转基因水稻及其亲本叶片的可见/近红外光谱分析

应用可见/近红外光谱技术实现了转基因水稻叶片的快速识别和叶绿素含量(SPAD)的快速检测。建立偏最小二乘-支持向量机(LS-SVM)鉴别模型,校正集的正确率为100%,同时应用连续投影算法(SPA)提取有效波长,建立SPA-LS-SVM鉴别模型,只用了全变量的0.3%进行建模,其预测集的正确率达到87.27%。在定量分析中,各模型的最优结果均来自经过正交信号校正(OSC)的光谱数据,经过SPA处理后的模型均优于最优的全波段PLS模型,说明SPA是一种有效的波长选择方法。最优SPAD值预测模型为SPA-LS-SVM,其相关系数(r)和预测均方根误差(RMSEP)分别为0.902 2和1.312 1,获得了满意的结果。这说明提出的SPA-LS-SVM方法能快速识别转基因水稻叶片并对SPAD值进行准确预测,为实现大田活体鉴别与连续监测提供了新方法。

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

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.