A Nondestructive Detection Method for the Muti-Quality Attributes of Oats Using Near-Infrared Spectroscopy

文献类型： 外文期刊

第一作者： Li, Linglei

作者： Li, Linglei;Gou, Guoyuan;Jia, Lang;Cao, Ruge;Li, Long;Wang, Lili;Zhang, Yonghu;Shen, Xiaogang

作者机构：

关键词： oat; near-infrared spectroscopy; feature selection; nondestructive; portable near-infrared spectrometer

期刊名称：FOODS （ 影响因子：5.1； 五年影响因子：5.6 ）

ISSN：

年卷期： 2024 年 13 卷 22 期

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收录情况： SCI

摘要： This study aimed to achieve a precise and non-destructive quantification of the amounts of total starch, protein, beta-glucan, and fat in oats using near-infrared technology in conjunction with chemometrics methods. Eight preprocessing methods (SNV, MSC, Nor, DE, FD, SD, BC, SS) were employed to process the original spectra. Subsequently, the optimal PLS model was obtained by integrating feature wavelength selection algorithms (CARS, SPA, UVE, LAR). After SD-SPA, total starch reached its optimal state (Rp2 = 0.768, RMSEP = 2.057). Protein achieved the best result after MSC-CARS (Rp2 = 0.853, RMSEP = 1.142). beta-glucan reached the optimal value after BC-SPA (Rp2 = 0.759, RMSEP = 0.315). Fat achieved the optimal state after SS-SPA (Rp2 = 0.903, RMSEP = 0.692). The research has shown the performance of the portable FT-NIR for a rapid and non-destructive quantification of nutritional components in oats, holding significant importance for quality control and quality assessment within the oat industry.

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