Classification of rice based on storage time by using near infrared spectroscopy and chemometric methods

文献类型： 外文期刊

第一作者： Miao, XueXue

作者： Miao, XueXue;Tao, ShuHua;Chen, Zuwu;Wang, JieMin;Huang, WeiDong;Yu, YaYing;Miao, Ying;Liu, DengBiao

作者机构：

关键词： Rice; Classification model; Near infrared spectroscopy; Knearest neighbor; Storage; Chemometrics

期刊名称：MICROCHEMICAL JOURNAL （ 影响因子：4.821； 五年影响因子：4.364 ）

ISSN： 0026-265X

年卷期： 2021 年 171 卷

页码：

收录情况： SCI

摘要： Rice is among the most important food crops, and feeds more than half of the world's population. The freshness of rice decreases with storage time, therefore a fast and easy system for determining quality is greatly needed. We have investigated the potential of near-infrared spectroscopy (NIRS), combined with chemometric methods, for distinguishing rice samples of one, two, and three years of storage. A total of 240 rice samples were analyzed in this study. Principal components analysis (PCA) was initially conducted to look for possible clustering. Next, two pattern recognition methods (partial least-squares discriminant analysis (PLS-DA), and nearest neighbor (KNN)) were compared for their usefulness in building classification models. All two scored high for sensitivity and specificity, but a difference was seen for predictive accuracy. PLS-DA achieved a 97% predictive accuracy, whereas the KNN model, built after first derivative spectral pretreatment, scored 100%. Therefore, NIRS coupled with chemometric methods can be considered as an effective method to classify rice from different years of storage. This article provides a new technology for the evaluation of rice freshness in the market.

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