title: [Analysis of transgenic and non-transgenic rice leaves using visible/near-infrared spectroscopy].
abstract: Visible/near-infrared (Vis/NIR) spectroscopy was investigated for the fast discrimination of rice leaves with different genes and the determination of chlorophyll content. Least squares-support vector machines (LS-SVM) was employed to discriminate transgenic rice leaves from non-transgenic ones. The classification accuracy of calibration samples reached to 100%. Successive projections algorithm (SPA) was proposed to select effective wavelengths. SPA-LS-SVM discrimination model was performed, and the result indicated that an 87.27% recognition ratio was achieved using only 0.3% of total variables. The optimal performance of each quantification model was achieved after orthogonal signal correction (OSA). Performances treated by SPA were better than that of full-spectrum PLS, which indicated that SPA is a powerful way for effective wavelength selection. The best performance of quantification was obtained by SPA-LS-SVM model; with correlation coefficient (R) and root mean square error of prediction (RMSEP) being 0.902 2 and 1.312 1, respectively. Excellent classification and prediction precision were achieved. The overall results indicated that the new proposed SPA-LS-SVM is a powerful method for varieties recognition and SPAD prediction. This study supplied a new and alternative approach to the further application of Vis/NIR spectroscopy in on-field classification and monitoring.
authors.affiliation: College of Biosystems Engineering and Food Science, Zhejiang University, Hangzhou 310058, China. jintianhaokuailea@163.com
authors.forename: Wen-chao
authors.initials: WC
authors.lastname: Zhu
authors.forename: Fang
authors.initials: F
authors.lastname: Cheng
mesh_terms: D000465:Algorithms; D002138:Calibration; D016018:Least-Squares Analysis; D008962:Models, Theoretical; D012275:Oryza; D018515:Plant Leaves; D030821:Plants, Genetically Modified; D019265:Spectroscopy, Near-Infrared; D060388:Support Vector Machine