Rapid and nondestructive quantification of deoxynivalenol in individual wheat kernels using near-infrared hyperspectral imaging and chemometrics

文献类型： 外文期刊

第一作者： Shen, Guanghui

作者： Shen, Guanghui;Yin, Xianchao;Dong, Fei;Xu, Jianhong;Shi, Jianrong;Cao, Yaoyao;Xu, Jianhong;Shi, Jianrong;Lee, Yin-Won

作者机构：

关键词： Nondestructive detection; Hyperspectral imaging; Deoxynivalenol; Near-infrared spectroscopy; Wheat kernels

期刊名称：FOOD CONTROL （ 影响因子：6.652； 五年影响因子：6.498 ）

ISSN： 0956-7135

年卷期： 2022 年 131 卷

页码：

收录情况： SCI

摘要： The present study aimed to evaluate the feasibility of using near-infrared hyperspectral imaging (NIR-HSI) and chemometrics for quantifying deoxynivalenol (DON) in individual wheat kernels. In total, 120 wheat kernels of severely damaged kernels, moderately damaged kernels and asymptomatic kernels (SDKs, MDKs and AKs, respectively) were collected, and the DON content in the individual wheat kernels was analyzed by HPLC-MS/ MS. Partial least squares (PLS), support vector machine (SVM) and local PLS based on global PLS scores (LPLS-S) algorithms were employed for building quantification models of DON. The results showed that SDKs and MDKs might contain low or no DON, while AKs could have a high DON content. Comparing the three modeling strategies, LPLS-S using mixed spectra achieved the best performance for kernels with RMSEP of 40.25 mg/kg and RPD of 2.24, which confirmed that NIR-HSI could be a feasible method for monitoring DON in individual kernels and removing highly contaminated kernels prior to food chain entry.

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