采用可见/近红外光谱检测大麦叶片过氧化氢酶与过氧化物酶含量的研究

采用可见/近红外光谱对丙酯草醚胁迫下大麦叶片过氧化氢酶(catalase, CAT)与过氧化物酶(peroxidase, POD)含量预测进行研究。对500～900 nm光谱采用移动平均法(moving average, MA)11点平滑方法进行预处理。采用蒙特卡罗-偏最小二乘法(monte carlo-partial least squares, MCPLS)方法分别对于CAT与POD的含量预测剔除7个与8个异常样本。基于全部光谱建立了CAT与POD含量预测的PLS，最小二乘支持向量机(least-squares support vector machine, LS-SVM)与极限学习机(extreme learning machine, ELM)模型，ELM模型对CAT含量预测效果最好,建模集相关系数(correlation coefficient of calibration, R_c)为0.916，预测集相关系数R_p为0.786;PLS模型对POD含量预测效果最佳，R_c为0.984，R_p为0.876。采用连续投影算法(successive projections algorithm, SPA)算法分别为CAT与POD预测选择了8个与19个特征波长，基于特征波长建立的PLS，LS-SVM与ELM模型中，ELM模型对CAT与POD含量预测效果均最佳，CAT含量预测的相关系数为R_c=0.928，R_p=0.790;POD含量预测的相关系数R_c=0.965，R_p=0.941。基于全谱与基于特征波长的回归分析模型预测效果相当，且对POD含量的预测效果优于对CAT含量的预测效果，而这需要进一步研究以得到精度和稳定性更高的预测模型。研究结果表明，采用可见/近红外光谱结合化学计量学方法可以实现对除草剂胁迫下大麦叶片CAT与POD含量的预测。

Application of Visible/Near-Infrared Spectroscopy to the Determination of Catalase and Peroxidase Content in Barley Leaves

Visible/near-infrared spectroscopy was applied to determine the content of catalase (CAT) and peroxidase (POD) in barley leaves under the herbicide stress of propyl 4-(2-(4, 6-dimethoxypyrimidin-2-yloxy) benzylamino) benzoate (ZJ0273). The spectral data of the barley leaves in the range of 500~900 nm were preprocessed by moving average with 11 points. Seven outlier samples for CAT and 8 outlier samples for POD were detected and removed by Monte Carlo-partial least squares (MCPLS). PLS, least-squares support vector machine (LS-SVM) and extreme learning machine (ELM) models were built for both CAT and POD. ELM model obtained best results for CAT, with correlation coefficient of calibration (R_c) of 0.916 and correlation coefficient of prediction (R_p) of 0.786. PLS model obtained best prediction results for POD, with R_c of 0.984 and R_p of 0.876. Successive projections algorithm (SPA) was applied to select 8 and 19 effective wavelengths for CAT and POD, respectively. PLS, LS-SVM and ELM models were built using the selected effective wavelengths of CAT and POD. ELM model performed best for CAT and POD prediction, with R_c of 0.928 and R_p of 0.790 for CAT and R_c of 0.965 and R_p of 0.941 for POD. The prediction results using the full spectral data and the effective wavelengths were quite close, and the prediction performance for POD was much better than the prediction performance for CAT, and the studies should be further explored to build more precise and more robust models for CAT and POD determination. The overall results indicated that it was feasible to use visible/near-infrared spectroscopy for CAT and POD content determination in barley leaves under the stress of ZJ0273.