光谱学与光谱分析 |
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Selection of Characteristic Wavelengths Using SPA and Qualitative Discrimination of Mildew Degree of Corn Kernels Based on SVM |
YUAN Ying, WANG Wei*, CHU Xuan, XI Ming-jie |
Beijing Key Laboratory of Optimization Design for Modern Agricultural Equipment, College of Engineering, China Agricultural University, Beijing 100083, China |
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Abstract The feasibility of Fourier transform near infrared (FT-NIR) spectroscopy with spectral range between 833 and 2 500 nm to detect the moldy corn kernels with different levels of mildew was verified in this paper. Firstly, to avoid the influence of noise, moving average smoothing was used for spectral data preprocessing after four common pretreatment methods were compared. Then to improve the prediction performance of the model, SPXY (sample set partitioning based on joint x-y distance) was selected and used for sample set partition. Furthermore, in order to reduce the dimensions of the original spectral data, successive projection algorithm (SPA) was adopted and ultimately 7 characteristic wavelengths were extracted, the characteristic wavelengths were 833, 927, 1 208, 1 337, 1 454, 1 861, 2 280 nm. The experimental results showed when the spectrum data of the 7 characteristic wavelengths were taken as the input of SVM, the radial basic function (RBF) used as the kernel function, and kernel parameter C=7 760 469, γ=0.017 003, the classification accuracies of the established SVM model were 97.78% and 93.33% for the training and testing sets respectively. In addition, the independent validation set was selected in the same standard, and used to verify the model. At last, the classification accuracy of 91.11% for the independent validation set was achieved. The result indicated that it is feasible to identify and classify different degree of moldy corn grain kernels using SPA and SVM, and characteristic wavelengths selected by SPA in this paper also lay a foundation for the online NIR detection of mildew corn kernels.
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Received: 2014-08-21
Accepted: 2014-12-05
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Corresponding Authors:
WANG Wei
E-mail: playerwxw@cau.edu.cn
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[1] Pojic′ M M, Mastilovic′ J S. Food and Bioprocess Technology, 2013, 6(2): 330. [2] LIU Xin-ru, ZHANG Li-ping, WANG Jian-fu, et al(刘心如, 张黎平, 王建福, 等). Spectroscopy and Spectral Analysis(光谱学与光谱分析), 2013, 33(8): 2092. [3] LIANG Jian, LIU Bin-mei, TAO Liang-zhi, et al(梁 剑,刘斌美,陶亮之,等). The Journal of Light Scattering(光散射学报), 2013, 25(4): 423. [4] Singh C B, Jayas D S, Paliwal J, et al. International Journal of Food Properties, 2012, 15(1): 11. [5] YAO Hai-bo, Hruska Z, Kincaid R, et al. Biosystems Engineering, 2013, 115: 125. [6] Bregman L M. Akademiia Nauk SSSR, Doklady, 1965, 162: 487. [7] Tallada J G, Wicklow D T, Pearson T C, et al. Transactions of the ASABE, 2011, 54(3): 1151. [8] Galvo R K H, Araujo M C U, José G E, et al. Talanta, 2005, 67(4): 736. [9] CHU Xiao-li(褚小立). Molecular Spectroscopy Analytical Technology Combined with Chemometrics and Its Applications(化学计量学方法与分子光谱分析技术). Beijing: Chemical Industry Press(北京: 化学工业出版社), 2011.
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