Spectrochimica Acta Part A: Molecular and Biomolecular Spectroscopy 137 (2015) 1244–1249

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Spectrochimica Acta Part A: Molecular andBiomolecular Spectroscopy

journal homepage: www.elsevier .com/locate /saa

Fourier transform infrared spectroscopy combined with chemometricsfor discrimination of Curcuma longa, Curcuma xanthorrhiza and Zingibercassumunar

http://dx.doi.org/10.1016/j.saa.2014.08.1391386-1425/� 2014 Elsevier B.V. All rights reserved.

⇑ Corresponding author at: Department of Chemistry, Faculty of Mathematics and Natural Sciences, Bogor Agricultural University, Jalan Agatis Kampus IPB Drama16680, Indonesia. Tel./fax: +62 251 8624567.

E-mail address: mra@ipb.ac.id (M. Rafi).

Eti Rohaeti a, Mohamad Rafi a,b,⇑, Utami Dyah Syafitri c, Rudi Heryanto a,b

a Department of Chemistry, Faculty of Mathematics and Natural Sciences, Bogor Agricultural University, Jalan Agatis Kampus IPB Dramaga, Bogor 16680, Indonesiab Biopharmaca Research Center – Research and Community Empowerment Institute, Bogor Agricultural University, Jalan Taman Kencana No. 3 Kampus IPB Taman Kencana,Bogor 16128, Indonesiac Department of Statistics, Faculty of Mathematics and Natural Sciences, Bogor Agricultural University, Jalan Meranti Kampus IPB Dramaga, Bogor 16680, Indonesia

h i g h l i g h t s

� Discrimination of C. longa, C.xanthorrhiza and Z. cassumunar byFTIR.� Principal component analysis and

canonical variate analysis are used fordiscrimination.� Canonical variate analysis gave

clearer discrimination between thethree species.

g r a p h i c a l a b s t r a c t

a r t i c l e i n f o

Article history:Received 22 March 2014Received in revised form 14 July 2014Accepted 31 August 2014

Keywords:C. longaC. xanthorrhizaZ. cassumunarFTIRChemometricsDiscrimination

a b s t r a c t

Turmeric (Curcuma longa), java turmeric (Curcuma xanthorrhiza) and cassumunar ginger (Zingibercassumunar) are widely used in traditional Indonesian medicines (jamu). They have similar color for theirrhizome and possess some similar uses, so it is possible to substitute one for the other. The identificationand discrimination of these closely-related plants is a crucial task to ensure the quality of the raw mate-rials. Therefore, an analytical method which is rapid, simple and accurate for discriminating these speciesusing Fourier transform infrared spectroscopy (FTIR) combined with some chemometrics methods wasdeveloped. FTIR spectra were acquired in the mid-IR region (4000–400 cm�1). Standard normal variate,first and second order derivative spectra were compared for the spectral data. Principal componentanalysis (PCA) and canonical variate analysis (CVA) were used for the classification of the three species.Samples could be discriminated by visual analysis of the FTIR spectra by using their marker bands.Discrimination of the three species was also possible through the combination of the pre-processed FTIRspectra with PCA and CVA, in which CVA gave clearer discrimination. Subsequently, the developedmethod could be used for the identification and discrimination of the three closely-related plant species.

� 2014 Elsevier B.V. All rights reserved.

ga, Bogor

E. Rohaeti et al. / Spectrochimica Acta Part A: Molecular and Biomolecular Spectroscopy 137 (2015) 1244–1249 1245

Introduction

Turmeric (Curcuma longa), java turmeric (Curcuma xanthorrhiza)and cassumunar ginger (Zingiber cassumunar) cultivated mostly inthe Java Island are widely used in traditional Indonesian medicines(jamu). C. longa and C. xanthorrhiza are usually aromatic and carmi-native, and are used to treat stomachic, hepatitis, jaundice, diabetes,atherosclerosis and bacterial infections [1], while Z. cassumunar isusually used to treat various diseases such as asthma, carminative,colic, diarrhea, stomachic, muscle and joint pain [2,3].

The color of their rhizome is yellow to orange for C. longa andC. xanthorrhiza, and pale yellow to yellow for Z. cassumunar. Thesethree plants are belongs to the Zingiberaceae family and therefore,they may have some similar chemical components. Phenolic com-pounds (diarylheptanoid, diarylpentanoid, phenylpropene derivative)and terpenoids (monoterpenoid, sesquiterpenoid, diterpenoid, andtriterpenoid) have been identified in C. longa and some of them alsopresent in C. xanthorrhiza. Diarylheptanoids/curcuminoids (curcumin,demethoxycurcumin, bisdemethoxycurcumin, etc.) and sesquiterpe-noids (curcumene, turmerone, zingiberene, etc.) were found as amajor group of compounds in C. longa and C. xanthorrhiza [4,5].While in Z. cassumunar, phenylbutanoids ((E)-1(3,4-dimethyl-phenyl) butadiene), diarylheptanoids (curcumin), monoterpenoids(sabinene) and sesquiterpenoids (zingiberene) were identified [2].Curcuminoids are responsible for the color of the three plants usedin this study.

Due to the similarity of the color of their rhizomes which pos-sess some similar uses, substitution for each other could be possi-ble, especially if in the powdered form and if the price ofC. xanthorrhiza is much higher than C. longa or Z. cassumunar. Ifthe substitution occured, it could be a serious problem becauseof inconsistency in their biological properties. In this case, theidentification and discrimination of these closely-related plantsare crucial in order to ensure the quality, safety and efficacy ofthe raw materials before they are converted to final products.Therefore, a rapid, simple and accurate analytical method is essen-tially required for the discrimination of these species.

Several analytical techniques such as spectroscopy (UV–Vis,FTIR, NMR, and MS) and chromatography (TLC, HPLC, and GC) havebeen used in the development of method for identification and dis-crimination of medicinal plants. Among these techniques, Fouriertransform infrared (FTIR) spectroscopy may be an attractive optionbecause it can meet the criteria of efficient analysis, i.e. easy to use,fast, and inexpensive [6]. Although medicinal plants will contain alarge variety of chemical components, their FTIR spectra some-times are found to differ even in the same species [7]. FTIR hasbeen widely used and is a well-established tool for quality controlin various industries, including herbal industry [8].

FTIR spectra contain complex data information describing theoverall chemical signal in a sample. Changes in the position andintensity of bands in the FTIR spectra would be associated withthe changes in the chemical composition of a sample. Therefore,FTIR spectra could be used to discriminate closely-related species,although the composition of a chemical compound is certainly notknown [8]. Obvious advantages of FTIR application to discriminatedifferent medicinal plants are not only effective and specific, butalso rapid and non-separative. Discrimination by visual inspectionin the FTIR spectra is not easy because the FTIR spectra pattern isvery complex, and to address this concern, the aid from chemomet-rics methods is needed [9]. The advantage of using chemometricsfor the interpretation of FTIR is the ability to link the spectral patternwith hidden information contained in a sample [10]. FTIR spectralanalysis alone or in combination with chemometrics methods hasbeen extensively used for species identification and discriminationof some closely-related species [11–22]. Because of the advantages

of FTIR spectroscopy, we used this techniques in combinationwith chemometrics methods for the first time to develop a qualita-tive method for discrimination of C. Longa, C. xanthorrhiza andZ. Cassumunar. This combined method was successfully applied forthe identification and discrimination of the three species.

Material and methods

Chemicals

Potassium bromide (KBr) for spectroscopy was purchased fromSigma–Aldrich (St Louis, USA). Analytical-grade ethanol for solventextraction was obtained from Merck (Darmstadt, Germany).

Sample preparation

Ninety-nine samples consisting of 35 samples of C. longa, 35samples of C. xanthorrhiza and 29 samples of Z. cassumunar werecollected during 2008–2010 from 11 regencies (2–5 samples fromeach regency) in Java Island, Indonesia: Bogor, Sukabumi, Sumed-ang, Purworejo, Wonogiri, Karanganyar, Semarang, Kulonprogo,Pacitan, Ponorogo, and Kediri (Table 1). Voucher specimens weredeposited at Biopharmaca Research Center, Bogor Agricultural Uni-versity, Indonesia. All samples were sieved, dried, and pulverizedprior to use. There are several methods to obtained IR spectra forthe purpose of identification and discrimination as described byZou et al. [10]. In this experiment, the samples were extracted witha solvent and after evaporating the solvent, the extracts wereblended with KBr. This method will provide better resolution inthe identification and discrimination of closely related plants[10]. Ethanol was used to extract the samples and after the extrac-tion process, the ethanol was evaporated by rotary evaporator.These ethanol extracts were then used for FTIR measurement.

FTIR spectroscopy measurement

FTIR spectra were obtained using a Bruker Tensor 37 FTIR spec-trophotometer equipped with deuterated triglycine-sulphate(DTGS) as detector and controlled by OPUS 4.2 software (Bruker,Germany). About 5 mg of ethanol extract of each sample wasmixed with 95 mg of KBr and then pressed to form a tablet. Thesample tablet was placed in the sample compartment and FTIRspectra were recorded in the region of 4000–400 cm�1 with32 scans/min and resolution of 4 cm�1.

FTIR spectral data pre-treatment

Spectral data pre-treatment is an important step before subject-ing the FTIR spectra data for multivariate analysis. It is necessary toperform this step in order to minimize the effect of light scattering,baseline variation, systematic noise, etc. [12] in all FTIR spectra ofthe samples. In this study, data from three different pre-treatmentmethods, namely standard normal variate (SNV), and first and sec-ond order derivative spectra, were compared.

Chemometrics analysis

Principal component analysis (PCA) and canonical variate anal-ysis (CVA) were used to build a model for discrimination ofC. longa, C. xanthorrhiza and Z. cassumunar and these analyses wereperformed in XLSTAT software version 2012.2.02 (Addinsoft, NewYork, USA).

Table 1Sources of samples.

Species Samplecode

Sources (subdistrict, regency, province)

C. longa CL-1 Wonogiri, Wonogiri, Central JavaCL-2 Ngadirejo, Wonogiri, Central JavaCL-3 Ngadirejo, Wonogiri, Central JavaCL-4 Tembalang, Semarang, Central JavaCL-5 Tembalang, Semarang, Central JavaCL-6 Tembalang, Semarang, Central JavaCL-7 Tawangmangu, Karanganyar, Central JavaCL-8 Tawangmangu, Karanganyar, Central JavaCL-9 Tegalombo, Pacitan, East JavaCL-10 Tegalombo, Pacitan, East JavaCL-11 Semen, Kediri, East JavaCL-12 Semen, Kediri, East JavaCL-13 Slahung, Ponorogo, East JavaCL-14 Slahung, Ponorogo, East JavaCL-15 Ngrayun, Ponorogo, East JavaCL-16 Tanjungkerta, Sumedang, West JavaCL-17 Tanjungkerta, Sumedang, West JavaCL-18 Rancakalong, Sumedang, West JavaCL-19 Cikembar, Sukabumi, West JavaCL-20 Cikembar, Sukabumi, West JavaCL-21 Cikembar, Sukabumi, West JavaCL-22 Gunung Putri, Bogor, West JavaCL-23 Cibungbulang, Bogor, West JavaCL-24 Dramaga, Bogor, West JavaCL-25 Purworejo, Purworejo, Central JavaCL-26 Purworejo, Purworejo, Central JavaCL-27 Wonogiri, Wonogiri, Central JavaCL-28 Tembalang, Semarang, Central JavaCL-29 Kalibawang, Kulonprogo, Special Region of

YogyakartaCL-30 Wates, Kulonprogo, Special Region of

YogyakartaCL-31 Pengasih, Kulonprogo, Special Region of

YogyakartaCL-32 Bandar, Pacitan, East JavaCL-33 Tegalombo, Pacitan, East JavaCL-34 Tegalombo, Pacitan, East JavaCL-35 Ponorogo, Ponorogo, East Java

C. xanthorrhiza CX-1 Wonogiri, Wonogiri, Central JavaCX-2 Ngadirejo, Wonogiri, Central JavaCX-3 Tembalang, Semarang, Central JavaCX-4 Tembalang, Semarang, Central JavaCX-5 Tembalang, Semarang, Central JavaCX-6 Karangpandan, Karanganyar, Central JavaCX-7 Tawangmangu, Karanganyar, Central JavaCX-8 Tawangmangu, Karanganyar, Central JavaCX-9 Semen, Kediri, East JavaCX-10 Semen, Kediri, East JavaCX-11 Slahung, Ponorogo, East JavaCX-12 Slahung, Ponorogo, East JavaCX-13 Ngrayun, Ponorogo, East JavaCX-14 Tanjungkerta, Sumedang, West JavaCX-15 Tanjungkerta, Sumedang, West JavaCX-16 Rancakalong, Sumedang, West JavaCX-17 Cikembar, Sukabumi, West JavaCX-18 Cikembar, Sukabumi, West JavaCX-19 Gunung Guruh, Sukabumi, West JavaCX-20 Gunung Putri, Bogor, West JavaCX-21 Ciampea, Bogor, West JavaCX-22 Dramaga, Bogor, West JavaCX-23 Pituruh, Purworejo, Central JavaCX-24 Purworejo, Purworejo, Central JavaCX-25 Purworejo, Purworejo, Central JavaCX-26 Wonogiri, Wonogiri, Central JavaCX-27 Tembalang, Semarang, Central JavaCX-28 Kalibawang, Kulonprogo, Special Region of

YogyakartaCX-29 Wates, Kulonprogo, Special Region of

YogyakartaCX-30 Wates, Kulonprogo, Special Region of

YogyakartaCX-31 Pengasih, Kulonprogo, Special Region of

Yogyakarta

Table 1 (continued)

Species Samplecode

Sources (subdistrict, regency, province)

CX-32 Bandar, Pacitan, East JavaCX-33 Bandar, Pacitan, East JavaCX-34 Bandar, Pacitan, East JavaCX-35 Ponorogo, Ponorogo, East Java

Z. cassumunar ZC-1 Wonogiri, Wonogiri, Central JavaZC-2 Ngadirejo, Wonogiri, Central JavaZC-3 Ngadirejo, Wonogiri, Central JavaZC-4 Tembalang, Semarang, Central JavaZC-5 Tembalang, Semarang, Central JavaZC-6 Tembalang, Semarang, Central JavaZC-7 Tawangmangu, Karanganyar, Central JavaZC-8 Tawangmangu, Karanganyar, Central JavaZC-9 Semen, Kediri, East JavaZC-10 Semen, Kediri, East JavaZC-11 Slahung, Ponorogo, East JavaZC-12 Slahung, Ponorogo, East JavaZC-13 Tanjungkerta, Sumedang, West JavaZC-14 Rancakalong, Sumedang, West JavaZC-15 Rajapolah, Tasikmalaya, West JavaZC-16 Cibadak, Sukabumi, West JavaZC-17 Cikembar, Sukabumi, West JavaZC-18 Gunung Guruh, Sukabumi, West JavaZC-19 Gunung Putri, Bogor, West JavaZC-20 Ciampea, Bogor, West JavaZC-21 Dramaga, Bogor, West JavaZC-22 Purworejo, Purworejo, Central JavaZC-23 Purworejo, Purworejo, Central JavaZC-24 Wonogiri, Wonogiri, Central JavaZC-25 Tembalang, Semarang, Central JavaZC-26 Kalibawang, Kulonprogo, Special Region of

YogyakartaZC-27 Wates, Kulonprogo, Special Region of

YogyakartaZC-28 Pengasih, Kulonprogo, Special Region of

YogyakartaZC-29 Ponorogo, Ponorogo, East Java

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Result and discussions

Discrimination by FTIR spectral analysis

Medicinal plants are complex mixture of chemicals, so their FTIRspectra will show overlapping of some characteristic absorptionbands of the functional groups in the sample [11]. The FTIR spectraof C. longa, C. xanthorrhiza and Z. cassumunar are shown in Fig. 1. Underthe same experimental conditions, the FTIR spectra of each speciesfrom different locations showed high similarities. As can be seen inFig. 1, the characteristic peaks in the FTIR spectra of the three samplesappeared at �3400 cm�1 corresponds to OAH absorption, at2800–3000 cm�1 to methyl (ACH3) and methylene (ACH2) symmet-ric and asymmetric stretching vibration, at 1740–1680 cm�1 areattributed to C@O absorption, at �1510 cm�1 are assigned to aro-matic skeletal stretching vibration, and at �1030 cm�1 are due toCAOH stretching vibration. Comparison of the FTIR spectra of C. longaand C. xanthorrhiza showed slight differences because they are fromthe same genus that they may have similar chemical components.However, when the FTIR spectra of C. longa and C. xanthorrhiza werecompared to the FTIR spectra of Z. cassumunar obvious differenceswere observed.

FTIR spectra could be used for the purposes of identification anddiscrimination of some closely-related medicinal plants. By compar-ing the FTIR spectra of the three species, it is recognized that thereare variations in their peak positions and intensities. Z. cassumunarcould be discriminated from C. longa and C. xanthorrhiza by using apeak at 1737 cm�1 for the peak only appeared in Z. cassumunar sam-ples. Two peaks at 833 cm�1 and 816 cm�1 were only found in C.longa samples while the C. xanthorrhiza and Z. cassumunar samples

Fig. 1. Representative FTIR spectra of C. longa (A), C. xanthorrhiza (B), and Z. cassumunar (C).

Fig. 2. Representative second derivative FTIR spectra of C. longa (A), C. xanthorrhiza (B), and Z. cassumunar (C).

E. Rohaeti et al. / Spectrochimica Acta Part A: Molecular and Biomolecular Spectroscopy 137 (2015) 1244–1249 1247

only showed one peak at 816 cm�1; by using this rationale, C. longacould be discriminated from the other two species in this study.Discrimination of C. xanthorrhiza from C. longa and Z. cassumunarcould be observed from the intensities of OH absorption near3400 cm�1. The intensities of the FTIR spectra of this functionalgroup were found much greater in all C. xanthorrhiza samples thanC. longa and Z. cassumunar samples.

Enhancement of the spectral resolution and amplification ofsmall differences in the ordinary spectra could be obtained usingsecond derivative spectra. With second derivative spectra, someoverlapping bands could also be resolved. Fig. 2 shows thesecond derivative FTIR spectra of the samples in the region1800–400 cm�1 and it is seen that there are some differences inthe spectra features. As illustrated in Fig. 2, it was clearly observedthat typical positive and negative peaks at 1590 and 1579 cm�1,respectively, only appeared in C. longa sample. Discrimination of C.xanthorrhiza from the other samples could be obtained by using neg-ative peaks at 989 cm�1 that were only observed in C. xanthorrhiza.Negative peak near 1738 cm�1 could be used for the identificationof Z. cassumunar because in this sample, the peak of Z. cassumunargave higher intensities compared to those of C. longa andC. xanthorrhiza. Beside the negative peak, discrimination ofZ. cassumunar from the other two plants could be obtained by using

two positive peaks at 1452 and 1430 cm�1 that were only found in Z.cassumunar sample.

Combination of FTIR spectra and chemometrics method fordiscrimination of C. longa, C. xanthorrhiza and Z. cassumunar

To confirm the results obtained from the visual inspection ofthe FTIR spectra for the discrimination of C. longa, C. xanthorrhizaand Z. cassumunar, a combination of FTIR spectra and chemomet-rics methods was used. Chemometrics is widely used to analyzea huge amount of data such as in FTIR spectra with an aim toresume information contained in the data matrix by reducingdimensions of the data, and finding the similarities or dissimilari-ties between observations and variables. In this study, some tech-niques in chemometrics, such as PCA and CVA, are used.

Pre-treatment of FTIR spectra is a standard procedure beforeusing the spectra in chemometrics analysis. SNV and first and sec-ond order derivative spectra were applied and compared. SNVworks by calculating the standard deviation of all data points ina given FTIR spectra and then the entire FTIR spectra is normalizedby this value, thus giving the FTIR spectra a unit standard devia-tion. SNV removes slope variation and also the scatter effects[23,24]. The first and second order derivative spectra are usually

Table 2Eigenvalues and cumulative percentage of total variance for each PCs.

Principal component Eigenvalue Cumulative percentage of total variance

1 165.536 59.3092 46.443 75.9493 41.023 90.6474 14.145 95.7145 3.911 97.1166 3.262 98.2857 1.760 98.9158 1.066 99.2979 0.470 99.465

10 0.415 99.614

Fig. 4. CVA plot of samples: C. longa ( ), C. xanthorrhiza ( ), and Z. cassumunar ( ).

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used to eliminate the baseline drifts and for the enhancement ofthe small spectral features [12]. In this work, pre-treatment ofthe FTIR spectra using SNV gave the best result for optimum groupsseparation for the three species. In this case, SNV was selected tonormalize the FTIR spectra before subjecting the spectra to PCAand CVA.

Principal component analysisPCA is a well-known unsupervised pattern recognition. The

main objective of the PCA is to reduce the data and extract theinformation in order to find a combination of variables or factorsfor describing major trends in a data set. PCA will transform theoriginal variables into new uncorrelated variables called principalcomponents (PCs) that maximize the explained variance in thedata on each successive component under the constraint of beingorthogonal to the previous PCs [25].

In this study, PCA was employed to discriminate the samplesaccording to the species based on the FTIR spectra in the regionof 2000–400 cm�1. This region was selected because it is complexand full of information with many vibrations attributed to thechemical components in all samples. The full 99 objects � 831variables data matrices were submitted to PCA. Table 2 providesthe information regarding the eigenvalues and cumulative per-centage of total variance from the 10 initial PCs. About 99.61% oftotal variance were explained by this 10 initial PCs. Typically,PCA plot for the first two PCs is used and being the most usefulin the analysis because both PCs contain the most variations inthe data. The closer the PCs values, the greater the similaritiesamong the samples. From the PCA plot, therefore, it could beobtained pattern of a sample, groupings, similarities and differ-ences [16].

Fig. 3. PCA plot of samples: C. longa ( ), C. xanthorrhiza ( ), and Z. cassumunar ( ).

Fig. 3 showed the score plot derived from PCA using the first twoPCs which accounted for 76% of the total variance (PC1 = 59.3% andPC2 = 16.7%). As can be seen in Fig. 3, the tested samples were clus-tered into three different groups. Most of the Z. cassumunar sampleswere separated from the C. longa and C. xanthorrhiza samples. Onlythree Z. cassumunar samples (ZC-3, ZC-10 and ZC-29) were detectedin the C. xanthorrhiza cluster. Although C. longa and C. xanthorrhizacould be discriminated, the distance between them was so close.This situation occurred due to the fact that the chemical profile ofthe two plants was nearly similar as can be seen in their FTIR spectra(Fig. 1). In general, PCA could discriminate the three species.

Canonical variate analysisCVA is one of the supervised pattern recognition and widely

used for multiple groups discrimination. The goal in CVA is to findlinear combination of variables that exhibit maximum among-groups variations to within-groups variations. Canonical variates(CVs) are the name for these linear combination [26]. In this work,CVA was used to discriminate the three herbs more clearly. CVAwill work effectively when the number of samples is more thanthe number of variables. First, PCA was employed in the FTIR spec-tra data of samples to get PCs before building a predictive modelusing CVA. The criterion proposed by Kaiser was used to determinethe number of PCs to be retained and used them in the CVA model.This criterion will retain PCs with eigenvalues greater than 1because these PCs explain as much variance as are observed inthe variables [27].

The CVA predictive model was built based on eight initial PCs(eigenvalues greater than 1) as input variables with the cumulativepercentage of total variance greater than 99%. Separate covariancematrices were used in this classification because there are differ-ences in the within-class covariance matrices from the Box test.From the result of CVA, the total variance from the two CVs was100% (CV1 = 86.7% and CV2 = 13.3%). This means that it is clearlyshowed that 100% of the original groups were correctly classifiedinto its own group (Fig. 4), indicating that the CVs obtained couldclearly discriminate the three species.

Leave-one-out cross-validation (LOOCV) method was used forevaluation of the predictive ability of this model. LOOCV worksby a single training set, one of sample is removed at a time andthe rest of the samples are used to build a model. Then theremoved sample is treated as an unknown and its class member-ship is predicted [28]. As a result from the LOOCV, about 98% ofall samples used in this study were correctly classified and only1 sample (CL-4 and CL-7) was misclassified. This result indicatesthat the model gives satisfactory prediction of the samples tested.

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Conclusions

Discrimination of C. longa, C. xanthorrhiza and Z. cassumunarwas achieved by FTIR spectral analysis in combination with PCAand CVA. Through visual analysis of the ordinary and second orderderivative FTIR spectra, the three species could be identified anddiscriminated by using their marker peaks. PCA and CVA could alsodiscriminate the three closely-related species, in which CVA gaveclearer classification based on the species. Therefore, the devel-oped method could be applied as an effective and nondestructivemethod for identification and discrimination of C. longa, C. xan-thorrhiza and Z. cassumunar.

Acknowledgment

The authors gratefully acknowledged the financial support ofthis research by the Fundamental Research Grant (No 45/13.24.4/SPK/B6-PD/2009) from The Directorate of Higher Education,Ministry of Education and Culture, Republic of Indonesia.

Appendix A. Supplementary material

Supplementary data associated with this article can be found, inthe online version, at http://dx.doi.org/10.1016/j.saa.2014.08.139.

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