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College of Geosciences and Surveying Engineering, China University of Mining and Technology, Beijing 100083, China |
QIAO Xiao-jun, JIANG Jin-bao*, LI Hui, QI Xiao-tong, YUAN De-shuai |
College of Geosciences and Surveying Engineering, China University of Mining and Technology, Beijing 100083, China |
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Abstract Moldy peanuts are likely to contain a strong carcinogen-aflatoxin. Identifying and separating the moldy peanuts quickly can prevent aflatoxin entering the food chain at the source, and reduce the risk of human ingesting aflatoxin. The study is to determine spectral features or index models to identify moldy peanuts efficiently by spectral analysis in Visible and Near-Infrared (VIR) hyperspectral images. Totally 253 moldy peanuts samples and 247 healthy samples were selected to obtain hyperspectral images, and a mean spectrum was calculated from each peanut kernel to represent the moldy or healthy sample. The continuous continuum removal was carried out on the spectra pixel-by-pixel. The modified first-order differential with different step-length was conducted, and the index of Area500~650 was calculated among dominantly separable spectral region of 500~650 nm. Then, the continuous Wavelet transform was applied to extract the spectral information of shapes and locations. Also, the index of Indexcwt was proposed to extract mold information. Results showed that the J-M distance was 1.95 in Area500-650 and 1.99 in Indexcwt, which indicates that the performance of both Area500~650 and Indexcwt is good enough to separate the moldy peanuts from the healthy.
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Received: 2016-08-11
Accepted: 2017-02-10
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Corresponding Authors:
JIANG Jin-bao
E-mail: jjb@cumtb.edu.cn
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[1] Qiao X, Jiang J, Qi X, et al. Food Chemistry, 2016, 220: 393.
[2] Wu F, Bhatnagar D, Bui-klimke T, et al. World Mycotoxin Journal, 2011, 4(1): 79.
[3] Wu F, Guclu H. PloS One, 2012, 7(9): e45151.
[4] Wu F, Stacy S L, Kensler T W. Toxicological Sciences, 2013,135(1):251.
[5] Wang W, Heitschmidt G W, Ni X, et al. Food Control, 2014 42:78.
[6] Wu D, Sun D W. Innovative Food Science & Emerging Technologies, 2013,19A:15.
[7] Robles-Kelly Antonio, Huynh Cong Phuoc. Imaging Spectroscopy for Scene Analysis. Springer,2013.
[8] Burns Donald A, Ciurczak Emil W. Handbook of Near-Infrared Analysis, 3rd ed. CRC Press,2007.
[9] Zare A, Ho K C. IEEE Signal Processing Magazine, 2014,31(1):95.
[10] ZHANG Bing(张 兵). Hyperspectral Image Classification and Target Detection(高光谱图像分类与目标探测). Beijing: Science Press(北京: 科学出版社), 2011.
[11] TONG Qing-xi, ZHANG Bing, ZHENG Lan-fen(童庆禧, 张 兵, 郑兰芬). Hyperspectral Remote Sensing: Principle, Technology and Applications(高光谱遥感-原理技术与应用). Beijing: Higher Education Press(北京: 高等教育出版社), 2006.
[12] JIANG Jin-bao, QIAO Xiao-jun, HE Ru-yan, et al(蒋金豹,乔小军,何汝艳,等). Spectroscopy and Spectral Analysis(光谱学与光谱分析),2016,36(2):379.
[13] Wu D, Sun D W. Innovative Food Science & Emerging Technologies, 2013,19b:1.
[14] Jiang J, Qiao X, He R. Journal of Food Engineering, 2016,169:284.
[15] Jiang J, Steven M D, H R, et al. International Journal of Greenhouse Gas Control, 2015, 37: 1.
[16] PU Rui-liang, GONG Peng(浦瑞良,宫 鹏). Hyperspectral Remote Sensing and Its Applications(高光谱遥感及其应用). Beijing: Higher Education Press(北京: 高等教育出版社), 2000.
[17] Burrus C S, Gopinath R A, Guo H. Introduction to Wavelets and Wavelet Transforms: a Primer,Upper Saddle River, NJ(USA): Prentice Hall, 1998.
[18] Cheng T, Rivard B, Sánchez-Azofeifa G A, et al. Remote Sensing of Environment, 2010,114(4):899.
[19] Adam E, Mutanga O. ISPRS Journal of Photogrammetry and Remote Sensing, 2009,64(6):612.
[20] Dian Yuanyong, Fang Shenghui, Le Yuan. J Indian Soc. Remote Sens., 2014,42(1): 61.
[21] JIANG Jin-bao, Michael D S, HE Ru-yan, et al(蒋金豹, Michael D S, 何汝艳, 等). Transactions of the Chinese Society of Agricultural Engineering(农业工程学报), 2013, 29(12): 163. |
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