深圳大学学报（理工版）2019，Vol.36Issue(2)：207-212,6.DOI:10.3724/SP.J.1249.2019.02207
太赫兹时域光谱结合PCA-LDA鉴别西洋参的研究
Identification of American ginseng by terahertz time domain spectroscopy combined with principal component analysis and linear discriminant analysis
摘要
Abstract
Terahertz time-domain spectroscopy is a new spectroscopic measurement technique that has been widely applied in material detection due to its ability to penetrate most non-conducting and non-polar materials and its intrinsical safe nature.In this work,terahertz time-domain spectroscopy combined with principle component analysis and linear discriminant analysis was applied to establish a non-destructive identification model for American ginseng after extraction and authentic American ginseng.The spectral analysis was based on the terahertz spectra and the absorbance spectra of American ginseng after extraction and authentic American ginseng showed little difference.The leave-one-out approach was used to evaluate the performance of the principle component analysis and linear discriminant analysis model.The result of the analysis suggested that the reliabilities of the top three principal components were more than 98.1％ and the recognition rates of the principle component analysis and linear discriminant analysis model were 100％ and 96.7％ in terms of the American ginseng after extraction and authentic American ginseng,and the total recognition rate was 98.3％.Our work suggests that by combining terahertz time-domain spectroscopy with principle component analysis and linear discriminant analysis,American ginseng after extraction and authentic American ginseng can be accurately distinguished and the identification results are reliable and practicable.关键词
光谱学/太赫兹时域光谱/主成分分析/线性判别分析/西洋参/鉴别Key words
spectroscopy/terahertz time-domain spectroscopy/principal component analysis/linear discriminant analysis/American ginseng/identification分类
数理科学引用本文复制引用
刘陵玉,常天英,张献生,崔洪亮..太赫兹时域光谱结合PCA-LDA鉴别西洋参的研究[J].深圳大学学报（理工版）,2019,36(2):207-212,6.基金项目
National Natural Science Foundation of China (61705120) （61705120）
Key Research and Development Program of Shandong Province (2018GGX101043,2017GGX10108,2017GGX10124)国家自然科学基金资助项目(61705120) （2018GGX101043,2017GGX10108,2017GGX10124）
山东省重点研发计划资助项目(2018GGX101043,2017GGX10108,2017GGX10124) （2018GGX101043,2017GGX10108,2017GGX10124）
