Classifying rapeseed varieties using Fourier transform infrared photoacoustic spectroscopy (FTIR-PAS)

Computers and Electronics in Agriculture 107 (2014) 58–63

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Computers and Electronics in Agriculture journal homepage: www.elsevier.com/locate/compag

Classifying rapeseed varieties using Fourier transform infrared photoacoustic spectroscopy (FTIR-PAS) Yuzhen Lu a, Changwen Du a,⇑, Changbing Yu b,⇑, Jianmin Zhou a a

State Key Laboratory of Soil and Sustainable Agriculture, Institute of Soil Science, Chinese Academy of Sciences, Nanjing 21008, PR China Key Laboratory of Biology and Genetic Improvement of Oil Crops, Oil Crops Research Institute of the Chinese Academy of Agricultural Sciences, Ministry of Agriculture, Wuhan 430062, PR China b

a r t i c l e

i n f o

Article history: Received 17 January 2014 Received in revised form 6 May 2014 Accepted 22 June 2014

Keywords: Rapeseeds Fourier transform infrared photoacoustic spectroscopy (FTIR-PAS) Variety classification Partial least squares-discriminant analysis (PLS-DA) Support vector machines (SVM) Successive projections algorithm (SPA)

a b s t r a c t This study proposed a methodology for classification of rapeseed varieties using Fourier transform infrared photoacoustic spectroscopy (FTIR-PAS). For this purpose, principal components analysis (PCA) was first used to reveal the separation of three varieties of rapeseeds, and then partial least squares-discriminant analysis (PLS-DA) and support vector machines (SVM) were employed for the classification task. The overall classification error rates (ERs) of prediction set were 7.5% and 0 for the models of PLS-DA and SVM, respectively. Furthermore, successive projections algorithm (SPA) was adopted to choose an appropriate variable subset as the inputs of PLS-DA and SVM. Both SPA-PLS-DA and SPA-SVM models gave improved predictive accuracy with significantly reduced model variables. The results of this study had showed the good performance of FTIR-PAS as a rapid, non-destructive and objective tool for classifying varieties of rapeseeds. Ó 2014 Elsevier B.V. All rights reserved.

1. Introduction Rapeseed (Brassica napus L.) is one of the most important oilseed crops cultivated around the world (Szydlowska-Czerniak et al., 2010). The variety of rapeseeds directly affects crop yield, and seed quality parameters such as the contents of oil and protein, and the fatty composition. Thus, it is important to classify rapeseed varieties. Rapeseed varieties are traditionally classified based on visual inspection on certain characteristics including seed size, color and weight. However, results of visual inspection, more or less, are plagued with the inconsistency and variability associated with the referees’ perception. Moreover, many varieties of rapeseeds are not visually distinct due to morphologic similarities of seeds. Thus, some biological methods such as protein electrophoretic and molecular marker techniques, are proposed for variety classification (Jedra et al., 1999; Lombard et al., 2000; Curn and Zaludova, 2007). This kind of methods, however, usually involves time-consuming and destructive chemical procedures. Therefore, a fast, nondestructive and objective method is needed for classification of rapeseed varieties. Non-destructive sensing technologies including visible/nearinfrared spectroscopy (Vis/NIRS) and mid-infrared spectroscopy ⇑ Corresponding authors. Tel.: +86 025 86881565; fax: +86 025 86881000 (C. Du; C. Yu). E-mail addresses: [email protected] (C. Du), [email protected] (C. Yu). http://dx.doi.org/10.1016/j.compag.2014.06.005 0168-1699/Ó 2014 Elsevier B.V. All rights reserved.

(MIRS), have provided powerful means for varietal classification of agricultural/food products (Cen et al., 2007; Miralbes, 2008; Cozzolino et al., 2009; Luo et al., 2011), due to the merits of rapidity, non-destructiveness, good reproducibility and absence of chemicals. Recently, Zou et al. (2011) presented a study regarding classification of rapeseed varieties using NIRS. Zou et al. extracted the first six principal components (PCs) as the input of distance discriminant analysis (DDA) and back-propagation neural network (BPNN) to distinguish five varieties of rapeseeds. However, the extraction of PCs was problematic since principal component analysis (PCA) was applied to the whole sample set rather than the calibration set, which might lead to overoptimistic predictive results. Further work is still needed to explore the performance of spectroscopy technologies in classifying rapeseed varieties. Photoacoustic spectroscopy (PAS) is unique as a non-destructive sensing technique, because it directly measures the energy absorbed by samples, rather than what is transmitted or reflected in NIRS or MIRS (Ryczkowski, 2010), thus making photoacoustic (PA) signals less susceptible to scattering effects of sample particles (Schmid, 2006). PAS has salient advantages concluding depth profiling, minimal sample preparation and suitability for a wide range of sample types (Bageshwar et al., 2010). PAS operated in Fourier transform mid-infrared (FTIR) system allows good quantitative and qualitative results to be easily obtained (McClelland et al., 2002). In the past decade, FTIR-PAS has found acceptance

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in quantitative analysis for woods (Bjarnestad and Dahlman, 2002), pulps (Nishi et al., 2006; Dang et al., 2007), soils (Du et al., 2009), and foods (Anderson et al., 2013). FTIR-PAS has been also utilized for food characterization (Irudayaraj et al., 2001), microorganism detection (Irudayaraj et al., 2002), and varietal identification of soybean seeds (Caires et al., 2008) and coffees (Gordillo-Delgado et al., 2012). Recently, Lu et al. (2014) successfully applied FTIRPAS for quantification of quality parameters in rapeseeds. However, no attempts of using FTIR-PAS for varietal classification of rapeseeds have been made. The main goal of this study was to investigate the use of FTIRPAS for classifying rapeseed varieties. To achieve this, partial least squares discriminant analysis (PLS-DA) and support vector machines (SVM) were used to develop classification models. Besides, classification models were further established based on a reduced subset of spectral variables selected by successive projection algorithm (SPA). The results obtained from all models were assessed in terms of classification error rates (ERs). 2. Materials and methods 2.1. Rapeseed samples Three common varieties of rapeseeds with a total of 120 samples were provided by Oil Crops Research Institute of the Chinese Academy of Agricultural Sciences. They were Deyou No.5, Qingza No.3 and Qingyou No.11. These rapeseed samples were harvested from a field experiment conducted in Yingtan Ecology Experimental Station. The seed color of all samples to be studied was black and difficult to visually classify. Of the whole samples, 59 samples were Deyou No.5, 34 Qingza No.3 and 27 Qingyou No.11. Before spectral scanning, all samples were air-dried and then stored in plastic bags at room temperature. 2.2. FTIR-PAS measurements Intact samples were directly subjected to spectral measurement using a Fourier transform infrared spectrometer (Nicolet 6700, USA) equipped with a photoacoustic cell (Model 300, MTEC, USA). After placing the sample (about 20 rapeseeds per test) in the cell holding cup (diameter 5 mm, height 3 mm) and purging the cell with dry helium (10 ml min1) for 10 s to remove CO2 and H2O, the scans were conducted in the mid-infrared wavenumber range of 500–4000 cm1 with a resolution of 4 cm1 and a mirror velocity of 0.32 cm s1. 32 successive scans were recorded, and the average spectrum for each sample was used in the data analysis. A carbon black reference was used for spectra-intensity normalization. 2.3. Spectral pretreatment and sample set partition The normalized spectra were preprocessed with a smoothing filter of Savitzky–Golay (Savitzky and Golay, 1964) with 23-point window and a polynomial of order 1. Principal components analysis (PCA) was applied to the smoothed spectra to examine the clustering of rapeseed varieties. The whole spectral dataset was divided into calibration and prediction sets, according to Kennard–Stone algorithm (Kennard and Stone, 1969). Partition results are shown in Table 1. Besides, spectral data were mean-centered before subsequent modeling procedures. 2.4. Partial least squares-discriminant analysis (PLS-DA) Partial least squares-discriminant analysis (PLS-DA) is implemented based on the standard PLS regression algorithm by using class variables in place of numeric variables. For two-class

Table 1 The number of samples in the sets of calibration and prediction. Sample set

Deyou No.5

Qinyou No.11

Qingza No.5

Calibration set Prediction set

39 20

18 9

23 11

problems, PLS1 algorithm is employed. In PLS1, the dummy variable Y is used as the response variable, and is set to 1 if the sample is in one of the two classes, and 0 if not. The cut-off value is usually 0.5 when the class number of an unknown sample is to be predicted. The PLS2, however, comes into play where more than two classes of samples need to be classified (Brereton, 2009). In this case, the class (one of N) of each sample is coded with N binary strings. For instance, in our work involving three classes, each sample is coded as one of the three following vectors, [1, 0, 0], [0, 1, 0], [0, 0, 1], denoting the classes 1, 2, and 3, respectively. More information about the theory and applications of PLS-DA can be found in the studies of Barker and Rayens (2003) and Galtier et al. (2011). In PLS-DA, the number of latent variables (LVs) needs to be carefully tuned. In this study, the optimal number of LVs in PLSDA, was decided based on the minimal classification error through a leave-ten-out cross validation. And the PLS-DA classification model was built in Matlab R2011b (The Math Works, USA), using PLS-Toolbox 4.2 (Eigenvector Research Inc., USA). 2.5. Support vector machines (SVM) Support vector machine (SVM) is a powerful machine learning algorithm developed by Cortes and Vapnik (1995). SVM is based on the strategy of structural risk minimization (SRM), and has an excellent training and generalization ability (Pontil and Verri, 1998). Thorough review about applications of SVM for classification purposes was made by Xu et al. (2006). In SVM, input vectors are mapped to a high dimensional space where a maximal separating hyperplane is constructed. By the use of kernel functions, all necessary computations are performed directly in the input space. Radical basis function (RBF) is widely used for nonlinear problems, and it can reduce the computational complexity of the training procedure and obtain good prediction results. For RBF, two parameters, c and c need to be a priori tuned. c is a regulation constant affecting the generalization performance of LS-SVM models, and c is the cost factor which controls the trade-off between training errors and model complexity of SVM models. The details of SVM classifier can be found in relevant articles (Evgeniou et al., 2000; Brereton and Lloyd, 2010). In this study, the optimal parameters of c and c were decided based on the minimal classification error through a two-dimension grid search coupled with a leave-ten-out cross validation, and the SVM classification model was built in Matlab R2011b (The Math Works, USA), using LIBSVM 3.17 toolbox (Chang and Lin, 2011). 2.6. Successive projection algorithm (SPA) The successive projections algorithm (SPA) was originally proposed by Araujo et al. (2001) for spectral variable selection in the framework of multivariate regression. Later, Pontes et al. (2005) modified SPA to handle classification problems. The goal of SPA is to select a small subset of variables with minimum multi-collinearity and maximum information. The core of SPA consists of a series of projection operations carried on the calibration spectral matrix, by which a series of subsets of variables could be formed. In order to select the most appropriate subset, a cost function associated with the average risk of misclassification by linear discriminant analysis (LDA) in a validation set, is calculated to guide variable selection. A detailed description of SPA and its

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applications is given elsewhere (Araujo et al., 2001; Pontes et al., 2005; Soares et al., 2013). In this study, the variables selected by SPA were used as the input of PLS-DA and SVM, and then led to the models of SPA-PLS-DA and SPA-SVM, respectively. SPA procedures were implemented in Matlab R2011b (The Math Works, USA). 2.7. Model evaluation standard To evaluate the performances of classification models, the error rate (ER) of classification for each class and overall samples in the calibration and prediction, was calculated and compared. The ER was defined the ratio of the number of misclassified samples to the total number of a sample set (Fan et al., 2011). 3. Results and discussion Fig. 1(a) shows the smoothed PA spectra of rapeseed samples of three varieties, Deyou No.5, Qinyou No.11 and Qingza No.5. The PA spectrum is a holistic reflection of compositional characteristics of the rapeseed matrix, and detailed explanation about the peaks observed in the PA spectra had been given elsewhere in our work (Lu et al., 2014). According to Fig. 1(a), all samples exhibited similar trends of PA spectra curves, and no visual separation was observed among three varieties of rapeseeds. So it was difficult for discriminate rapeseeds varieties directly based on the PA spectra. However, differences in PA intensity among rapeseed samples, associated with quantity differences of rapeseed constituents, could be found. Those differences would make it possible to discriminate different varieties. 3.1. PCA PCA was performed on the PA spectra to examine the qualitative differences among the three varieties of rapeseeds. Fig. 1(b)

presents the PCA score plot (PC1  PC2) derived from the PA spectra of all rapeseed samples. The first and second principal components (PCs) accounted for 82.59% and 10.18% of the total variance, respectively. Obviously, samples of the same variety basically clustered together, and no overlaps were observed among three varieties. The PCA score plot preliminarily showed that FTIRPAS spectra contained information related to rapeseed varieties, which could be exploited by discriminant models in further analysis. 3.2. PLS-DA model PLS-DA was employed to develop a calibration model to classify rapeseed varieties. The number of LVs in PLS-DA played a crucial rule for obtaining good classification results. Fig. 2(a) shows the plot of overall error rate (ER) of classification versus the number of LVs in a leave-ten-out cross validation. According to the plot, the first six LVs were retained for model calibration due to the lowest ER for cross validation. The classification results of the PLS-DA model are summarized in Table 1, and the predicted classes of samples in prediction set are shown in Fig. 2(b). It could be seen that four samples were misclassified in the calibration set, which corresponded to the overall ER of 5%. The number of misclassified samples for Deyou No.5, 1, Qinyou No.11 and Qingza No.5, was one, two and one, respectively. Qinyou No.11 got the maximal ER of 11.1%, which was understandable due to a relatively dispersive distribution of samples in PC1  PC2 score plot. According to Fig. 2(b), there existed three samples misclassified in prediction set, corresponding to the overall ER of 7.5%. Two samples were the variety Deyou No.5 and one was Qingza No.5. The samples of Qinyou No.11 were correctly classified. In general, the PLS-DA model achieved more than 90% success rates in the calibration and prediction sets, which verified the feasibility of FTIR-PAS for classifying rapeseed varieties.

Fig. 1. (a) The smoothed PA spectra of all rapeseed samples; different color lines correspond to different rapeseed samples. (b) PC1  PC2 score plot for the overall set of 120 rapeseed samples (h: Deyou No.5, 4: Qinyou No.11, and s: Qingza No.5). The variance explained by each principal component (PC) is indicated in parentheses.

Fig. 2. (a) Overall error rate of classification versus PLS-DA latent variables in a leave-ten-out cross validation for discrimination of rapeseed varieties; (b) the predicted classes of each rapeseed sample obtained from the PLS-DA model (h: Deyou No.5, 4: Qinyou No.11, and s: Qingza No.5), and the zero values of predicted classes indicate the misclassification of samples.

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Fig. 3. (a) The contour plot of the optimization parameters of c and c in a leave-ten-out cross validation for discrimination of rapeseed varieties, and the position of the sign ‘X’ indicated the optimal result; (b) the predicted classes of each rapeseed sample obtained from the SVM model (h: Deyou No.5, 4: Qinyou No.11, and s: Qingza No.5), and the zero values of predicted classes indicate the misclassification of samples.

Table 2 Classification results of different models in the calibration set and prediction set. Classification models

Calibration set

Prediction set

ER for each variety (%)

PLS-DA SVM SPA-PLS-DA SPA-SVM

Overall ER (%)

Class 1

Class 2

Class 3

2.6 0.0 7.8 0.0

11.1 0.0 0.0 0.0

4.3 0.0 4.3 0.0

5.0 0.0 5.0 0.0

ER for each variety

Overall ER (%)

Class 1

Class 2

Class 3

10 0.0 0.0 0.0

0.0 0.0 0.0 0.0

9.1 0.0 18.2 0.0

7.5 0.0 5.0 0.0

ER, error rate of classification; Class 1, Deyou No.5; Class 2, Qinyou No.11; Class 3, Qingza No.5.

3.3. SVM model Full-spectrum data without data reduction was directly used as the input of SVM to build a classification model. The SVM parameters, i.e., c (RBF kernel width) and c (SVM cost factor), were decided based on the minimal classification error through a grid-search method combined with a leave-ten-out cross validation. The search ranges of c and c were set to from 106 to 10 with 15 values spaced uniformly, and from 103 to 100 with 11 values spaced uniformly, respectively. The search result is illustrated in the Fig. 3(a). The optimal parameter combination of c and c was specified by the position of the sign ‘X’, where c and c were 0.0001 and 100, respectively. In the cross validation, only one misclassification occurred for the variety of Qinyou No.11, which corresponded to the overall ER of 1.25%. The SVM model was then built using the optimal parameters of c and c. The classification results of the model are summarized in Table 2. Despite one misclassification in cross validation, zero classification ERs were achieved for each variety in both calibration and prediction sets. Fig. 3(b) shows the predicted class values of all samples in prediction set. Each sample was correctly classified. The result further

confirmed the applicability of FTIR-PAS and also evidenced the superiority of SVM as a classification model over PLS-DA. Besides, it could be concluded that there existed a nonlinear relationship between spectral information and rapeseed varieties, and the SVM model with RBF as a kernel function could well exploit this nonlinearity for the task of varietal classification. 3.4. Models of SPA-PLS-DA and SPA-SVM Despite the low classification errors achieved above, full-spectrum input increased the model complexity and computational cost. Successive projections algorithm (SPA) was thus adopted to select the most relevant spectral variables to develop a simplified model. As a result, 28 variables were selected. Those selected variables are shown in Fig. 4(a). It could be observed that most variables selected were located in characteristic points (peaks, valleys, shoulders, inflections) of rapeseed spectrum. The classification models of SPA-PLS-DA and SPA-SVM were established by using selected variables as the input of PLS-DA and SVM, respectively. The optimal number of LS was five for the SPA-PLS-DA model; and the optimal parameters of c and c were

Fig. 4. (a) Mean PA spectrum of all the rapeseed samples where circles indicated the spectral variables selected by SPA; (b) the predicted classes of each rapeseed sample obtained from the SPA-PLS-DA model (h: Deyou No.5, 4: Qinyou No.11, and s: Qingza No.5), and the zero values of predicted classes indicate the misclassification of samples.

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0.0031623 and 10 for the SPA-SVM model. The classification results of the two models are given in Table 2. For the SPA-PLSDA model, four misclassifications, i.e., three samples of Deyou No.5 and one sample of Qingza No.5, were produced in the calibration set. The corresponding overall ER was 5%, which was the same as that obtained from the PLS-DA model. For the prediction set, two samples of Qingza No.5 were misclassified (Fig. 4(b)), also indicating an overall ER of 5% , lower than 7% achieved by the PLS-DA model. In general, the SPA-PLS-DA model was more preferable compared to the PLS-DA model, due to the lower overall ER in the prediction set and much less modeling variables. For the SPASVM model, no misclassification was observed in both the calibration and prediction sets, which implied the classification accuracy was not affected by SPA variable selection. As could be seen above, the use of SPA had improved the classification models by reducing input variables and lowering, at least not enlarging, the overall classification ERs. Compared to Vis/NIRS, FTIR-PAS in this study demonstrated a comparable discriminating capacity for varietal classification of rapeseeds. Apart from the high predictive accuracy obtained, FTIR-PAS also possessed merits in the rapidity of spectral recording, minimal sample requirement (about 100 mg per test) and no sample preparation. It was important to note that FTIR-PAS were not commonly found in labs due to a high instrumental cost. However, the situation would be likely to change in the near future due to the advances of photoacoustic instrumentation. At present, lowcost and portable PA instruments had been devised and available (Rabasovic et al., 2009; Capitan-Vallvey and Palma, 2011). Besides, more rapeseed varieties and samples with a wide variability across growth seasons, geographical locations and agronomic practices, would be considered in the next study, to further explore FTIRPAS coupled with chemometrics in classification of rapeseed varieties. 4. Conclusion This work presented the first application of FTIR-PAS as a rapid, non-destructive and objective tool for varietal classification of rapeseeds. The use of PCA and the development of classification models of PLS-DA and SVM, were discussed. Perfect classification in calibration and prediction sets was achieved by the SVM model, outperforming the PLS-DA model. Furthermore, the two models were improved by using SPA to downsize modeling variables. FTIR-PAS had been verified as a promising tool for variety classification of rapeseeds. However, further work was essential to explore the discriminating power of FTIR-PAS by collecting more rapeseed varieties of samples. Acknowledgements This work was supported by the National Natural Scientific Foundation of China (41130749). We are genuinely grateful to two anonymous reviewers for their valuable comments on an earlier manuscript. References Anderson, T.J., Ai, Y., Jones, R.W., Houk, R.S., Jane, J., Zhao, Y., Birt, D.F., McClelland, J.F., 2013. Analysis of resistant starches in rat cecal contents using Fourier transform infrared photoacoustic spectroscopy. J. Agric. Food Chem. 61, 1818– 1822. Araujo, M.C.U., Saldanha, T.C.B., Galvao, R.K.H., Yoneyama, T., Chame, H.C., Visani, V., 2001. The successive projections algorithm for variable selection in spectroscopic multicomponent analysis. Chemom. Intell. Lab. Syst. 57, 65–73. Bageshwar, D.V., Pawar, A.S., Khanvilkar, V.V., Kadam, V.J., 2010. Photoacoustic spectroscopy and its applications – a review. Eurasian J. Anal. Chem. 52, 187– 203. Barker, M., Rayens, W., 2003. Partial least squares for discrimination. J. Chemom. 17, 166–173.

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