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Discrimination of Wheat Scab Infection Level by Fourier Mid-Infrared Technology Combined with Sparse Representation Based Classification Method |
LIANG Kun1, 2, ZHANG Xia-xia1, 2, DING Jing1, 2, XU Jian-hong3, HAN Dong-shen1, 2, SHEN Ming-xia1, 2* |
1. College of Engineering, Nanjing Agricultural University, Nanjing 210031, China
2. Jiangsu Province Engineering Lab for Modern Facility Agriculture Technology and Equipment, Nanjing 210031, China
3. Jiangsu Key Laboratory for Food Quality and Safety-State Key Laboratory Cultivation Base Ministry of Science and Technology/Jiangsu Academy of Agricultural Sciences, Nanjing 210014, China |
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Abstract This paper aims to explore the response of Fourier transform mid-infrared (FT-MIR) spectra to the changes of the main components in wheat scab with infected different grades and to realize a non-destructive detection of grades of wheat scab based on FT-MIR spectroscopy combined with Sparse Representation based Classification algorithms. The FT-MIR spectra of 95 wheat samples infected with different grades of wheat scab samples were collected in 4 000~400 cm-1. The sensitive wavelengths in the FT-MIR spectra of wheat samples were selected by X-loading Weights and Random Forest algorithms, and Sparse Representation based Classification algorithms were used to build models to predict grades of wheat scab. The results showed that the characteristic wavelengths selected by XLW algorithm and RF algorithm achieved an accuracy of more than 90% for each qualitative analysis model, thus, the characteristic wavelength extraction algorithms could effectively simplify the model and improve efficiency. RF-SRC model had the best results, because the accuracy of the modeling set was 97% and the accuracy of the test data set was 96%. Being infected different grade wheat scab could cause the change of the content of water, starch, cellulose, soluble nitrogen , protein and fat in wheat samples. The characteristic wavelength selected by the RF algorithm could reflect the difference of the spectral characteristics of the FT-MIR spectra of these materials, so the grades discrimination of wheat scab by the RF-SRC model can achieve the best effect. Therefore, it is feasible to distinguish the grades of FHB in Wheat by using FT-MIR spectroscopy and pattern recognition method. This paper explained the mechanism of measuring the grades of FHB in Wheat by FT-MIR.
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Received: 2018-08-18
Accepted: 2018-12-27
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Corresponding Authors:
SHEN Ming-xia
E-mail: mingxia@njau.edu.com
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[1] Dahl B, Wilson W W. Agricultural Systems, 2018, 162: 145.
[2] Jaillais B, Roumet P, Pinson-Gadais L. Food Control, 2015, 54: 250.
[3] Dweba C C, Figlan S, Shimelis H A. Crop Protection, 2017, 91(2017): 114.
[4] Barbedo J G A, Tibola C S, Fernandes J M C. Biosystems Engineering, 2015, 131: 65.
[5] DU Ying-ying, CHEN Xiao-he, LIANG Kun, et al(杜莹莹, 陈小河, 梁 琨, 等). Science and Technology of Food Industry (食品工业科技), 2016, 37(17): 54.
[6] CHEN Shu-xi, XIE Chuan-qi, WANG Qiao-nan, et al(程术希, 谢传奇, 王巧男, 等). Spectroscopy and Spectral Analysis (光谱学与光谱分析), 2014, 34(5): 1362.
[7] LIANG Kun, DU Ying-ying, LU Wei, et al(梁 琨, 杜莹莹, 卢 伟, 等). Transactions of the Chinese Society for Agricultural Machinery(农业机械学报), 2016, 47(2): 309.
[8] Yu S, Sheng L, Zhang C, et al. Spectrochimica Acta Part A Molecular & Biomolecular Spectroscopy, 2018, 198: 88.
[9] Shi H, Yu P. Food Control, 2017, 82.
[10] Suchowilska E, Kandler W, Wiwart M, et al. International Agrophysics, 2012, 26(2): 207.
[11] Williams P J, Geladi P, Britz T J, et al. Journal of Cereal Science, 2012, 55: 272.
[12] Mahesh S, Jayas D S, Paliwal J et al. Sensing and Instrumentation for Food Quality and Safety, 2011, 5: 1.
[13] Wang Y, Gao Y, Yu X, et al. Food Analytical Methods, 2016, 9(1): 131.
[14] Mu K X, Feng Y Z, Chen W, et al. Chemometrics and Intelligent Laboratory Systems, 2018, 179: 46.
[15] Zhang S, Wu X, You Z, et al. Computers and Electronics in Agriculture, 2017, 134: 135.
[16] HE Chun-xia, FU Lei-ming, XIONG Jing,et al(何春霞, 傅雷鸣, 熊 静, 等). Journal of Nanjing Agricultural University(南京农业大学学报), 2016, 39(2): 325.
[17] Amir R M, Anjum F M, Khan M I, et al. Journal of Food Science & Technology, 2013, 50(5): 1018.
[18] Singh V K, Devi A, Pathania S, et al. Biocatalysis and Agricultural Biotechnology, 2017 (9): 58. |
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