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Analysis and Identification of Rice Adulteration Using Terahertz Spectroscopy and Pattern Recognition Algorithms

文献类型： 外文期刊

作者： Li, Chao 1 ; Li, Bin 1 ; Ye, Dapeng 2 ;

作者机构： 1.Beijing Res Ctr Informat Technol Agr, Beijing 100097, Peoples R China

2.Fujian Agr & Forestry Univ, Coll Mech & Elect Engn, Fuzhou 350002, Peoples R China

关键词： Back propagation neural network; partial least squares-discriminant analysis; rice adulteration; spectral analysis; support vector machines; terahertz spectroscopy

期刊名称：IEEE ACCESS （ 影响因子：3.367； 五年影响因子：3.671 ）

ISSN： 2169-3536

年卷期： 2020 年 8 卷

页码：

收录情况： SCI

摘要： Rice adulteration is a severe problem in agro-products and food regulatory agencies, suppliers, and consumers. In this study, to effectively distinguish whether high-quality rice is mixed with low-quality rice, detection and analysis of adulterated rice in five levels with different mixing proportions was conducted via terahertz spectroscopy and pattern recognition algorithms. Initially, samples were prepared and spectral data were acquired by using the terahertz transmission mode, and a principal component analysis (PCA) algorithm was applied to extract features from original spectrum information and reduce data dimensions. Subsequently, partial least squares-discriminant analysis (PLS-DA), support vector machine (SVM), and a back propagation neural network (BPNN) combined with the absorption spectra after different pretreatments, including standard normal variate (SNV) transformation, baseline correction (BC), and first derivative (1st derivative), were applied to establish the classification models. Results indicate that an SVM model employing the absorption spectra with a 1st derivative pretreatment exhibits the best discrimination ability, with an accuracy up to 97.33% in the prediction set. This result proves that terahertz spectroscopy combined with chemometric methods can be an effective tool to identify rice adulteration levels.

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