外文摘要：Ab s tr a c t. Rice is an annual plant from the family of Oryzeae, provides the main food for about 2.5 billion people. The quality of this product is under the influence of various factors. Quality control and adulteration detection are among the main issues in the rice industry for which, various methods have been developed. Some of these methods are costly or with low accuracy. Therefore, this study aimed to investigate and detect adulteration with spectroscopic devices and chemometric methods as well as neural network approach. The results of this study indicated the highest accuracy (100%) in the detection of authentic rice for Fouriertransform infrared combined with C -support vector machine (linear and polynomial functions) and visible-near-infrared device with quadratic discriminant analysis, multivariate discriminant analysis, Bayesian, and Decision Tree. The lowest accuracy was also related to support vector machine method with Sigmoid function for both devices. Principal component analysis method also provided very high accuracy for both devices (accuracy of 100% for visible-near-infrared and 99% for Fourier-transform infrared).
外文关键词：Spectroscopy;Machine learning algorithms;authenticity verification;rice quality control
作者：Zareiforoush, Hemad;Karami, Hamed;Zaresani, Hamed;Sayyah, Amir Hosein Afkari;Khorramifar, Ali;Gancarz, Marek;Tabor, Sylwester
作者单位：Univ Guilan;Univ Mohaghegh Ardabili;Polish Acad Sci;Agr Univ Krakow;Knowledge Univ
期刊名称：INTERNATIONAL AGROPHYSICS
期刊影响因子：0.0
出版年份：2024
出版刊次：38(2)
原文传递申请：江苏省科技资源（工程技术文献）统筹服务平台