Combination of NIR spectroscopy and algorithms for rapid differentiation between one-year and two-year stored rice

Shijie Shi; Junheng Feng; Lichao Yang; Junyang Xing; Gaofeng Pan; Jichao Tang; Jing Wang; Juan Liu; Cougui Cao; Yang Jiang

doi:10.1016/j.saa.2023.122343

Combination of NIR spectroscopy and algorithms for rapid differentiation between one-year and two-year stored rice

Spectrochim Acta A Mol Biomol Spectrosc. 2023 Apr 15:291:122343. doi: 10.1016/j.saa.2023.122343. Epub 2023 Jan 13.

Authors

Shijie Shi¹, Junheng Feng², Lichao Yang³, Junyang Xing¹, Gaofeng Pan⁴, Jichao Tang¹, Jing Wang¹, Juan Liu¹, Cougui Cao⁵, Yang Jiang⁶

Affiliations

¹ College of Plant Science & Technology, Huazhong Agricultural University, Wuhan 430070, China.
² Cangzhou Academy of Agriculture and Forestry Sciences, Cangzhou 061001, China.
³ College of Economics and Management, Huazhong Agricultural University, Wuhan 430070, China.
⁴ Xiangyang Academy of Agricultural Sciences, Xiangyang 441022, China.
⁵ College of Plant Science & Technology, Huazhong Agricultural University, Wuhan 430070, China; Shuangshui Shuanglü Institute, Huazhong Agricultural University, Wuhan 430070, China.
⁶ College of Plant Science & Technology, Huazhong Agricultural University, Wuhan 430070, China; Shuangshui Shuanglü Institute, Huazhong Agricultural University, Wuhan 430070, China. Electronic address: jiangyang@mail.hzau.edu.cn.

PMID: 36657285
DOI: 10.1016/j.saa.2023.122343

Abstract

Storage is necessary for rice to ensure the year-round consumption of rice. With the increase in storage time, the taste quality and commercial value of rice gradually decrease. The accurate determination of the freshness of rice is critical to the rice trade. However, it is difficult to distinguish aging rice from fresh rice, so a quick and simple method is needed to identify the freshness of the rice. In this study, a combination of near-infrared spectroscopy (NIR) and various algorithms, such as partial least squares discriminant analysis (PLS-DA), support vector machines (SVM), and classification and regression trees (CART), were used to differentiate the freshness of rice. PLS-DA and SVM demonstrated excellent classification ability in identifying the freshness of rice, with sensitivity and specificity of 1. The original spectra were used with 100% accuracy in the test set to determine the freshness of the rice. As a result, PLS-DA and SVM can be used to determine the freshness of the rice.

Keywords: Classification algorithm; Near-infrared spectroscopy; Rice; Storage time.

MeSH terms

Algorithms
Discriminant Analysis
Least-Squares Analysis
Oryza* / chemistry
Spectroscopy, Near-Infrared* / methods
Support Vector Machine