Authenticity Detection of Black Rice by Near-Infrared Spectroscopy and Support Vector Data Description

被引:26
|
作者
Chen, Hui [1 ,2 ]
Tan, Chao [1 ]
Lin, Zan [1 ,3 ]
机构
[1] Yibin Univ, Key Lab Proc Anal & Control Sichuan Univ, Yibin 644000, Sichuan, Peoples R China
[2] Yibin Univ, Yibin 644000, Sichuan, Peoples R China
[3] Chongqing Med Univ, Affiliated Hosp 1, Chongqing 400016, Peoples R China
基金
中国国家自然科学基金;
关键词
MILK;
D O I
10.1155/2018/8032831
中图分类号
O65 [分析化学];
学科分类号
070302 ; 081704 ;
摘要
Black rice is an important rice species in Southeast Asia. It is a common phenomenon to pass low-priced black rice off as high-priced ones for economic benefit, especially in some remote towns. There is increasing need for the development of fast, easy-to-use, and low-cost analytical methods for authenticity detection. The feasibility to utilize near-infrared (NIR) spectroscopy and support vector data description (SVDD) for such a goal is explored. Principal component analysis (PCA) is used for exploratory analysis and feature extraction. Another two data description methods, i.e., k-nearest neighbor data description (KNNDD) and GAUSS method, are used as the reference. A total of 142 samples from three brands were collected for spectral analysis. Each time, the samples of a brand serve as the target class whereas other samples serve as the outlier class. Based on both the first two principal components (PCs) and original variables, three types of data descriptions were constructed. On average, the optimized SVDD model achieves acceptable performance, i.e., a specificity of 100% and a sensitivity of 94.2% on the independent test set with tight boundary. It indicates that SVDD combined with NIR is feasible and effective for authenticity detection of black rice.
引用
收藏
页数:8
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