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Proceedings Paper

Variable selection based near infrared spectroscopy quantitative and qualitative analysis on wheat wet gluten
Author(s): Chengxu Lü; Xunpeng Jiang; Xingfan Zhou; Yinqiao Zhang; Naiqian Zhang; Chongfeng Wei; Wenhua Mao
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Paper Abstract

Wet gluten is a useful quality indicator for wheat, and short wave near infrared spectroscopy (NIRS) is a high performance technique with the advantage of economic rapid and nondestructive test. To study the feasibility of short wave NIRS analyzing wet gluten directly from wheat seed, 54 representative wheat seed samples were collected and scanned by spectrometer. 8 spectral pretreatment method and genetic algorithm (GA) variable selection method were used to optimize analysis. Both quantitative and qualitative model of wet gluten were built by partial least squares regression and discriminate analysis. For quantitative analysis, normalization is the optimized pretreatment method, 17 wet gluten sensitive variables are selected by GA, and GA model performs a better result than that of all variable model, with R2V=0.88, and RMSEV=1.47. For qualitative analysis, automatic weighted least squares baseline is the optimized pretreatment method, all variable models perform better results than those of GA models. The correct classification rates of 3 class of <24%, 24-30%, >30% wet gluten content are 95.45, 84.52, and 90.00%, respectively. The short wave NIRS technique shows potential for both quantitative and qualitative analysis of wet gluten for wheat seed.

Paper Details

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Proc. SPIE 10461, AOPC 2017: Optical Spectroscopy and Imaging, ; doi: 10.1117/12.2281441
Show Author Affiliations
Chengxu Lü, Chinese Academy of Agricultural Mechanization Sciences (China)
Xunpeng Jiang, COFCO Feed Co., Ltd. (China)
Xingfan Zhou, Beijing Municipal Institute of Labour Protection (China)
Yinqiao Zhang, Chinese Academy of Agricultural Mechanization Sciences (China)
Naiqian Zhang, Kansas State Univ. (United States)
Chongfeng Wei, Chinese Academy of Agricultural Mechanization Sciences (China)
Wenhua Mao, Chinese Academy of Agricultural Mechanization Sciences (China)


Published in SPIE Proceedings Vol. 10461:
AOPC 2017: Optical Spectroscopy and Imaging

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