Spectrochimica Acta Part A: Molecular and Biomolecular Spectroscopy 135 (2015) 757–763

Contents lists available at ScienceDirect

Spectrochimica Acta Part A: Molecular andBiomolecular Spectroscopy

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

Phylogeny of cultivated and wild wheat species using ATR–FTIRspectroscopy q

http://dx.doi.org/10.1016/j.saa.2014.07.0251386-1425/� 2014 Elsevier B.V. All rights reserved.

q This work was financially supported by the Middle East Technical University,Scientific Research Project BAP- 01-08-2012-008.⇑ Corresponding author. Tel.: +90 312 210 51 75; fax: +90 312 210 79 76.

E-mail address: sertac@metu.edu.tr (S. Onde).

Pinar Demir, Sertac Onde ⇑, Feride SevercanDepartment of Biology, Middle East Technical University, 06800 Ankara, Turkey

h i g h l i g h t s

� ATR–FTIR spectroscopy was used forphylogenetic differentiation ofwheats.� Spectra were used in hierarchical

cluster and principal componentanalysis.� Studied genera are clearly separated

by using the lignin band.� Species are grouped according to

their genome structures by using thewhole spectra.

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 10 April 2014Received in revised form 2 July 2014Accepted 15 July 2014Available online 4 August 2014

Keywords:AegilopsTriticumATR-FITRPhylogeneticPCA

a b s t r a c t

Within the last decade, an increasing amount of genetic data has been used to clarify the problems inher-ent in wheat taxonomy. The techniques for obtaining and analyzing these data are not only cumbersome,but also expensive and technically demanding. In the present study, we introduce infrared spectroscopyas a method for a sensitive, rapid and low cost phylogenetic analysis tool for wheat seed samples. For thispurpose, 12 Triticum and Aegilops species were studied by Attenuated Total Reflection–Fourier TransformInfrared (ATR–FTIR) spectroscopy. Hierarchical cluster analysis and principal component analysis clearlyrevealed that the lignin band (1525–1505 cm�1) discriminated the species at the genus level. However,the species were clustered according to their genome commonalities when the whole spectra were used(4000–650 cm�1). The successful differentiation of Triticum and its closely related genus Aegilops clearlydemonstrated the power of ATR–FTIR spectroscopy as a suitable tool for phylogenetic research.

� 2014 Elsevier B.V. All rights reserved.

Introduction such as durum wheat (Triticum turgidum L. ssp. durum (Desf.)

Taxonomically, the bread wheat (Triticum aestivum L.), which isincluded in the Magnoliophyta division of the plant kingdom,under the Poaceae family, is one of the most important and fre-quently cultivated food crops, followed by rice and maize. The tribeTriticeae, where bread wheat belongs, also contains crop plants

Husn.), barley (Hordeum vulgare L.), as well as rye (Secale cerealeL.) which were domesticated about 10,000 years ago in the NearEastern Fertile Crescent. Currently, Triticum species provide thestaple food requirements of about 35% of the human populationsaround the world [1]. During the 20th century, obtaining abioticor biotic stress-resistant wheat plants with higher productivitythrough plant breeding efforts became progressively more impor-tant. The discovery of genes invaluable to this purpose in wildwheat relatives within the genus Aegilops L. excited the plantbreeders. Although the distribution of wheat agriculture is nearlyworldwide, according to van Slageren [2]: ‘‘Aegilops species may

758 P. Demir et al. / Spectrochimica Acta Part A: Molecular and Biomolecular Spectroscopy 135 (2015) 757–763

be characterized as a Mediterranean -Western Asiatic element’’. Inthe Flora of Turkey and the East Aegean Islands [3] and its supple-ments [4], the Triticeae tribe was described as containing 74species within 16 genera. Among these, Aegilops and Triticum L.consisted of fifteen and eleven species, respectively.

Triticum species are characterized by the presence of four basicgenomes designated with the letters A, B, D, and G [5] where thecommon bread wheat possesses BBAADD genomic structure.Within the Triticum genus, species with a basic chromosome num-ber of 7 are observed. Among these species diploidy, tetraploidyand hexaploidy are quite common and are derived through hybrid-izations between Triticum and diploid species of the genus Aegilops.

Some of the Aegilops species played an important role as beingthe secondary gene pools of wheat domestication. Aegilops speciescontain the B, C, D, M, N, S, T, U, and X genomes [6]. However, notonly historically, but also taxonomically, the complex relationshipswithin the Aegilops genus and their interactions with the Triticumgenus is far from clear and subject to debate. Starting with thepre-Linnaean literature [2], the debate continued with a faster paceduring the 20th and 21st century and at least 8 classification sys-tems were proposed by various authors [7]. Until recently, these sys-tems were mostly based on morphological characteristics observedwithin herbarium samples. Within the last decade, an accumulationof molecular genetic data (analysis of nuclear, chloroplast and mito-chondrial genes) has been applied to wheat taxonomy [8,9]. Butthese techniques are expensive and technically demanding. Thisnecessitated a search for other techniques that could compensateinherent disadvantages of the conventional Polymerase Chain Reac-tion (PCR) and DNA sequencing. One such technique, although notnew, is the Fourier Transformed Infrared (FTIR) Spectroscopy,invented by Peter Fellgett in 1949. With the introduction of Fouriertransform methodology and the advances in microcomputer tech-nology, chemometric and statistical analysis techniques, today FTIRspectroscopy is a powerful tool to provide quantitative and qualita-tive information regarding the molecular conformations and inter-action of macromolecules like carbohydrates, lipids, proteins andnucleic acids. Therefore, infrared spectroscopy can be used as a fin-gerprint. Although the majority of FTIR spectroscopy applicationsconcern the characterization of molecules present in organismssuch as plant cell wall components [10,11] and bacteria [12,13], verylittle work has been done on the phylogenetic analysis of plants. Inthe earliest study concerning the phylogenetic analysis of floweringplants by FTIR spectroscopy, seven plant species were compared anda successful discrimination was observed between monocotyledon-ous and dicotyledonous species [14]. Three genera from higherplants were characterized by their chemical compositions [15]and the results positively coincided with phylogenetic standingsof these genera. Two studies on Camellia species [16,17] confirmthe power of FTIR spectroscopy towards understanding the biogeo-graphic distribution and classification of the species concerned, andthe authors pointed out a parallelism between FTIR spectroscopydata and morphological classification system. Roots of Pea and Oatplants were successfully discriminated by FTIR-ATR spectroscopy[18]. A total of 65 gymnosperm plant exudates were characterizedand compared by micro-FTIR spectroscopy [19]. The results werepostulated to have direct implications for the assessment of fossilpotential and the chemotaxonomic interpretation of modern andfossil gymnosperm exudates. Recently, phylogenetic relationshipsamong Ulmus elongata trees collected from 7 regions in China werestudied by FTIR spectroscopy, thermal gravimetric (TG) and differ-ential thermal (DT) analysis [20]. The results obtained by FTIR spec-troscopy, combined with the TG and DT analysis were proposed tobe suitable to determine the phylogenetic relationships betweenU. elongata populations.

The aim of this study is to use the practical and low-cost Atten-uated Total Reflection–Fourier Transform Infrared (ATR–FTIR)

spectroscopy as an alternative tool for clarifying the complex andmuch debated phylogenetic relationship among Triticum and Aegi-lops species.

Materials and methods

Plant materials

Dry individual plants of four Triticum species (23 samples) andeight Aegilops species (47 samples) were collected from their natu-ral habitats and the locations are given in Table 1. For collecting thesamples, a 1 m2 quadrat was used and at least 5 plants were pickedand placed in paper bags. After they were brought to the laboratoryand placed in a refrigerator, they were used within a week. In orderto prevent contamination, they were surface sterilized as describedelsewhere [21]. To remove any residual moisture, the seeds werefreeze dried for 24 h prior to sample preparation for the FTIR spec-troscopy. The seeds were then grounded for 5 min in a marble mor-tar containing liquid nitrogen. Names of the species, their genomestructures, chromosome numbers and location information wherethey were collected are given in Table 1.

FTIR spectroscopy and analysis

FTIR spectra of 70 samples (23 Triticum and 47 Aegilops) wererecorded using Perkin-Elmer Spectrum 100 FTIR spectrometer witha Universal ATR Sampling Accessory (Perkin-Elmer Inc., Norwalk,CT, USA). In order to make the powdered samples in close contactwith the Di/ZnSe ATR crystal, we used a pressure of 150 forcegauge for every sample, which was the maximum allowable pres-sure for this device. The spectra were collected at room tempera-ture in the 4000–650 cm�1 region by using Spectrum 100software (Perkin-Elmer). Each interferogram was averaged for 50scans at a resolution of 4 cm�1. Prior to seed sample spectral dataacquisition, the background spectrum was recorded. This back-ground spectrum was automatically subtracted from the spectraof samples in order to remove the interference of air spectrumfrom that of wheat seed samples. For each sample, three differentfractions which gave identical spectra were scanned under thesame conditions. The average spectra of these three repetitivemeasurements were used as the spectrum of one seed sample dur-ing the analyses. Band positions were measured from the centre ofmass by using Spectrum 100 software [22]. Baseline correctionwhich was performed by using the Spectrum 100 software, wasused only to show the resultant FTIR spectra in a presentationalmanner. None of the analysis in this study is based on baseline cor-rected spectra.

Principle Component Analysis (PCA)

Principle Component Analysis (PCA) is used to reduce thedimensions of data into a series of orthogonal eigenvectors whichare a linear function of a number of original variables, while retain-ing the information about the variations in data points [23]. For anFTIR spectral input, this corresponds to the reduction of hundredsof absorbance values at corresponding spectral wavenumbers intoa single point. The coordinates are the principle components (PC)and the plot obtained is called the scores plot [24]. Each PCdescribes the spectral variability among samples in decreasingorder. Thus, the first principle component, i.e. PC1, expresses mostof the variance in the data; PC2 expresses the second largest vari-ance in the data and so on. As a result, information about the classseparation is obtained by clustering the similar samples from thescores plot. An increase in the spatial separation between thetwo points in a scores plot corresponds to an increase in the dis-similarity between these two samples, i.e. the absorbance spectra

Table 1Plant species, their genome structures, chromosome numbers and location (altitude) used in the study.

Species Genome structure Chromosome number Location (altitude)

Ae. biuncialis CuCuMbMb 2n = 28 Kahramanmaras (750 m)Ae. triuncialis CuCuCC 2n = 28 Ankara (1100 m)Ae. umbellulata CuCu 2n = 14 Manisa-Kırkagaç (200 m)Ae. cylindrica DDCC 2n = 28 Ankara (1000 m)Ae. neglecta CuCuMtMt 2n = 28 Adana (80 m)Ae. speltoides BB 2n = 14 Tunceli (1100 m)Ae. speltoides var. aucheri BB 2n = 14 Kahramanmaras (900 m)Ae. speltoides var. ligustica BB 2n = 14 Corum (800 m)T. dicoccon AABB 2n = 28 Ankara (850 m)T. boeoticum AbAb 2n = 14 Ankara (850 m)T. monococcum AmAm 2n = 14 Ankara (850 m)T. spelta BBAADD 2n = 42 Ankara (850 m)

Fig. 1. Baseline-corrected, representative FTIR spectra of Ae. speltoides Tausch (topline) and T. spelta (bottom line) in the (A) 4000–650 cm�1, (B) 3020–2800 cm�1 and(C) 1800–650 cm�1 regions.

P. Demir et al. / Spectrochimica Acta Part A: Molecular and Biomolecular Spectroscopy 135 (2015) 757–763 759

in the case of FTIR spectra as the input. Since PCs are orthogonal toone another, their interpretation can be done independently.

By applying PCA to the FTIR spectra, the n � k spectral datamatrix A is decomposed into the scores P and loading T matrices[25];

A ¼ PTT ð1Þ

where Ank defines the absorbance value of the nth spectrum at thekth wavenumber. Thus, P is a n �m matrix and hence has thedimension of number of spectrum by principal components whileT is a k �m matrix and hence has the dimension of wavenumbersby principal components [25,26].

As a result, scores matrix defines the location of the samples in thenew principal components space and loading matrix defines the rea-son of the construction of principal components, i.e. which spectralpeaks cause the observed clustering of samples [25,27]. The princi-pal components in turn describe the statistical variation in the spec-tra, i.e. the variability of the spectrum with respect to the mean of thespectra and thus contain the characteristics of the samples[25,26,28]. Furthermore, the spectral variability among the samplesis described with decreasing order in the principal components.

The analyses were performed on vector normalized first deriv-ative spectra that were nine points smoothed with the Savitzky–Golay algorithm [29]. Vector normalization and derivation wereboth performed using the OPUS 5.5 software (Bruker Optics,GmbH).

In vector normalization, firstly the average y-value of the spec-trum in the region of concern is calculated [30]. Afterwards, thisaverage y-value is subtracted from the spectrum which results ina spectrum that is centred around y = 0. This respective spectrumis divided by the square root of the sum of y-value squares. Math-ematically defining;

am ¼P

kaðkÞN

ð2Þ

a0ðkÞ ¼ aðkÞ � am ð3Þ

a00ðkÞ ¼ a0ðkÞffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiPk a0ðkÞ½ �2

q ð4Þ

where am is the average y-value, a(k) is the y value of the spectrumat the kth position, N is the number of spectral points, a0(k) is theresult of the subtraction of the average y-value from the y valueof the spectrum at the kth position and a00(k) is the resultant y-valueof the spectrum at the kth position. It should also be noted that thevector norm of the resultant spectrum is 1, i.e.;X

k

½a00ðkÞ�2 ¼ 1 ð5Þ

The data obtained were further imported into MATLAB (MathworksInc.) and analyzed using the PCA program which was kindly pro-vided by Prof. Dr. Eric Goormaghtigh.

Hierarchical cluster analysis

Hierarchical cluster analysis determines the similaritiesbetween the spectra of samples by using distance calculation andclassification algorithms. The results are presented in the form ofa dendrogram which shows clustering in two dimensions bygraphical means. The magnitude of similarity is the heterogeneity,

Table 2Assignments of the labelled bands in Fig. 1.

Peak # Wavenumber (cm�1) Band assignment

1 3500–3300 OHANAH stretching vibrations: carbohydrates, proteins [15]2 3008 Olefinic@CH [15]3 2959 CH3 asymmetric stretching: mainly lipids with contribution from proteins, carbohydrates, nucleic acids [15]4 2923 CH2 asymmetric stretching: mainly lipids with contribution from proteins, carbohydrates, nucleic acids [15]5 2872 CH3 symmetric stretching: mainly proteins with contribution from lipids, carbohydrates, nucleic acids [15]6 2852 CH2 symmetric stretching: mainly lipids with contribution from proteins, carbohydrates, nucleic acids [15]7 1745 C@O ester stretching, lipids, carbohydrates [15]8 1642 Amide I (C@O stretching): proteins, pectins [15]9 1540 Amide II (CAN stretching, NAH bending): mainly proteins [15]

10 1514 Lignin [33]11 1446 CAH: cell wall polysaccharides [15]12 1414 OAH bending: cell wall polysaccharides, alcohols and carboxylic acids [15]13 1369, 1335, 1315, 1280 Cellulose [34,35]14 1239 Amide III (CAN and NAH stretching): mainly proteins proteins [15]15 1205 Cellulose [35]16 1150 Starch [36,37]17 1124 Starch [36]18 1101 Pectin [15]19 1076 b-(1 � >6) or b-(1 � >3) linked galactan [13]20 1049 Arabinogalactorhamnoglycan, starch [13]21 1017 Pectin [36]22 990 Arabinoxylans [38]23 932 Starch [39]24 897 Arabinogalactan, xyloglucan, galactoglucomannan [13]25 861 Starch [36]26 764 Starch [39]27 702 Cellulose [40]

Fig. 2. (A) Baseline-corrected, average FTIR spectrum of Ae. speltoides in the 1575–1485 cm�1 region. (B) Vector normalized, second derivative spectra in the sameregion.

760 P. Demir et al. / Spectrochimica Acta Part A: Molecular and Biomolecular Spectroscopy 135 (2015) 757–763

where increasing heterogeneity corresponds to increasing dissim-ilarity. In our study, hierarchical cluster analysis was performedon the lignin band (1525–1505 cm�1) as well as on the whole spec-trum (4000–650 cm�1) separately. The analyses were performedon vector normalized, first derivative spectra that were nine pointssmoothed with the Savitzk–Golay algorithm [29]. The heterogene-ity calculations were made by using the OPUS 5.5 software pro-gram with Euclidian distance and Ward’s algorithm [31]. Ward’salgorithm clusters homogeneous objects as much as possible bycombining the spectra that form a within-cluster variance withthe smallest distance [32].

The percentage of correctly differentiated species or genomes isgiven as the ‘‘Accuracy of Differentiation’’. In the case of speciesdifferentiation as Aegilops and Triticum species from the analysisof the lignin band (1525–1505 cm�1), it is found by summing thenumber of Aegilops species that are in the Aegilops Cluster withthe number of Triticum species that are in the Triticum Clusterand dividing this sum by the total number of samples. This is con-verted into a percentage by multiplying the result with 100. Calcu-lation of the accuracy of differentiation for the discrimination ofphylogenetic types (A, B and C Clades) from the analysis of thewhole spectrum (4000–650 cm�1) was done by summing the Agenome samples that are in the A Clade, the B genome samplesthat are in the B Clade and the C genome samples that are in theC Clade. The result was later divided by the total number of sam-ples and this was converted into a percentage by multiplying theresult with 100.

Results and discussion

In this phylogenetic investigation, we tried to resolve the rela-tionship of Triticum and its secondary gene pool provider Aegilopsby using a non-conventional approach namely, ATR–FTIR Spectros-copy. The analyses were performed on a total of 70 samplesbelonging to 4 Triticum and 8 Aegilops species.

Baseline-corrected, representative FTIR spectra of Ae. speltoidesTausch and T. spelta (L.) Thell. in the 4000–650 cm�1,

3020–2800 cm�1 and 1800–650 cm�1 regions are seen in Fig. 1A,B and C, respectively. The assignments of the labelled bands onthese figures are seen in Table 2.

FTIR spectra of Triticum or Aegilops seeds contain several infra-red bands arising from different components within seed tissues.Thus, the differences among Triticum or Aegilops species result indistinct spectral signatures. The chemometric methods such ashierarchical cluster analyses and principal component analyseswere applied to 12 Triticum and Aegilops species, based on the spec-tral differences. In the current study, these chemometric methodswere carried out for the lignin band (1525–1505 cm�1) as well asfor the whole spectrum (4000–650 cm�1) separately. In order to

Fig. 3. (A) Hierarchical cluster analysis of the first derivative vector normalized spectra with nine point smoothing in the 1525–1505 cm�1 range. The numbers next to thespecies’ names are the sample numbers. The accuracy of differentiation is 91.43%. (B) PC2 versus PC1 scores plot of the first derivative vector normalized spectra with ninepoint smoothing in the 1525–1505 cm�1 range. Circles: Triticum species, Squares: Aegilops species.

P. Demir et al. / Spectrochimica Acta Part A: Molecular and Biomolecular Spectroscopy 135 (2015) 757–763 761

obtain a successful differentiation, analyses were firstly applied tobaseline corrected, normalized raw data. However, better differen-tiation was obtained when the analyses were applied to the vectornormalized first derivative spectra. Normalization is a procedurewhich eliminates the differences between spectra that arise dueto absorptions from different amounts of samples [41]. In the vec-tor normalization procedure, the spectra are divided into theirEuclidian norms in order to obtain a homogeneous set of samples[42]. First derivative spectra on the other hand, are used to mini-mize the baseline variability [41] and to emphasize the structuraldifferences between spectra [43].

The differentiation of Triticum species from Aegilops specieswere achieved when the lignin band centred at 1514 cm�1 wasemployed [33]. Fig. 2A shows the 1575–1485 cm�1 region of theFTIR spectrum of Ae. speltoides and Fig. 2B shows the correspondingsecond derivative spectrum in that region. As can be seen fromFig. 2B, the lignin band lies within the range of 1525–1505 cm�1

in the absorbance spectra. Thus, chemometric methods wereapplied to the 1525–1505 cm�1 region to discriminate the speciesat the genus level. The results of the hierarchical cluster and prin-cipal component analysis performed on the vector normalized firstderivative spectra can be seen in Fig. 3A and B, respectively.

From the dendrogram seen in Fig. 3A, it is apparent that 70 sam-ples are discriminated into two major clusters as Triticum and Aegi-lops. The accuracy of discrimination was found to be as 91.43%,corresponding to 64 correct discriminations out of 70. Since thisregion corresponded to lignin band, the obvious differencesbetween lignin compositions of these two genera might be a majorfactor of this clustering. There is evidence that the lignin content ofwild plant species and their domesticated relatives show consider-able variation [44]. However, the lack of comparative studies onthe caryopsis-lignin compositions of these species prevented fur-ther deductions. Nevertheless, the PC2 versus PC1 scores plot forthis spectral range, as shown in Fig. 3B, also supported the exis-tence of discrimination between the two genera as in the case ofhierarchical cluster analysis.

In this plot, the first PC1 score corresponds to the 75% of thespectral variability between wheat species and PC2 correspondsto that of 18.3%. It can be seen that along the first principal compo-nent, Aegilops samples were clustered on the positive side whereasTriticum samples were clustered on the negative side which indi-cates differentiation of Aegilops and Triticum species at genus level.Therefore we concluded that the choice of the lignin band is suffi-cient to discriminate species at the genus level.

Fig. 4. (A) Hierarchical cluster analysis of the vector normalized, first derivative spectra with nine point smoothing in the 4000–650 cm�1 range. The numbers next to thespecies’ names are the sample numbers. (B) PC4 versus PC1 result of the first derivative vector normalized spectra with nine point smoothing in the 4000–650 cm�1 range.Stars: species with the A genome, Squares: species with the B genome and Circles: species with the C genome.

762 P. Demir et al. / Spectrochimica Acta Part A: Molecular and Biomolecular Spectroscopy 135 (2015) 757–763

We employed the whole spectrum (4000–650 cm�1) for phylo-genetic discrimination of the species since differences in the chro-mosomal DNA contribute significantly to the spectral data, whichin turn might reflect differences that exist between the species.Hierarchical cluster analysis result of the vector normalized, firstderivative spectra in the 4000–650 cm�1 range (whole range) canbe seen in Fig. 4A. The results clearly show that Triticum and Aegi-lops genera are clearly discriminated and that two major Cladeswere recognized: Clade A + B and Clade C. The first complex clade(A + B) is divided into 2 sub-clades. Sub-clade A which includedall Triticum species with a clear sub-clustering of diploid T. mono-coccum L. and T. boeoticum Boiss.) and polyploid (T. dicoccon Sch-rank and T. spelta) species having the A genome. The sub-clade Bcontained Ae. speltoides, Ae. speltoides Tausch var. ligustica (Savign.)Zhuk. and Ae. speltoides Tausch var. aucheri (Boiss.) which have theB genome. However, although Ae. cylindrica possesses DDCC gen-ome composition; it is located within this sub-clade. The secondclade (C) contained Ae. biuncialis Vis., Ae. triuncialis L., Ae. umbellula-ta Zhuk. and Ae. neglecta Req. ex Bertol., which have the C genome.

From the above hierarchical cluster analysis results, the mostnoteworthy finding was probably the clustering of the diploidand polyploid Triticum species discrete from each other but withinthe same A sub-clade of the A + B clade. Discrimination of theconcerned Triticum species according to their differences at thegenomic level were also observed in a previous phylogenetic study

based on sequence diversity in the trnT-F region of chloroplastDNA [8].

The positioning of the B sub-cluster (containing Ae. speltoidesand its varieties) next to the polyploid Triticum A sub-cluster isinteresting and also observed before [8]. This sub-cluster is namedB, since the common denominator between the species is the BBgenome (Table 1). Although still controversial, T. spelta (BBAADD)and T. dicoccon (AABB) are believed to have inherited the BB gen-ome from Ae. speltoides [9] whereas, the DD genome of T. speltacomes from a hybridization event between T. dicoccon (AABB)and Ae. tauschii Coss. (DD).

Although if possesses the DDCC genome composition, theexpected placement of Ae. cylindrica within the C-clade was notobserved, and this species was found to be located in the A + B-clade. The fact that the polyploid T. spelta (BBAADD) is also locatedwithin this clade suggests an evolutionary closeness of the two spe-cies. A previous study on the molecular cytogenetic analysis of Ae.cylindrica [45] showed that both the Dc and Cc genomes of Ae. cyl-indrica are very similar to the D and C genomes of the diploid pro-genitor species Ae. tauschii and Ae. markgrafii (Greuter) Hammer(Syn: Ae. caudata L.), respectively. Since Ae. tauschii is also the pro-genitor of the DD genome for T. spelta, positioning of the Ae. cylind-rica and T. spelta within the same clade is reasonably acceptable.Also a very similar clustering pattern including Ae. tauschii, T. spelta,T. aestivum and Ae. cylindrica was observed elsewhere [8].

P. Demir et al. / Spectrochimica Acta Part A: Molecular and Biomolecular Spectroscopy 135 (2015) 757–763 763

The principal component analysis also supported the hierarchi-cal cluster analysis by displaying a clear discrimination betweenA + B and C clades. PC4 versus PC1 scores plot of the whole spec-trum can be seen in Fig. 4B. Here, PC1 and PC4 accounted for61.5% and 4.73% of the spectral variability contained between sam-ples, respectively. As can be seen from Fig. 4B, genome A and C spe-cies clustered on the positive side while genome B speciesclustered on the negative side of the PC4 axis.

The accuracy of differentiation was found to be as 87.14%, cor-responding to 61 correct discriminations out of 70. This value isslightly less than the value obtained from the discrimination ofthe species according to the Lignin band and explains the slightdata overlaps at the PC analysis. Also as the number of speciesincrease, overlapping data points are usually observed [46] in PCanalysis. Actually with even two species such overlaps areobserved in PC analysis [47]. In our analysis, a few such near-over-laps were observed and we believe that when the evolutionarycloseness of the species is concerned, they are not surprising.

Conclusions

The results of this study demonstrated the power of ATR–FTIRSpectroscopy as an alternative tool for phylogenetic research. Notonly at the genus level, but also at the species level, the speciesconcerned are discriminated according to their evolutionary relat-edness and our results show similar patterns with a previous anal-ysis on the trnT-F region of chloroplast DNA [8]. PCR is a preciseand widely accepted technique that gives reliable results in phylo-genetic research. However, it is expensive and time consuming.

In this study, ATR–FTIR experiments coupled with hierarchicalcluster analysis and PCA were performed to phylogenetically dis-criminate the Triticum and Aegilops species. Hierarchical clusteranalysis and PCA are unsupervised classification methods in whichthe samples are clustered without any prior class assignment [13].Thus, the usage of these unsupervised techniques made it possiblefor us to compare the resultant ATR–FTIR phylogenetic discrimina-tion with that of PCR results obtained elsewhere [8].

Similar arguments hold also for other vibrational spectroscopictechniques such as Near Infrared spectroscopy (NIRS) and Ramanspectroscopy. Although there are several studies regarding NIRSand Raman spectroscopy on plants [48–52], only a limited numberof such studies are devoted to phylogenetic discrimination of plantspecies. In one case a commercially important plant species is dis-tinguished from its close taxonomic ally by using FT-NIR and FT-MIR [47]. Another study was about the discrimination of plant spe-cies in the Amazon by using FT-NIR [46]. The usage of spectroscopictechniques for the phylogenetic differentiation of plant species is atits infancy. For example, it is still unclear whether spectra obtainedwith different instruments are comparable [46]. Thus, differentvibrational spectroscopic techniques should be employed more instudying the phylogenetic differentiation of plants since no singletechnique is adequate to explain the complex nature of the evolu-tionary events leading to the formation of species.

Acknowledgement

We wish to thank Murat Ozgen and Melahat Avci Birsin fromAnkara University, Faculty of Agriculture, Department of Crop Sci-ences, for providing us the plant material.

References

[1] S. Bibi, M.U. Dahot, I.A. Khan, A. Khatri, M.H. Naqvi, Pak. J. Bot. 41 (2009) 1023–1027.

[2] M.W. van Slageren, Wild Wheats: A Monograph of Aegilops L. andAmblyopyrum (Jaub.&Spach) Eig. (Poaceae). ICARDA/WageningenAgricultural University Papers, 1994.

[3] P.H. Davis, Flora of Turkey and the East Aegean Islands, vol. 9, EdinburghUniversity Press, Edinburgh, 1985. pp 150–268.

[4] P.H. Davis, R.R. Mill, K. Tan, Flora of Turkey and the East Aegean Islands(suppl.), vol. 10, Edinburgh University Press, Edinburgh, 1988.

[5] B.S. Gill, B. Friebe, in: B.C. Curtis, S. Rajaram, H. Gómez Macpherson (Eds.),Bread Wheat: Improvement and Production, Food and AgricultureOrganization of the United Nations, Rome, Italy, 2002, pp. 71–88.

[6] B.R. Baum, T. Edwards, D.A. Johnson, Mol. Phylogenet. Evol. 53 (2009) 34–44.[7] B.K. Kilian, E.M. Mammen, R. Sharma, A. Graner, F. Salamini, K. Hammer, H.

Ozkan, in: C. Kole (Ed.), Wild Crop Relatives: Genomic and Breeding Resources,Springer-Verlag, Berlin, Heidelberg, 2011, pp. 1–76.

[8] Dizkirici, C. Kansu, S. Onde, M. Birsin, M. Ozgen, Z. Kaya, Genet. Resour. CropEvol. 60 (2013) 2227–2240.

[9] K.A. Golovnina, S.A. Glushkov, A.G. Blinov, V.I. Mayorov, L.R. Adkison, N.P.Goncharov, Plant Syst. Evol. 264 (2007) 195–216.

[10] S. Philippe, P. Robert, C.C. Barron, L. Saulnier, F. Guillon, J. Agr. Food Chem. 54(2006) 2303–2308.

[11] M. Kacurakova, P. Capeka, V. Sasinkova, N. Wellner, A. Ebringerova, Carbohyd.Polym. 43 (2000) 195–203.

[12] S. Garip, A.G. Gozen, F. Severcan, Food Chem. 113 (2009) 1301–1307.[13] D. Naumann, in: R.A. Meyers (Ed.), Encyclopedia of Analytical Chemistry, John

Wiley & Sons Ltd., Chichester, 2000, pp. 102–131.[14] S.W. Kim, S.H. Ban, H. Chung, S. Cho, H.J. Chung, P.S. Choi, O.J. Yoo, J.R. Liu, Plant

Cell Rep. 23 (2004) 246–250.[15] S.T. Gorgulu, M. Dogan, F. Severcan, Appl. Spectrosc. 61 (2007) 300–308.[16] H.F. Lu, J.B. Shen, X.Y. Lin, J.L. Fu, Taxon 57 (2008) 1274–1288.[17] J.B. Shen, H.F. Lu, Q.F. Peng, J.F. Zheng, Y.M. Tian, J. Syst. Evol. 46 (2008) 194–

204.[18] A. Naumann, G. Heine, R. Rauber, Field Crop. Res. 119 (2010) 78–84.[19] R. Tappert, A.P. Wolfe, R.C. McKellar, M.C. Tappert, K. Muehlenbachs, Int. J.

Plant Sci. 172 (2011) 120–138.[20] J.G. Gao, Y.H. Wu, G.D. Xu, W.Q. Li, G.H. Yao, J. Ma, P. Liu, Biochem. Syst. Ecol 40

(2012) 184–191.[21] M. Özgen, M. Türet, S. Altınok, C. Sancak, Plant Cell Rep. 18 (1998) 331–335.[22] G. Cakmak, F. Zorlu, M. Severcan, F. Severcan, Anal. Chem. 83 (2011) 2438–

2444.[23] H.H. Nieuwoudt, B.A. Prior, I.S. Pretorius, M. Manley, F.F. Bauer, J. Agr. Food

Chem. 52 (2004) 3726–3735.[24] T. Nakamura, J.G. Kelly, J. Trevisan, L.J. Cooper, A.J. Bentley, P.L. Carmichael,

A.D. Scott, M. Cotte, J. Susini, P.L. Martin-Hirsch, S. Kinoshita, N.J. Fullwood, F.L.Martin, Mol. Vis. 16 (2010) 359–368.

[25] D.B. Dahlberg, S.M. Lee, S.J. Wenger, J.A. Vargo, Appl. Spectrosc. 51 (1997)1118–1124.

[26] P.J. Gemperline, in: P.J. Gemperline (Ed.), Practical Guide to Chemometrics,second ed., CRC Press, 2006.

[27] F.L. Martin, M.J. German, E. Wit, T. Fearn, N. Ragavan, H.M. Pollock, J. Comput.Biol. 14 (2007) 1176–1184.

[28] L. Chen, N.C. Carpita, W.D. Reiter, R.H. Wilson, C. Jeffries, M.C. McCann, Plant J.16 (1998) 385–392.

[29] Savitzky, M.J.E. Golay, Anal. Chem. 36 (1964) 1627–1639.[30] OPUS 5.5. Software, Help Menu, Section 6.3-Data Processing.[31] J.H. Ward, J. Am. Stat. Assoc. 58 (1963) 236–244.[32] F. Severcan, O. Bozkurt, R. Gurbanov, G. Gorgulu, J. Biophoton. 3 (2010) 621–

631.[33] P. Yu, H. Block, Z. Niu, K. Doiron, J. Synchrotron Radiat. 14 (2007) 382–390.[34] C.B. Singh, D.S. Jayas, F. Borondics, N.D. White, J. Stored Prod. Res. 47 (2011)

372–377.[35] R. Liu, H. Yu, Y. Huang, Cellulose 12 (2005) 25–34.[36] P. Robert, M.L. Marquis, C.C. Barron, F. Guillon, L. Saulnier, J. Agr. Food Chem.

53 (2005) 7014–7018.[37] A. Moroi, N. Vartolomei, A.V. Arus, I.D. Nistor, I.M. Lazar, AUDJG-Food Technol.

35 (2011) 33–45.[38] L. Saulnier, P. Robert, M. Grintchenko, F. Jamme, B. Bouchet, F. Guillon, J. Cereal

Sci. 50 (2009) 312–317.[39] R. Kizil, J. Irudayaraj, K. Seetharaman, J. Agr. Food Chem. 50 (2002) 3912–

3918.[40] M. Åkerholm, B. Hinterstoisser, L. Salmén, Carbohyd. Res. 339 (2004) 569–

578.[41] L. Mariey, J.P. Signolle, C. Amiel, J. Travert, Vib. Spectrosc. 26 (2001) 151–159.[42] F. Severcan, P.I. Haris, in: F. Severcan, P.I. Haris (Eds.), Vibrational

Spectroscopy in Diagnosis and Screening, IOS Press, Amsterdam, 2012, pp.1–11.

[43] P. de la Mata, A. Dominguez-Vidal, J.M. Bosque-Sendra, A. Ruiz-Medina, L.Cuadros-Rodríguez, M.J. Ayora-Cañada, Food Control 23 (2012) 449–455.

[44] J.F. Pedersen, K.P. Vogel, D.L. Funnell, Crop Sci. 45 (2005) 812–819.[45] G. Linc, B.R. Friebe, R.G. Kynast, M. Molnar-Lang, B. Köszegi, J. Sutka, B.S. Gill,

Genome 42 (1999) 497–503.[46] F.M. Durgante, N. Higuchi, A. Almeida, A. Vicentini, Forest Ecol. Manag. 291

(2013) 240–248.[47] J.E. Maree, A.M. Viljoen, Vib. Spectrosc. 55 (2011) 146–152.[48] M. Navarro Escamilla, F. Rodenas Sanz, L. Li, S.A. Schönbichler, B. Yang, G.K.

Bonn, C.W. Huck, Talanta 114 (2013) 304–310.[49] C. Miralbés, Food Chem. 106 (2008) 386–389.[50] U.P. Agarwal, Planta 224 (2006) 1141–1153.[51] D. Cozzolino, A. Fassio, A. Gimenez, J. Sci. Food Agr. 81 (2001) 142–146.[52] O. Piot, J.C. Autran, M. Manfait, J. Cereal Sci. 32 (2000) 57–71.