A Rapid and Nondestructive Detection Method for Rapeseed Quality Using NIR Hyperspectral Imaging Spectroscopy and Chemometrics

文献类型： 外文期刊

第一作者： Wang, Du

作者： Wang, Du;Li, Xue;Ma, Fei;Yu, Li;Zhang, Wen;Jiang, Jun;Zhang, Liangxiao;Li, Peiwu;Zhang, Liangxiao;Zhang, Liangxiao;Li, Peiwu;Li, Peiwu

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关键词： rapeseed; NIR hyperspectral imaging; quality parameters; kernel partial least square regression

期刊名称：APPLIED SCIENCES-BASEL （ 影响因子：2.7； 五年影响因子：2.9 ）

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年卷期： 2023 年 13 卷 16 期

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收录情况： SCI

摘要： In this study, a fast and non-destructive method was proposed to analyze rapeseed quality parameters with the help of NIR hyperspectral imaging spectroscopy and chemometrics. Hyperspectral images were acquired in the reflectance mode. Meanwhile, the region of interest was extracted from each image by the regional growth algorithm. The kernel partial least square regression was used to build prediction models for crude protein content, oil content, erucic acid content, and glucosinolate content of rapeseed. The results showed that the correlation coefficients were 0.9461, 0.9503, 0.9572, and 0.9335, whereas the root mean square errors of prediction were 0.5514%, 0.5680%, 2.8113%, and 10.3209 mu mol/g for crude protein content, oil content, erucic acid content, and glucosinolate content, respectively. It demonstrated that NIR hyperspectral imaging is a promising tool to determine rapeseed quality parameters in a rapid and non-invasive manner.

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