Analysis of transgenic and non-transgenic rice leaves using visible/near-infrared spectroscopy

• Published in: Guang pu xue yu guang pu fen xi Vol. 32; no. 2; p. 370
• Main Authors: Zhu, Wen-chao, Cheng, Fang
• Format: Journal Article
• Language: Chinese
• Published: China 01.02.2012
• Subjects: Algorithms; Calibration; Least-Squares Analysis; Models, Theoretical; Oryza; Plant Leaves; Plants, Genetically Modified; Spectroscopy, Near-Infrared; Support Vector Machine
• ISSN: 1000-0593
• DOI: 10.3964/j.issn.1000-0593(2012)02-0370-04

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