Non-invasive classification of laver using visible and near-infrared spectroscopy
Visible and near infrared (NIR) spectroscopy was utilized to classify the verities of laver. As there are almost six hundreds of NMR variables which would cause poor classification and long calculation time, uninformative variables should be eliminated. Successive projections algorithm (SPA) was applied to select the effective variables from the full-spectrum (FS). Finally 13 variables were selected, and were inputted into least-square support vector machine (LS-SVM) to do the classification. A better result of 96.55％ correct answer rate of SPA-LS-SVM model was obtained, compared to that of the principal component analysis (PCA)-LS-SVM model. It was proved that SPA is effective algorithm for spectra variable selection. As a conclusion, Vis-NIR spectroscopy is a feasible way to distinguish laver varieties fast and accurately.
laver Successive projections algorithm (SPA) least-square support vector machine (LS-SVM) principal component analysis (PCA)
Xiaojing Chen Meng Xu Qibo Cai Xuming Hu
College of Physics and Electronic Information engineering, Wenzhou University, Wenzhou 325027, China
国际会议
长沙
英文
1958-1961
2010-05-11（万方平台首次上网日期，不代表论文的发表时间）