 (2-cyclopropylidene-177-trimethyl-bicylo[221] heptane) were orthogonal projectedto X

1015840

The spectrum projection graph was shown in Figure 8It can be seen from Figure 8 that there was a range inwhich the length of the residue vector was close to zero tocomponent 4 Considering the value of re

119894was quite close

to 0 and due to errors and interference from noise andbackground and so forth in actual systems component 2-cyclopropylidene-177-trimethyl-bicyolo[221] heptane wasdetermined to also exist in the studied leaf ofAgrimonia eupa-toria However to component 1 therewas not a range inwhichthe length of the residue vector was close to zero and one candetermine that component 34-dimethylbenzaldehyde didnot exist in the studied leaf sample Similar to this way othercorrelative components which coexisted in main root andleaf of Agrimonia eupatoria could be obtained The resultswere also listed in Table 1 In total there were 52 componentscommon to two different plant parts of Agrimonia eupatoriaHowever because of the very low signal-to-noise ratio someof the components may have gone undetected

To those constituents not to be common componentsin the studied leaf of Agrimonia eupatoria the performed

procedure formain root ofAgrimonia eupatoriawas also usedto extract pure spectrum of each component as described inSection 41 Accordingly identification of these componentswere performed as in Section 42 Thirty components inessential oil of the studied leaf sample which were not foundin main root of Agrimonia eupatoria sample were identifiedwith SFA The results were listed in Table 2

45 Comparison of Samples As shown in Tables 1 and 268 and 65 volatile constituents in the main root and leaf ofAgrimonia eupatoriawere identified respectively Among theidentified components there were 52 common componentsexisting in the two studied samples but the content of eachcomponent in leaf is lower than that in main root Themain components in both main root and leaf of Agrimoniaeupatoria were cedrol 120572-pinene linalool 120572-terpineol 120572-eudesmol eucalyptol and so on The results indicated thatboth main root and leaf of Agrimonia eupatoria were consis-tent to some extent By the use of similarity assessment softa pattern recognition program recommend by the ChinesePharmacopoeial committee [20] the common pattern oftwo specimens can be constructed The similarity of X

1and


X2to their common chromatogram was 09462 and 09417

respectively Obviously the similarity was relatively highbut some differences also existed between them Howevercomponents not common to both parts were generally low inabundance The difference reflects the discrepancy betweenthe main root and leaf of Agrimonia eupatoria

5 Conclusions

Combined chemometric methods were first used to analyzethe volatile components in main root and leaf of Agrimoniaeupatoria After extractionwithwater distillationmethod thevolatile components in Agrimonia eupatoria were detectedby GC-MS The pure spectrum of each volatile compo-nent in main root of Agrimonia eupatoria was extractedwith SFA Then OPR was used to obtain the correlativecomponents from leaf sample This study shows that theapplication of combined approach is a powerful tool whichdoes aimat comprehensivly revealing the quality andquantityof chemical constituents of Agrimonia eupatoria samplesfrom different plant parts The obtained information can beused for effective evaluation of similarity or differences ofanalytical samples This developed method can also be usedfor quality control of Agrimonia eupatoria samples

Acknowledgments

This research work was financially supported by ProvinceNatural Science Foundation of Zhejiang of PR China (Grantno Y4110075) and Zhejiang Qianjiang Talent Project (Grantno 2010R10054) The research work was also financially sup-ported by Welfare Technology Research Project of Zhejiangof PR China (Grant no 2013C31124) andQuzhou TechnologyProjects (Grant no 2013Y003)

References

[1] F Lu X Y Ba and Y Z He ldquoChemical constituents ofAgrimoniae Herbardquo Chinese Traditional and Herbal Drugs vol43 no 5 pp 851ndash855 2012

[2] J J Kim J Jiang D W Shim et al ldquoAnti-inflammatory andanti-allergic effects ofAgrimonia pilosaLedeb extract onmurinecell lines and OVA-induced airway inflammationrdquo Journal ofEthnopharmacology vol 140 no 2 pp 213ndash221 2012

[3] H A Ali N H A Orooba and W A S Khulood ldquoCytotoxiceffects of Agrimonia eupatoria L against cancer cell lines invitrordquo Journal of the Association of Arab Universities for Basicand Applied Sciences 2013

[4] W L Li H C Zheng J Bukuru and N De Kimpe ldquoNaturalmedicines used in the traditional Chinese medical system fortherapy of diabetesmellitusrdquo Journal of Ethnopharmacology vol92 no 1 pp 1ndash21 2004

[5] K Zhang L Dai and Z R Zheng ldquoIsolation and characteriza-tion of an acidic polysaccharide from Agrimonia Pilosa LedebrdquoChinese Journal of Biochemical Pharmaceutics vol 29 no 3 pp171ndash174 2008

[6] Z J Jin ldquoThe chemical composition and clinical researchprogress of Agrimonia eupatoriardquo West China Journal of Phar-maceutical Sciences vol 21 no 5 pp 468ndash472 2006

[7] Y Pan H Liu Y Zhuang L Ding L Chen and F QiuldquoStudies on isolation and identification of flavonoids in herbsof Agrimonia Pilosardquo Zhongguo Zhongyao Zazhi vol 33 no 24pp 2925ndash2928 2008

[8] X Y Ba Y Z He F Lu and L L Shi ldquoResearch progressof Agrimonia Pilosa Ledebrdquo Journal of Liaoning University ofTraditional Chinese Medicine vol 13 no 5 pp 258ndash260 2011

[9] Y Zhao P-Y Li and J-P Liu ldquoStudies on chemical constituentsof essential oil from Hainyvein Agrimonia Pilosardquo ChinesePharmaceutical Journal vol 36 no 10 pp 672ndash673 2001

[10] Y H Pei X Li and T R Zhu ldquoStudies on the chemicalconstituents from the root-sprouts of Agrimonia Pilosa LedebrdquoActa Pharmaceutica Sinica vol 24 no 6 pp 431ndash437 1989

[11] H L Shen R Manne Q S Xu D Chen and Y Liang ldquoLocalresolution of hyphenated chromatographic datardquoChemometricsand Intelligent Laboratory Systems vol 45 no 1-2 pp 323ndash3281999

[12] C J Xu J H Jiang and Y Z Liang ldquoEvolving windoworthogonal projections method for two-way data resolutionrdquoAnalyst vol 124 no 10 pp 1471ndash1476 1999

[13] R Manne H L Shen and Y Z Liang ldquoSubwindow factoranalysisrdquo Chemometrics and Intelligent Laboratory Systems vol45 no 1-2 pp 171ndash176 1999

[14] F C Sanchez S C Rutan M D G Garcıa and D LMassart ldquoResolution of multicomponent overlapped peaks bythe orthogonal projection approach evolving factor analysisand window factor analysisrdquo Chemometrics and IntelligentLaboratory Systems vol 36 no 2 pp 153ndash164 1997

[15] B Y Li Y Hu Y Z Liang L F Huang C Xu and P XieldquoSpectral correlative chromatography and its application toanalysis of chromatographic fingerprints of herbal medicinesrdquoJournal of Separation Science vol 27 no 7-8 pp 581ndash588 2004

[16] Y Z Liang White Grey and Black Multicomponent Systemsand Their Chemometric Algorithms Hunan Publishing Houseof Science and Technology Changsha China 1996

[17] F Q Guo Y Z Liang C J Xu and L F Huang ldquoDeterminationof the volatile chemical constituents of Notoptergium inciumby gas chromatography-mass spectrometry and iterative ornon-iterative chemometrics resolution methodsrdquo Journal ofChromatography A vol 1016 no 1 pp 99ndash110 2003

[18] Y M Wang L Z Yi Y Z Liang et al ldquoComparative analysisof essential oil components in Pericarpium Citri ReticulataeViride and Pericarpium Citri Reticulatae by GC-MS combinedwith chemometric resolution methodrdquo Journal of Pharmaceuti-cal and Biomedical Analysis vol 46 no 1 pp 66ndash74 2008

[19] Chinese Pharmacopoeia Committee Chinese PharmacopoeiaAppendix 62 Publishing House of Peoplersquos Health BeijingChina 2000

[20] P Xie S Chen Y Liang X Wang R Tian and R UptonldquoChromatographic fingerprint analysis-a rational approach forquality assessment of traditional Chinese herbal medicinerdquoJournal of ChromatographyA vol 1112 no 1-2 pp 171ndash180 2006

Submit your manuscripts athttpwwwhindawicom

Hindawi Publishing Corporationhttpwwwhindawicom Volume 2014

Inorganic ChemistryInternational Journal of

Hindawi Publishing Corporation httpwwwhindawicom Volume 2014

International Journal ofPhotoenergy


Carbohydrate Chemistry

International Journal of


Journal of

Chemistry


Advances in

Physical Chemistry

Hindawi Publishing Corporationhttpwwwhindawicom

Analytical Methods in Chemistry

Journal of

Volume 2014

Bioinorganic Chemistry and ApplicationsHindawi Publishing Corporationhttpwwwhindawicom Volume 2014

SpectroscopyInternational Journal of


The Scientific World JournalHindawi Publishing Corporation httpwwwhindawicom Volume 2014

Medicinal ChemistryInternational Journal of


Chromatography Research International


Applied ChemistryJournal of



Theoretical ChemistryJournal of


Journal of

Spectroscopy

Analytical ChemistryInternational Journal of


Journal of


Quantum Chemistry


Organic Chemistry International

ElectrochemistryInternational Journal of



CatalystsJournal of


of the volatile ingredients in Agrimonia eupatoria is veryimportant

As for the analysis of the volatile compounds in Agri-monia eupatoria only a few reports have been seen in theliterature [9 10] They are usually performed with gas chro-matography (GC) and gas chromatography-mass spectrom-etry (GC-MS) which are based on gas chromatographicretention indices or MS for qualitative and quantitative anal-ysis However because the composition of Agrimonia eupa-toria is very complicated and the contents of many impor-tant volatile components in Agrimonia eupatoria are verylow suitable sample-preparing methods are necessary beforedetection by GC-MS such as steam distillation [9] Althoughpreparing methods are used for the analysis process of thecomplicated Agrimonia eupatoria samples it is still impossi-ble to obtain complete separation of all the volatile chemicalconstituents of Agrimonia eupatoria In these general GCor GC-MS reports it is difficult to assess the purity ofchromatographic peaks and the peak inspected as one com-ponent may be a mixture of several components The resultsobtained by thesemethodswhich have beenmentioned abovewould be questionable Fortunately with the developmentof hyphenated instruments multidimensional data revealingthe compositions of samples can be obtained from GC-MSHPLC-DAD and so onThen many associated chemometricmethods [11ndash18] which can be used to resolve multidimen-sional data have been developedThus more information forchemical analysis both in chromatographic separation andin spectral identification can be obtained which makes itpossible to interpret these complex systems

On the other hand there may be some sameness and dif-ferences to exist in Agrimonia eupatoria from different plantparts To find the pharmacological active components thatexist in essential oils exactly it is important that the methodfor the detailed study of the components in Agrimonia eupa-toria from different plant parts such as the root and leaf wasestablished

In this paper two chemometrics methods subwindowfactor analysis [13] and orthogonal projection resolution(OPR) [14] were used to analyze the volatile constituents ofAgrimonia eupatoria from different plant parts for the firsttime The volatile components of Agrimonia eupatoria fromtwo different plant parts were extractedwithwater distillationand subjected to GC-MS analysis Firstly the qualitative andquantitative analysis of volatile components in the main rootof Agrimonia eupatoria was completed with the help of sub-window factor analysis Secondly the common peaks inleaf of Agrimonia eupatoria were extracted with orthogonalprojectionmethodAt last to those constituents in leaf whichwere not identified withOPRmethod the qualitative analysiswas also performed with subwindow factor analysis Thena simple and reliable combined approach for the systematicstudy of the volatile constituents in the main root and leafAgrimonia eupatoria was developed Not only more infor-mation was obtained but also the reliability of componentswas improved The obtained results can provide foundationfor further development of fingerprint and quality control ofAgrimonia eupatoria

2 Theory and Method

21 Subwindow Factor Analysis (SFA) The detailed processof SFA has been described in the literature [13] here only abrief depiction of the method is given

According to the Lambert-Beer Law a two-dimensionaldata X

119898times119899produced by hyphenated instruments can be

expressed as the product of two matrices as follows

X119898times119899= C119898times119901

S119879119899times119901+ E (1)

where X119898times119899

denotes response matrix representing 119901 com-ponents of 119898 spectra measured at regular time intervals andat 119899 different wavelengths or mass-to-charge ratios Matrix Cis the pure composed of 119901 columns each one describing thechromatographic concentration profile of a pure chemicalspecies Similarly the matrix S119879 consists of 119901 rows corre-sponding to pure spectra of the chemical species Matrix Edenotes measurement noiseThe superscript119879 represents thetranspose of matrix

It is crucial to identify left and right subwindows of SFAIn the former an interfering compound starts to elute beforethe analyte appears in a chromatogram to the left of the ana-lyte and in the latter another interference continues to eluteafter the analyte has stopped eluting The rank analysis canprovide the number of chemical components of the left andright ones say 119898

1and 119898

2 respectively And the number of

components in the combination of left and right ones is1198981+ 1198982minus 1 since the analyte is common to both One may

then find an orthogonal basis g1 g2 g1198981 spanning the

spectral subspace of the left subwindow and a similar basisf1 f2 f1198982 spanning that of the right subwindow cor-

responding to matrices G and F by means of singular-value decomposition The common spectral vector v to bothsubspaces can be written as linear combinations of both setsof basis for an ideal case

v = Ga

v = Fb(2)

Under the conditions a119879a = b119879b = 1 In reality Ga and Fbare not identical on account of interference from noise andbackground and so forth And we search for vectors a and bwhich minimize the squared norm

119873 = Ga minus Fb2 = a119879G119879Ga + b119879F119879Fb minus 2a119879G119879Fb (3)

SinceG119879G = I1198981andF119879F = I119898

2(unitmatrices of dimension

1198981times 1198981 and119898

2times 1198982 resp) we obtain

119873 = 2 minus 2a119879G119879Fb (4)

Here if a and b are the left and right singular vectorsrespectively associated with the first largest singular value 119889

1

of the matrixG119879F inserting this result in (3) we again obtain

119873 = 2 (1 minus 1198891) (5)

The singular values 119889119894of the matrix G119879F are in the range

0 le 119889119894le 1 and the larger the value of 119889

1is the closer




(6)









P119894= 119868 minus X119879

119894(X119879119894)

+

(7)


119894represents



119894is given by

r119894= P119894k119886 (8)



119886on the


re119894=

1003817100381710038171003817

r119894

1003817100381710038171003817

2

(119894 = 1 2 119898 minus 119908 + 1) (9)





119894contains


119894 hence the


119894 then re

119894will


3 Experimental


4

3

2

1

010 20 30 40 50


Inte

nsity

(times107)

C







12

10

8

6

4

2

0114 115 116


Inte

nsity

(times105)

(a)

6

5

4

3

2

Log

(eig

enva

lue)


(b)


100

80

60

40

20

00 50 100 150

Inte

nsity

Standard

105

133

134

mz

(a)

mz

10080604020

00 50 100 150

Inte

nsity

Component 1

105

133134

(b)












10080604020

00 20 40 60 80 100 120 140 160 180 200

Inte

nsity

Standard

43

41

95

93105

111

133

134

71

67

mz

(a)

10080604020

00 20 40 60 80 100 120 140 160 180 200

Inte

nsity

Component 2

43

41

95

93

105111

133

134

71

mz

(b)


100

50

00 50 100 200150

Inte

nsity

Standard

39

4191

105

119 161

133

937969

mz

(a)

100

50

00 50 100 200150

Inte

nsity

Component 4

3941

55

91105

119106

161

133

93

79

81

69

mz

(b)




0

2

4

6

8

10


Inte

nsity

(times105)

1

2

3

4

5








a X2

b

1 4639 4612 120572-Pinene C10H16


6H12O 005

3 5172 5123 120573-Pinene C10H16

1274 5368 5322 Camphene C

10H16

3215 5714 5692 3-Octanol C

10H18O 027

6 6032 5971 Cymene C10H14

0187 6354 6289 D-Limonene C

10H16


10H18O 326

9 7290 7195 120572-trans-Ocimene C10H16

05110 7572 7547 Linalool C

10H18O 572

11 8157 8093 120572-Campholenal C10H16O 072

12 8682 8631 L-Camphor C10H16O 211


14 10241 10196 4-Terpineol C10H18O 147

15 10473 10432 120572-Terpineol C10H18O 421


17 10941 10906 Pulegone C10H16O 017




05221 11712 11621 1-(2-Furyl)-1-hexanone C

10H14O2

48722 11801 11762 Bergamot oil C

12H20O2


9H18O2


9H20O2

01025 12871 12821 Thymol C

10H14O 082


27 13914 13865 Anethole C10H12O 007


37229 14794 14738 Neryl acetate C

12H20O2


12H20O2


12H20O 004



11H14O2

05234 16812 16762 120572-Cedrene C

15H24

28735 17059 17012 120572-Longipinene C

15H24


15H24

08137 17475 mdash 120573-Cedrene C

15H24


13H22O 084


00540 20305 20242 Longofolene C

15H24


15H24

04242 21530 21477 Curcumene C

15H22

07243 21875 21813 120573-Selinene C

15H24

09244 22041 21872 120572-Selinene C

15H24

04745 23057 22971 120575-Guaiene C

15H24


15H24

01347 24561 24510 120572-Bisabolene C

15H24


15H24

02349 25673 25615 120591-Cadinene C

15H24

04350 25858 25792 Cuparene C

15H24

037


Table 1 Continued


a X2

b

51 26006 25921 Myristicin C11H12O3

04552 26821 mdash 120572-Guaiene C

15H24


15H26O 022

54 27708 27647 e-Cadinene C15H24


15H24O 058

56 29275 17292 120575-Cadinene C15H24

15357 31510 31432 Cedrol C

15H26O 1437

58 31576 31425 epi-Cedrol C15H26O 115


60 33132 33040 120572-Cadinol C15H26O 143

61 33674 33592 Patchoulol C15H26O 217


63 35027 34631 Cubenol C15H26O 072


07665 39492 39422 Torreyol C

15H26O 038



15H26O 006



2


119894S119879119894 Similar




1frommain





1may


4

5

3

2

1

00 10 20 30 40

1

2


Inte

nsity

(times107)

C

C998400





sample X2



1015840

of X2




1100 1120 1140 1160 1180 120 1220

06

04

02

0


Leng

th o

f res

idue

vec

tor

(a)

1100 1120 1140 1160 1180 120 1220

06

04

0

02


Leng

th o

f res

idue

vec

tor

(b)




5H8O








9 19172 120573-Damascone C13H18O

10 25310 Cadala-1(10)38-triene C15H22






1015840







1and




5 Conclusions


Acknowledgments


References






























Journal of

Chemistry


Advances in

Physical Chemistry



Journal of

Volume 2014














Journal of

Spectroscopy



Journal of


Quantum Chemistry






CatalystsJournal of




(6)









P119894= 119868 minus X119879

119894(X119879119894)

+

(7)


119894represents



119894is given by

r119894= P119894k119886 (8)



119886on the


re119894=

1003817100381710038171003817

r119894

1003817100381710038171003817

2

(119894 = 1 2 119898 minus 119908 + 1) (9)





119894contains


119894 hence the


119894 then re

119894will


3 Experimental


4

3

2

1

010 20 30 40 50


Inte

nsity

(times107)

C







12

10

8

6

4

2

0114 115 116


Inte

nsity

(times105)

(a)

6

5

4

3

2

Log

(eig

enva

lue)


(b)


100

80

60

40

20

00 50 100 150

Inte

nsity

Standard

105

133

134

mz

(a)

mz

10080604020

00 50 100 150

Inte

nsity

Component 1

105

133134

(b)












10080604020

00 20 40 60 80 100 120 140 160 180 200

Inte

nsity

Standard

43

41

95

93105

111

133

134

71

67

mz

(a)

10080604020

00 20 40 60 80 100 120 140 160 180 200

Inte

nsity

Component 2

43

41

95

93

105111

133

134

71

mz

(b)


100

50

00 50 100 200150

Inte

nsity

Standard

39

4191

105

119 161

133

937969

mz

(a)

100

50

00 50 100 200150

Inte

nsity

Component 4

3941

55

91105

119106

161

133

93

79

81

69

mz

(b)




0

2

4

6

8

10


Inte

nsity

(times105)

1

2

3

4

5








a X2

b

1 4639 4612 120572-Pinene C10H16


6H12O 005

3 5172 5123 120573-Pinene C10H16

1274 5368 5322 Camphene C

10H16

3215 5714 5692 3-Octanol C

10H18O 027

6 6032 5971 Cymene C10H14

0187 6354 6289 D-Limonene C

10H16


10H18O 326

9 7290 7195 120572-trans-Ocimene C10H16

05110 7572 7547 Linalool C

10H18O 572

11 8157 8093 120572-Campholenal C10H16O 072

12 8682 8631 L-Camphor C10H16O 211


14 10241 10196 4-Terpineol C10H18O 147

15 10473 10432 120572-Terpineol C10H18O 421


17 10941 10906 Pulegone C10H16O 017




05221 11712 11621 1-(2-Furyl)-1-hexanone C

10H14O2

48722 11801 11762 Bergamot oil C

12H20O2


9H18O2


9H20O2

01025 12871 12821 Thymol C

10H14O 082


27 13914 13865 Anethole C10H12O 007


37229 14794 14738 Neryl acetate C

12H20O2


12H20O2


12H20O 004



11H14O2

05234 16812 16762 120572-Cedrene C

15H24

28735 17059 17012 120572-Longipinene C

15H24


15H24

08137 17475 mdash 120573-Cedrene C

15H24


13H22O 084


00540 20305 20242 Longofolene C

15H24


15H24

04242 21530 21477 Curcumene C

15H22

07243 21875 21813 120573-Selinene C

15H24

09244 22041 21872 120572-Selinene C

15H24

04745 23057 22971 120575-Guaiene C

15H24


15H24

01347 24561 24510 120572-Bisabolene C

15H24


15H24

02349 25673 25615 120591-Cadinene C

15H24

04350 25858 25792 Cuparene C

15H24

037


Table 1 Continued


a X2

b

51 26006 25921 Myristicin C11H12O3

04552 26821 mdash 120572-Guaiene C

15H24


15H26O 022

54 27708 27647 e-Cadinene C15H24


15H24O 058

56 29275 17292 120575-Cadinene C15H24

15357 31510 31432 Cedrol C

15H26O 1437

58 31576 31425 epi-Cedrol C15H26O 115


60 33132 33040 120572-Cadinol C15H26O 143

61 33674 33592 Patchoulol C15H26O 217


63 35027 34631 Cubenol C15H26O 072


07665 39492 39422 Torreyol C

15H26O 038



15H26O 006



2


119894S119879119894 Similar




1frommain





1may


4

5

3

2

1

00 10 20 30 40

1

2


Inte

nsity

(times107)

C

C998400





sample X2



1015840

of X2




1100 1120 1140 1160 1180 120 1220

06

04

02

0


Leng

th o

f res

idue

vec

tor

(a)

1100 1120 1140 1160 1180 120 1220

06

04

0

02


Leng

th o

f res

idue

vec

tor

(b)




5H8O








9 19172 120573-Damascone C13H18O

10 25310 Cadala-1(10)38-triene C15H22






1015840







1and




5 Conclusions


Acknowledgments


References






























Journal of

Chemistry


Advances in

Physical Chemistry



Journal of

Volume 2014














Journal of

Spectroscopy



Journal of


Quantum Chemistry






CatalystsJournal of


12

10

8

6

4

2

0114 115 116


Inte

nsity

(times105)

(a)

6

5

4

3

2

Log

(eig

enva

lue)


(b)


100

80

60

40

20

00 50 100 150

Inte

nsity

Standard

105

133

134

mz

(a)

mz

10080604020

00 50 100 150

Inte

nsity

Component 1

105

133134

(b)












10080604020

00 20 40 60 80 100 120 140 160 180 200

Inte

nsity

Standard

43

41

95

93105

111

133

134

71

67

mz

(a)

10080604020

00 20 40 60 80 100 120 140 160 180 200

Inte

nsity

Component 2

43

41

95

93

105111

133

134

71

mz

(b)


100

50

00 50 100 200150

Inte

nsity

Standard

39

4191

105

119 161

133

937969

mz

(a)

100

50

00 50 100 200150

Inte

nsity

Component 4

3941

55

91105

119106

161

133

93

79

81

69

mz

(b)




0

2

4

6

8

10


Inte

nsity

(times105)

1

2

3

4

5








a X2

b

1 4639 4612 120572-Pinene C10H16


6H12O 005

3 5172 5123 120573-Pinene C10H16

1274 5368 5322 Camphene C

10H16

3215 5714 5692 3-Octanol C

10H18O 027

6 6032 5971 Cymene C10H14

0187 6354 6289 D-Limonene C

10H16


10H18O 326

9 7290 7195 120572-trans-Ocimene C10H16

05110 7572 7547 Linalool C

10H18O 572

11 8157 8093 120572-Campholenal C10H16O 072

12 8682 8631 L-Camphor C10H16O 211


14 10241 10196 4-Terpineol C10H18O 147

15 10473 10432 120572-Terpineol C10H18O 421


17 10941 10906 Pulegone C10H16O 017




05221 11712 11621 1-(2-Furyl)-1-hexanone C

10H14O2

48722 11801 11762 Bergamot oil C

12H20O2


9H18O2


9H20O2

01025 12871 12821 Thymol C

10H14O 082


27 13914 13865 Anethole C10H12O 007


37229 14794 14738 Neryl acetate C

12H20O2


12H20O2


12H20O 004



11H14O2

05234 16812 16762 120572-Cedrene C

15H24

28735 17059 17012 120572-Longipinene C

15H24


15H24

08137 17475 mdash 120573-Cedrene C

15H24


13H22O 084


00540 20305 20242 Longofolene C

15H24


15H24

04242 21530 21477 Curcumene C

15H22

07243 21875 21813 120573-Selinene C

15H24

09244 22041 21872 120572-Selinene C

15H24

04745 23057 22971 120575-Guaiene C

15H24


15H24

01347 24561 24510 120572-Bisabolene C

15H24


15H24

02349 25673 25615 120591-Cadinene C

15H24

04350 25858 25792 Cuparene C

15H24

037


Table 1 Continued


a X2

b

51 26006 25921 Myristicin C11H12O3

04552 26821 mdash 120572-Guaiene C

15H24


15H26O 022

54 27708 27647 e-Cadinene C15H24


15H24O 058

56 29275 17292 120575-Cadinene C15H24

15357 31510 31432 Cedrol C

15H26O 1437

58 31576 31425 epi-Cedrol C15H26O 115


60 33132 33040 120572-Cadinol C15H26O 143

61 33674 33592 Patchoulol C15H26O 217


63 35027 34631 Cubenol C15H26O 072


07665 39492 39422 Torreyol C

15H26O 038



15H26O 006



2


119894S119879119894 Similar




1frommain





1may


4

5

3

2

1

00 10 20 30 40

1

2


Inte

nsity

(times107)

C

C998400





sample X2



1015840

of X2




1100 1120 1140 1160 1180 120 1220

06

04

02

0


Leng

th o

f res

idue

vec

tor

(a)

1100 1120 1140 1160 1180 120 1220

06

04

0

02


Leng

th o

f res

idue

vec

tor

(b)




5H8O








9 19172 120573-Damascone C13H18O

10 25310 Cadala-1(10)38-triene C15H22






1015840







1and




5 Conclusions


Acknowledgments


References






























Journal of

Chemistry


Advances in

Physical Chemistry



Journal of

Volume 2014














Journal of

Spectroscopy



Journal of


Quantum Chemistry






CatalystsJournal of


10080604020

00 20 40 60 80 100 120 140 160 180 200

Inte

nsity

Standard

43

41

95

93105

111

133

134

71

67

mz

(a)

10080604020

00 20 40 60 80 100 120 140 160 180 200

Inte

nsity

Component 2

43

41

95

93

105111

133

134

71

mz

(b)


100

50

00 50 100 200150

Inte

nsity

Standard

39

4191

105

119 161

133

937969

mz

(a)

100

50

00 50 100 200150

Inte

nsity

Component 4

3941

55

91105

119106

161

133

93

79

81

69

mz

(b)




0

2

4

6

8

10


Inte

nsity

(times105)

1

2

3

4

5








a X2

b

1 4639 4612 120572-Pinene C10H16


6H12O 005

3 5172 5123 120573-Pinene C10H16

1274 5368 5322 Camphene C

10H16

3215 5714 5692 3-Octanol C

10H18O 027

6 6032 5971 Cymene C10H14

0187 6354 6289 D-Limonene C

10H16


10H18O 326

9 7290 7195 120572-trans-Ocimene C10H16

05110 7572 7547 Linalool C

10H18O 572

11 8157 8093 120572-Campholenal C10H16O 072

12 8682 8631 L-Camphor C10H16O 211


14 10241 10196 4-Terpineol C10H18O 147

15 10473 10432 120572-Terpineol C10H18O 421


17 10941 10906 Pulegone C10H16O 017




05221 11712 11621 1-(2-Furyl)-1-hexanone C

10H14O2

48722 11801 11762 Bergamot oil C

12H20O2


9H18O2


9H20O2

01025 12871 12821 Thymol C

10H14O 082


27 13914 13865 Anethole C10H12O 007


37229 14794 14738 Neryl acetate C

12H20O2


12H20O2


12H20O 004



11H14O2

05234 16812 16762 120572-Cedrene C

15H24

28735 17059 17012 120572-Longipinene C

15H24


15H24

08137 17475 mdash 120573-Cedrene C

15H24


13H22O 084


00540 20305 20242 Longofolene C

15H24


15H24

04242 21530 21477 Curcumene C

15H22

07243 21875 21813 120573-Selinene C

15H24

09244 22041 21872 120572-Selinene C

15H24

04745 23057 22971 120575-Guaiene C

15H24


15H24

01347 24561 24510 120572-Bisabolene C

15H24


15H24

02349 25673 25615 120591-Cadinene C

15H24

04350 25858 25792 Cuparene C

15H24

037


Table 1 Continued


a X2

b

51 26006 25921 Myristicin C11H12O3

04552 26821 mdash 120572-Guaiene C

15H24


15H26O 022

54 27708 27647 e-Cadinene C15H24


15H24O 058

56 29275 17292 120575-Cadinene C15H24

15357 31510 31432 Cedrol C

15H26O 1437

58 31576 31425 epi-Cedrol C15H26O 115


60 33132 33040 120572-Cadinol C15H26O 143

61 33674 33592 Patchoulol C15H26O 217


63 35027 34631 Cubenol C15H26O 072


07665 39492 39422 Torreyol C

15H26O 038



15H26O 006



2


119894S119879119894 Similar




1frommain





1may


4

5

3

2

1

00 10 20 30 40

1

2


Inte

nsity

(times107)

C

C998400





sample X2



1015840

of X2




1100 1120 1140 1160 1180 120 1220

06

04

02

0


Leng

th o

f res

idue

vec

tor

(a)

1100 1120 1140 1160 1180 120 1220

06

04

0

02


Leng

th o

f res

idue

vec

tor

(b)




5H8O








9 19172 120573-Damascone C13H18O

10 25310 Cadala-1(10)38-triene C15H22






1015840







1and




5 Conclusions


Acknowledgments


References






























Journal of

Chemistry


Advances in

Physical Chemistry



Journal of

Volume 2014














Journal of

Spectroscopy



Journal of


Quantum Chemistry






CatalystsJournal of




a X2

b

1 4639 4612 120572-Pinene C10H16


6H12O 005

3 5172 5123 120573-Pinene C10H16

1274 5368 5322 Camphene C

10H16

3215 5714 5692 3-Octanol C

10H18O 027

6 6032 5971 Cymene C10H14

0187 6354 6289 D-Limonene C

10H16


10H18O 326

9 7290 7195 120572-trans-Ocimene C10H16

05110 7572 7547 Linalool C

10H18O 572

11 8157 8093 120572-Campholenal C10H16O 072

12 8682 8631 L-Camphor C10H16O 211


14 10241 10196 4-Terpineol C10H18O 147

15 10473 10432 120572-Terpineol C10H18O 421


17 10941 10906 Pulegone C10H16O 017




05221 11712 11621 1-(2-Furyl)-1-hexanone C

10H14O2

48722 11801 11762 Bergamot oil C

12H20O2


9H18O2


9H20O2

01025 12871 12821 Thymol C

10H14O 082


27 13914 13865 Anethole C10H12O 007


37229 14794 14738 Neryl acetate C

12H20O2


12H20O2


12H20O 004



11H14O2

05234 16812 16762 120572-Cedrene C

15H24

28735 17059 17012 120572-Longipinene C

15H24


15H24

08137 17475 mdash 120573-Cedrene C

15H24


13H22O 084


00540 20305 20242 Longofolene C

15H24


15H24

04242 21530 21477 Curcumene C

15H22

07243 21875 21813 120573-Selinene C

15H24

09244 22041 21872 120572-Selinene C

15H24

04745 23057 22971 120575-Guaiene C

15H24


15H24

01347 24561 24510 120572-Bisabolene C

15H24


15H24

02349 25673 25615 120591-Cadinene C

15H24

04350 25858 25792 Cuparene C

15H24

037


Table 1 Continued


a X2

b

51 26006 25921 Myristicin C11H12O3

04552 26821 mdash 120572-Guaiene C

15H24


15H26O 022

54 27708 27647 e-Cadinene C15H24


15H24O 058

56 29275 17292 120575-Cadinene C15H24

15357 31510 31432 Cedrol C

15H26O 1437

58 31576 31425 epi-Cedrol C15H26O 115


60 33132 33040 120572-Cadinol C15H26O 143

61 33674 33592 Patchoulol C15H26O 217


63 35027 34631 Cubenol C15H26O 072


07665 39492 39422 Torreyol C

15H26O 038



15H26O 006



2


119894S119879119894 Similar




1frommain





1may


4

5

3

2

1

00 10 20 30 40

1

2


Inte

nsity

(times107)

C

C998400





sample X2



1015840

of X2




1100 1120 1140 1160 1180 120 1220

06

04

02

0


Leng

th o

f res

idue

vec

tor

(a)

1100 1120 1140 1160 1180 120 1220

06

04

0

02


Leng

th o

f res

idue

vec

tor

(b)




5H8O








9 19172 120573-Damascone C13H18O

10 25310 Cadala-1(10)38-triene C15H22






1015840







1and




5 Conclusions


Acknowledgments


References






























Journal of

Chemistry


Advances in

Physical Chemistry



Journal of

Volume 2014














Journal of

Spectroscopy



Journal of


Quantum Chemistry






CatalystsJournal of


Table 1 Continued


a X2

b

51 26006 25921 Myristicin C11H12O3

04552 26821 mdash 120572-Guaiene C

15H24


15H26O 022

54 27708 27647 e-Cadinene C15H24


15H24O 058

56 29275 17292 120575-Cadinene C15H24

15357 31510 31432 Cedrol C

15H26O 1437

58 31576 31425 epi-Cedrol C15H26O 115


60 33132 33040 120572-Cadinol C15H26O 143

61 33674 33592 Patchoulol C15H26O 217


63 35027 34631 Cubenol C15H26O 072


07665 39492 39422 Torreyol C

15H26O 038



15H26O 006



2


119894S119879119894 Similar




1frommain





1may


4

5

3

2

1

00 10 20 30 40

1

2


Inte

nsity

(times107)

C

C998400





sample X2



1015840

of X2




1100 1120 1140 1160 1180 120 1220

06

04

02

0


Leng

th o

f res

idue

vec

tor

(a)

1100 1120 1140 1160 1180 120 1220

06

04

0

02


Leng

th o

f res

idue

vec

tor

(b)




5H8O








9 19172 120573-Damascone C13H18O

10 25310 Cadala-1(10)38-triene C15H22






1015840







1and




5 Conclusions


Acknowledgments


References






























Journal of

Chemistry


Advances in

Physical Chemistry



Journal of

Volume 2014














Journal of

Spectroscopy



Journal of


Quantum Chemistry






CatalystsJournal of


1100 1120 1140 1160 1180 120 1220

06

04

02

0


Leng

th o

f res

idue

vec

tor

(a)

1100 1120 1140 1160 1180 120 1220

06

04

0

02


Leng

th o

f res

idue

vec

tor

(b)




5H8O








9 19172 120573-Damascone C13H18O

10 25310 Cadala-1(10)38-triene C15H22






1015840







1and




5 Conclusions


Acknowledgments


References






























Journal of

Chemistry


Advances in

Physical Chemistry



Journal of

Volume 2014














Journal of

Spectroscopy



Journal of


Quantum Chemistry






CatalystsJournal of




5 Conclusions


Acknowledgments


References






























Journal of

Chemistry


Advances in

Physical Chemistry



Journal of

Volume 2014














Journal of

Spectroscopy



Journal of


Quantum Chemistry






CatalystsJournal of










Journal of

Chemistry


Advances in

Physical Chemistry



Journal of

Volume 2014














Journal of

Spectroscopy



Journal of


Quantum Chemistry






CatalystsJournal of

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