基于近红外光谱技术的油菜叶片丙二醛含量快速检测方法研究

Fast Determination of Malondialdehyde in Oilseed Rape Leaves Using Near Infrared Spectroscopy

应用近红外光谱技术实现了油菜叶片中丙二醛(MDA)含量的快速无损检测。对90个油菜叶片样本进行光谱扫描,用60个样本建模,30个样本验证。经过平滑、变量标准化、一阶及二阶求导、去趋势等预处理后,建立了MDA预测的偏最小二乘法(PLS)模型。将PLS提取的有效特征变量(LV)和连续投影算法(SPA)提取的有效波长作为最小二乘-支持向量机(LS-SVM)的输入变量,分别建立了LV-LS-SVM和SPA-LS-SVM模型。以预测集的预测相关系数(r),预测标准偏差(RMSEP)作为模型评价指标。结果表明,油菜叶片中MDA含量预测的最优模型为LV-LS-SVM模型,LV-LS-SVM在去趋势处理后的预测效果为r=0.999 9,RMSEP=0.530 2;在二阶求导处理后的预测效果为r=0.999 9,RMSEP=0.395 7。说明应用光谱技术检测油菜叶片中MDA的含量是可行的,并能获得满意的预测精度,为油菜大田生长状况的动态连续监测提供了新的方法。

Near infrared（NIR） spectroscopy was applied for the fast and nondestructive determination of malondialdehyde（MDA） content in oilseed rape leaves.A total of 90 leaf samples were collected,the calibration set was composed of 60 samples,and the prediction set was composed of 30 samples.Different preprocessing methods were used before the calibration stage,including smoothing,standard normal variate,first and second derivative,and detrending.Then partial least squares（PLS） models were developed for the prediction of MDA content in oilseed rape leaves.The latent variables selected by PLS and effective wavelengths selected by successive projections algorithm（SPA） were used as the inputs of least square-support vector machine（LS-SVM） to develop LV-LS-SVM and SPA-LS-SVM models.The correlation coefficients（r） and root mean square error of prediction（RMSEP） were used as the model evaluation indices.Excellent results were achieved by LV-LS-SVM model,and the prediction results by LS-SVM model using detrending spectra were r=0.999 9 and RMSEP=0.530 2,and those by LS-SVM model using 2-Der spectra were r=0.999 9 and RMSEP=0.395 7.The results showed that NIR spectroscopy could be used for determination of MDA content in oilseed rape leaves,and an excellent prediction precision was achieved.This study supplied a new approach to the dynamic and continuous field monitoring of growing status of oilseed rape.