Near-infrared reflectance spectroscopy for the rapid discrimination of kernels and flours of different wheat species

Journal of Food Composition and Analysis 51 (2016) 30–36

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Original research article

Near-infrared reflectance spectroscopy for the rapid discrimination of kernels and flours of different wheat species Jochen U. Zieglera,* , Martin Leitenbergera , C. Friedrich H. Longinb , Tobias Würschumb , Reinhold Carlea,c , Ralf M. Schweiggerta a b c

University of Hohenheim, Institute of Food Science and Biotechnology, Garbenstraße 25, D-70599 Stuttgart, Germany University of Hohenheim, State Plant Breeding Institute, Fruwirthstraße 21, D-70599, Stuttgart, Germany King Abdulaziz University, Faculty of Science, Biological Science Department, P.O. Box 80257, Jeddah 21589, Saudi Arabia

A R T I C L E I N F O

Article history: Received 23 March 2016 Received in revised form 4 June 2016 Accepted 11 June 2016 Available online xxx Keywords: Wheat Triticum Authenticity Adulteration Chemometrics Classification NIRS

A B S T R A C T

Since products made of ancient wheat species are enjoying increasing demand by consumers, reliable and rapid methods for the automatable differentiation of flours and kernels of costly ancient species from less expensive bread wheat are required. In the present study, we demonstrate near-infrared (NIR) reflectance spectroscopy to represent a rapid and powerful method for product authentication. Multiclass partial least square discrimination analyses (PLSDA) were based on NIR spectra of kernels and flours from bread wheat (n = 705), spelt (n = 673), durum (n = 75), emmer (n = 75), and einkorn (n = 73), showing accuracy values of 80–100%. Two-class classification analyses allowed the clear-cut differentiation of species of the same degree of ploidy (durum vs. emmer and bread wheat vs. spelt) by the PLSDA model and validation without misclassifications. Most importantly, the detection of adulterations of spelt flours with bread wheat flours was feasible. Two spectral ranges (1370–1450 nm and 1850–1930 nm) were identified to exert the highest discriminative power between bread wheat and spelt. Since NIR spectrometers are routinely being used in the cereal industry for the determination of, e.g., protein and water content of wheat kernels and flour, the implementation of our approach may instantly allow for the authenticity control of wheat kernels and flours therefrom. ã 2016 Elsevier Inc. All rights reserved.

1. Introduction The ancient hulled species spelt (Triticum aestivum ssp. spelta), emmer (T. turgidum ssp. dicoccum), and einkorn (T. monococcum) have recently gained an enormous popularity among consumers as ‘healthy alternatives’ to modern bread wheat (T. aestivum ssp. aestivum) (Shewry, 2009). This public perception was fueled by latest reports on einkorn, containing outstandingly high amounts of micronutrients such as minerals, carotenoids, and other lipophilic antioxidants (Ziegler et al., 2016; Hidalgo and Brandolini, 2014). The increasing use of alternative wheat species for processing has created an urgent demand of industrial millers and bakers for the rapid and inexpensive discrimination of kernels and flours from different wheat species. However, such methods for a reliable and robust differentiation are scarce. Recently, the alkylresorcinol composition as determined by HPLC-PDA was

* Corresponding author. E-mail addresses: [email protected], [email protected] (J.U. Ziegler). http://dx.doi.org/10.1016/j.jfca.2016.06.005 0889-1575/ã 2016 Elsevier Inc. All rights reserved.

shown to be suitable for the differentiation of wheat species with a different degree of ploidy (Ciccoritti et al., 2013; Ziegler et al., 2015; Knödler et al., 2010); however, being unable to differentiate between the commercially important spelt and bread wheat (both hexaploid). Furthermore, Ruibal-Mendieta et al. (2004) suggested the distinction of spelt and bread wheat by their oleate/palmitate ratio of their lipid fraction. However, because of expensive, laborintensive, and time-consuming laboratory work, the cereal industry has not yet implemented this method into industrial practice. As a non-invasive method, digital imaging systems were proposed for the identification and discrimination of different Canadian wheat classes (Manickavasagan et al., 2008; Neuman et al., 1989). Since the successful differentiation of bread wheat varieties by near-infrared spectroscopy (NIRS) had been previously reported (Delwiche et al., 1995; Miralbés, 2008), we now sought to translate this approach into the differentiation of different wheat species. Among several advantages of this technique, NIRS is already widespread in the cereal industry, rapidly applied and nondestructive (Cen and He, 2007; Porep et al., 2015), thus often allowing to replace tedious wet-chemical analyses (Jespersen and Munck, 2009). Main analytical parameters assessed by NIRS are

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protein and moisture content of cereal kernels and flours (Cen and He, 2007; Miralbés, 2004; Jespersen and Munck, 2009). Furthermore, NIRS has been reported to allow the detection of wheat samples contaminated with mycotoxins (Gaspardo et al., 2012; Girolamo et al., 2009), the quantitation of pigments in cereals (Geleta et al., 2014; Brenna and Berardo, 2004; Atienza et al., 2005), and the prediction of parameters regarding the baking quality of wheat (Jirsa et al., 2008; Miralbés, 2004; Dowell et al., 2006). Although NIRS has already been established in the 1960’s for cereal analyses (Agelet and Hurburgh, 2010), NIRS applications still focus on determining chemical parameters like moisture, protein, and ash content. Therefore, the aim of the present study was the application of NIRS for the differentiation of five cultivated wheat species. For this purpose, a large sample set (ntotal = 1523) comprising at least fifteen varieties each of bread wheat, spelt, durum, emmer, and einkorn was grown on different locations and harvested in several years. The development of discrimination models was based on the chemometric evaluation of the NIR spectra obtained of wheat kernels and flours. The derived models should provide an inexpensive, rapid, and automatable tool for quality and authenticity control in wheat breeding, cultivation, trading, and processing. 2. Materials and methods 2.1. Plant material and sample preparation Sample set 1a comprised kernels of 15 varieties each of bread wheat, spelt, durum, emmer, and einkorn grown on four sites in Southern Germany (Stuttgart-Hohenheim, Oberer Lindenhof, Seligenstadt, and Eckartsweier). All samples were produced by the State Plant Breeding Institute (University of Hohenheim, Stuttgart, Germany) and harvested in 2013. Spelt, emmer, and einkorn samples were dehulled and cleaned with a laboratory seed cleaner (Samatec-Roeber, Bad Oeynhausen, Germany). A detailed sample list is provided in Supplemental Table S1. Sample set 1b contained whole grain flours, which were obtained by milling an aliquot of 20 g kernels of sample set 1a to a particle size of 0.5 mm (laboratory mill ZM1, Retsch, Haan, Germany). Noteworthy, detailed results on lipophilic antioxidants and agronomic traits of the investigated sample set 1 have been previously reported (Ziegler et al., 2016; Longin et al., 2016). Sample set 2 comprised a larger collection of 627 bread wheat and 598 spelt kernel samples. A detailed list of the respective varieties, growing locations, and harvest years of sample set 2 is provided in Supplemental Table S2. As mentioned above, the investigated sample sets comprised varieties grown on four different locations within Germany. Consequently, environmental variations on the samples were considered. In order to investigate the feasibility of NIRS for the detection of adulterations of spelt flours by intentional admixture of less expensive bread wheat flours, bread wheat and spelt flours were blended in different ratios (sample set 3). The used varieties, growing locations, and mixture ratios are listed in Supplemental Table S3. 2.2. NIR spectrometric analyses NIR analyses of grain samples were performed in the reflectance mode using two different NIR spectrometers. An industrial NIR monochromator scanning spectrometer (Spectra Star 2400 RTW, Unity Scientific, Columbia, MD, USA) equipped with an InGaAs detector was applied for the acquisition of spectra in the range of 1200–2400 nm at 1 nm intervals. Three single

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spectra were averaged to obtain the used NIR spectra of all wheat kernel samples, i.e. of sample sets 1a and 2. Furthermore, a laboratory Fourier transform near-infrared (FT-NIR) spectrometer (Spectrum IdentiCheck, Perkin Elmer, Norwalk, CT, USA) equipped with IdentiCheck Reflectance Accessory (ICRA) and a lead sulphide detector was used. Acquisition was performed from 650 to 2500 nm at 0.2 nm intervals. The used NIR spectra were averaged from 16 single spectra of samples from sample sets 1a (kernels), 1b (flours), and 3 (mixtures of bread wheat and spelt flours), being placed in glass vials under complete light exclusion. 2.3. Chemometrics and data analyses Chemometric analyses were performed applying Solo software version 8.1. (Eigenvector Research, Wenatchee, WA, USA). Preprocessing the NIR-spectral data, reflectance values (R) were converted into absorbance values (A) by logarithmic transformation (A = log10(1/R)). Subsequently, the first derivative of the absorbance spectra was obtained using the Savitzky-Golay derivatization algorithm (filter width = 11, polynomial order = 2). The derived spectral data were mean centered prior to applying the discrimination method of partial least squares (PLS). To obtain the discrimination model, each wheat species was defined as a separate class. Cross-validation of the model was executed using 20 sub-validation sets of 4 samples each (Venetian blinds crossvalidation). Additionally, an external validation was carried out for all models by splitting the sample set randomly into a calibration and a validation set. Supplemental Tables S1 and S2 show the assignment of the samples to the respective groups. All identified samples were classified as true positive (TP), false positive (FP), true negative (TN), and false negative (FN). Then, the sensitivity of the model for one class was obtained as the percentage of true positively classified samples among all samples of the respective class (TP/(TP + FN)
100%), while the specificity represented the percentage of true negatively assigned samples among all samples having an actual identity different to that of the assigned class (TN/(TN + FP)  100%). The accuracy of the model was the rate of all true positively and true negatively assigned samples among all samples ((TP + TN)/(TP + TN + FP + FN)  100%). For all two-class classifications, the Matthews correlation coefficient was computed (Matthews, 1975). The calculated models included data of all respective samples and therefore, no outliers were excluded. Furthermore, an unsupervised principal component analysis (PCA) based on the preprocessed NIR spectra was used to classify bread wheat and spelt samples of sample set 2. NIR spectra of bread wheat and spelt flour mixtures (sample set 3) were preprocessed as described above, and calibrated with a partial least squares (PLS) regression, to investigate the applicability of NIRS for spelt flour adulteration. 3. Results and discussion 3.1. Relevant spectral range A laboratory Fourier transform (FT) NIR spectrometer (Spectrum IdentiCheck) and a dispersive spectrometer for industrial application (Spectra Star 2400) were used to record NIR spectra of wheat kernels. Following common application notes of the cereal industry, the Spectra Star 2400 was set to acquire spectral data from 1200 to 2400 nm. Nevertheless, we used our laboratory FT-NIR spectrometer to record a wider spectral range from 650 to 2500 nm in order to assess the influence of the commonly omitted spectral ranges from 650 to 1200 nm and from 2400 to 2500 nm. The absorbances of these spectral regions showed large intensity variations, and their inclusion into our

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chemometric model was insignificant or even hampering the discriminative power (data not shown). Therefore, only the spectral range of 1200–2400 nm was used for data from both spectrometers, allowing faster acquisition cycles, and additionally facilitating the implementation of our model into current industrial NIR applications. 3.2. Five-class discrimination of kernels of bread wheat, durum, emmer, einkorn, and spelt Based on preprocessed NIR spectra, partial least square discrimination analyses (PLSDA) were performed to discriminate between bread wheat, spelt, durum, emmer, and einkorn kernels (sample set 1a). Fig. 1A and B shows the resulting confusion matrices for models considering spectra from both used spectrometers. Irrespective of the applied spectrometers, PLSDA were computed with 6 latent variables (LVs) and yielded highly comparable results, showing the robustness of the discrimination model. However, as shown in Table 1, the model based on spectra recorded with the industrial spectrometer reached slightly higher accuracy values for the classification of all five wheat species (88.9–100% for model; 84.6–99.7% for cross-validation) than that recorded with the laboratory FT-NIR spectrometer (83.6–95.0% for model; 80.2–93.3% for cross-validation). For an external validation of these models, the sample sets were divided randomly into a calibration set (40 samples) and a validation set (20 samples) as shown in Supplemental Table S1. The external validation showed accuracy values of 78.0–99.0%, comparable to those of the crossvalidation (Table 1). Furthermore, accuracy clearly depended on the species examined. For instance, all einkorn samples were classified correctly in the model using the industrial NIR spectrometer (Fig. 1B). In contrast, 19 of 60 emmer samples (sensitivity 68.3%) and 16 of 60 spelt samples (sensitivity 73.3%) were misclassified. In total, 46 of 298 samples were misclassified with the industrial NIR spectrometer-based model, whereas the FT-NIR spectrometer-based model displayed 73 misclassifications. The results presented are comparable with previous reports on NIRS discriminations among different varieties of bread wheat with accuracy values of 88–99% (Miralbés, 2008; Delwiche et al., 1995). 3.3. Two-class discrimination of bread wheat vs. spelt and durum vs. emmer Since recognition rates of multi-class classifications were previously described to be lower than those of pairwise classifications (Neuman et al., 1989), we sought to further increase the discriminative power between wheat kernels of different species by a reduction to two wheat species (classes). The analytical discrimination of equiploid wheat species was previously shown to be highly intricate or even impossible (Ziegler et al., 2015; Ciccoritti et al., 2013). In general, studies on the differentiation of wheat species with the same ploidy are scarce. Therefore, models for the hexaploid species (bread wheat and spelt) and the tetraploid species (durum and emmer) were calculated based on spectra recorded with the industrial NIR spectrometer. Strikingly, the model allowed discriminating all durum (n = 60) from emmer (n = 60) samples and vice versa, without any misclassified samples. Therefore, an accuracy of 100% was obtained for the recognition of both species in the model, the cross-validation, and the external validation (Table 2), indicating that a sufficient number of spectra was included in the model for a clear-cut classification of durum and emmer samples. Considering the hexaploid species, one spelt sample out of 60 was misclassified as bread wheat in the calibration model, while 4 and 2 spelt samples were erroneously assigned in the cross-

Fig. 1. Confusion tables of multi-class discrimination analyses of kernel spectra assessed with a laboratory FT-NIR spectrometer (A) and a industrial spectrometer (B), and flour spectra assessed with a laboratory spectrometer (C).

validation and the external validation, respectively (Table 2). Despite the high accuracies of 99.2%, 96.7%, and 90.5% for the model, the cross-validation, and the external validation, respectively, we hypothesized that a clear-cut classification of all samples

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Table 1 Sensitivity, specificity, and accuracy of the PLS model (M), the cross-validation (CV), and the external validation (EV) for the NIR-based multi-class discrimination of kernels and flours from different wheat species. na

Species

Sensitivity

Specificity

M

EVb

CV

Kernel (laboratory FT-NIR spectrometer) Bread wheat 60 81.7 Spelt 60 80.0 Durum 60 80.0 Emmer 60 41.7 58 94.8 Einkorn

M

Accuracy EVb

CV

M

EVb

CV

66.7 55.0 76.7 23.3 87.9

65.0 60.0 65.0 35.0 95.0

95.4 90.3 94.5 94.1 90.3

92.4 86.6 90.8 87.8 90.8

92.5 86.3 93.8 88.8 93.8

92.6 88.3 91.6 83.6 95.0

87.2 80.2 87.9 74.8 93.3

87.0 81.0 88.0 78.0 94.0

86.7 73.3 95.0 68.3 100.0

76.9 60.0 90.0 100.0 53.3

90.0 65.0 90.0 80.0 95.0

96.6 94.1 95.8 94.1 100.0

93.3 92.0 92.4 92.4 99.6

96.3 96.3 97.5 90.0 100.0

94.6 89.9 95.6 88.9 100.0

89.9 85.6 91.9 84.6 99.7

95.0 90.0 96.0 88.0 99.0

Flour (laboratory FT-NIR spectrometer) 58 89.7 Bread wheat Spelt 60 78.3 Durum 57 96.5 Emmer 60 90.0 Einkorn 57 100.0

70.7 61.7 96.5 78.3 98.2

94.4 60.0 81.3 50.0 100.0

94.4 96.6 98.3 99.1 100.0

91.0 92.2 96.2 96.6 100.0

88.2 97.3 91.0 94.6 100.0

93.5 92.8 97.9 97.3 100.0

87.0 86.0 96.2 92.8 99.7

89.4 89.4 89.4 85.1 100.0

Kernel (industrial spectrometer) 60 Bread wheat Spelt 60 Durum 60 Emmer 60 Einkorn 58

a b

Sample size. Detailed information about samples included into calibration and validation is supported in Supplemental Tables S1 and S2.

Table 2 Sensitivity, specificity, and accuracy of the PLS model (M), the cross-validation (CV), and the external validation (EV) for the NIR-based two-class discrimination of durum vs. emmer and bread wheat vs. spelt. Species

K/Fa

nb

LVc

Sensitivity

Specificity

M

CV

EV

f

false classifiedd

Accuracy

M

CV

EV

f

M

CV

EV

f

MCCe

M

CV

EV

f

M

CV

EVf

Durum Emmer

K

60 60

6

100 100

100 100

100 100

100 100

100 100

100 100

100 100

100 100

100 100

0

0

0

1

1

1

Bread wheat Spelt

K

60 60

6

100 98.3

100 93.3

90.0 100

98.3 100

93.3 100

100 90.0

99.2 99.2

96.7 96.7

95.0 95.0

1

4

2

0.983

0.935

0.905

Bread wheat Spelt

K

450 450

4

100 100

100 100

100 100

100 100

100 100

100 100

100 100

100 100

100 100

0

0

0

1

1

1

Bread wheat Spelt

F

58 60

6

100 100

91.4 93.3

94.4 90.0

100 100

93.3 91.4

90.0 94.4

100 100

92.4 92.4

92.1 92.1

0

9

3

1

0.847

0.843

a b c d e f

Model based on spectra from kernels (K) or flours (F). Sample size. Latent variables of the PLS model. Including false negatively and false positively assigned samples. MCC: Matthews Correlation Coefficient. Detailed information about samples included into calibration and validation is supported in Supplemental Tables S1 and S2.

might be achieved when increasing the sample size. Hence, an expanded sample set 2 comprising of 627 bread wheat and 598 spelt samples grown on three different locations was investigated to increase the robustness of the PLS discrimination of bread wheat and spelt kernels by NIRS. These samples were randomly divided into a calibration set including 450 samples each of bread wheat and spelt and a validation set of 177 bread wheat and 148 spelt samples. In the resulting PLSDA model (4 LVs), all samples were classified correctly without misclassifications irrespective of applying a cross-validation (Table 2) or an external validation (data not shown). Consequently, the model, calibrated and validated with an ample sample set, demonstrated its capability of unambiguous differentiation of bread wheat and spelt kernels by NIRS. Our results based on NIR spectra in the range of 4167– 8333 cm 1 are in agreement with preliminary findings obtained from Fourier transform mid-infrared-attenuated total reflection (FT-MIR-ATR; 575–4500 cm 1) spectra of a small sample set

comprising 5 spelt and 3 bread wheat varieties (Wiwart et al., 2014). To confirm the obtained PLSDA results, an unsupervised principal component analysis (PCA) model was calculated based on the NIR spectra of the complete sample set 2 (598 spelt samples and 627 bread wheat samples). The resulting score plot is displayed in Fig. 2A. Astonishingly, the first two principal components (PC1 and PC2) explained 94.7% of the total variance among the bread wheat and spelt samples (60.2% by PC1; 34.5% by PC2). Bread wheat and spelt samples formed two differentiated clusters, although an entirely discrete separation of all bread wheat and spelt as achieved by PLSDA was not obtained by PCA. Suchowilska et al. (2012) previously suggested the usefulness of MIR spectra (650–4000 cm 1) to differentiate between different wheat species, including 12 bread wheat, 10 spelt, 13 emmer, and 11 einkorn samples. In agreement with our study, single spelt varieties showed considerable similarities with bread wheat and

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Fig. 2. Scores plot (A) and total loadings plot (B) of a PCA model based on NIR spectra of bread wheat and spelt kernels (sample set 2). See Supplemental Table S2 for further information on the used wheat varieties. Total loadings represent the sum of the absolute values of the loadings of a specific wavelength on PC1 and PC2 that had been multiplied with the percentage of the explained variance of the respective PCs.

einkorn samples (Suchowilska et al., 2012). However, when using our NIR spectra (4167–8333 cm 1) for the large sample set 2 and the PLSDA model based on four latent variables (LVs), a complete clear-cut separation of bread wheat from spelt samples was

successfully achieved by NIR spectroscopy, although being not illustratable due to its 4-dimensional character. In order to investigate the discriminative potential of specific spectral ranges, the absolute values of loadings of a specific

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wavelength on PC1 and PC2 were multiplied with the percentage of explained variance of the respective PCs and then summed up to yield the total loadings, as previously described by Steingass et al. (2015). These total loadings were plotted against the respective wavelength in order to reveal which wavelength contributed most to the observed discrimination (Fig. 2B). Two spectral ranges (1370–1450 nm and 1850–1930 nm), which correspond to overtone bands of R OH bonds (Cen and He, 2007; Jespersen and Munck, 2009), exerted the highest impact on the discrimination of wheat samples. 3.4. Discrimination of flours and detection of adulterations Wheat kernels were milled to whole grain flours (sample set 1b) to obtain a higher sample homogeneity and, thus, to enhance the sensitivity and accuracy of the wheat species classification. By analogy to the kernels, spectra recorded with our laboratory FT-NIR spectrometer were computed with a PLS discrimination analysis based on 6 LVs. The resulting model showed significantly enhanced classification parameters for all wheat species (Table 1). The accuracy of the model ranged from 92.8% for spelt to 100% for einkorn. Interestingly, 18 of a total of 27 misclassified samples (out of 292 flour samples in total) were caused by false assignment of bread wheat and spelt (Fig. 1C) – two commercially important and differently priced species. Contrariwise, the kernel-based model only yielded 3 false assignments of bread wheat and spelt (Fig. 1A). Thus, spectra of flours of these species unexpectedly resulted in an inferior discriminative power as compared to those of the kernels. In contrast, the differentiation of the remaining species was superior when examining flours instead of kernels. By analogy to the kernels, a two-class discrimination model (6 LVs) of bread wheat and spelt flour NIR spectra was computed. All samples were correctly classified in this model, whereas the crossvalidation showed 9 misclassifications (Table 2). The high accuracy values (100 and 92.4% for model and cross-validation, respectively) illustrate the feasible application of NIRS for the classifications of wheat flours, by analogy to our results on wheat kernels. Besides the classification of pure flours, the detection of adulterations by blending different wheat species is of high economic interest. Therefore, investigating the feasibility of NIRS

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for the detection of adulterations, spelt flour was blended with different proportions of bread wheat (Supplemental Table S3). A PLS regression with 3 LVs was calculated, and the predicted ratio of bread wheat flour in the mixture was plotted over the real relative amount of admixture in the sample (Fig. 3). The resulting calibration model showed an unexpectedly high coefficient of determination (R2 = 0.966), and a standard error of calibration (RMSEC) of 5.2%. However, applying a cross-validation (6 subvalidation sets with 3 samples each), the prediction accuracy decreased, showing a standard error of cross-validation (RMSECV) of 22.6%, probably caused by the small number of samples included in our calibration set used to provide the targeted proof-ofconcept. Consequently, NIRS was shown to allow the detection of adulterations of expensive spelt flours with cheaper bread wheat flours. Further research on a broader sample set of flours is expected to provide models with an increased accuracy. 4. Conclusion The present study demonstrated the successful discrimination of five cultivated wheat species by NIR spectroscopy and subsequent chemometric analyses. The multiclass model based on 5 wheat species investigated possessed lower discriminative power than our fully discrete two-class systems comparing bread wheat vs. spelt or durum vs. emmer. Nevertheless, all models showed accuracy values >80%. In Germany, spelt is the second most important wheat species for bread making after bread wheat. Since spelt has a higher price than bread wheat due to lower crop yields, the differentiation of spelt from bread wheat is of high interest for the cereal industry. Considering a calibration set of 900 samples and an external validation set of 325 samples, the differentiation of bread wheat and spelt kernels was achieved without misclassifications, demonstrating the high discriminative power of the analyses. Beyond differentiating kernels, NIRS combined with PLSDA also allowed the successful classification of wheat flours and the detection of mutual adulterations of wheat flours from different species. Further study should be encouraged to identify the compounds responsible for the observed clear-cut classification. Although being already in broad use by the cereal industry, NIRS is expected to gain further importance as an important tool for the quality and authenticity control of wheat kernels and flours, increasingly superseding time-, and labor-intensive wetchemical analyses. Acknowledgements We thank Christiane Maus and Verena Till for assessing the NIR spectra at the State Plant Breeding Institute. One of the authors (J.U. Z.) is grateful for funding of the present research by the Landesgraduiertenförderung Baden-Württemberg (State Graduate Scholarships). Appendix A. Supplementary data Supplementary data associated with this article can be found, in the online version, at http://dx.doi.org/10.1016/j.jfca.2016.06.005. References

Fig. 3. Predicted and real proportion of bread wheat flour added to spelt flour, i.e. 0% represents a pure spelt flour and 100% a pure bread wheat flour. The used flours originated from two commercially-relevant varieties of spelt (Oberkulmer Rotkorn, Samir) and bread wheat (JB Asano, Cubus), and were grown on two different locations. Further details may be found in the Supplementary Table S3.

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