CAJ | 학술논문

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（per-oxidase ，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 ，Rc ）为0.916，预测集相关系数 Rp 为0.786；PLS模型对POD含量预测效果最佳，Rc 为0.984，Rp 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 Rc ＝0.928， Rp ＝0.790；POD 含量预测的相关系数 Rc ＝0.965， Rp ＝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。
채용가견/근홍외광보대병지초미협박하대맥협편과양화경매（catalase ，CAT ）여과양화물매（per-oxidase ，POD）함량예측진행연구。대500～900 nm광보채용이동평균법（moving average ，MA）11점평활방법진행예처리。채용몽특잡라-편최소이승법（monte carlo-partial least squares ，MCPLS）방법분별대우CAT여POD적함량예측척제7개여8개이상양본。기우전부광보건립료CAT여POD함량예측적PLS ，최소이승지지향량궤（least-squares support vector machine ，LS-SVM ）여겁한학습궤（extreme learning machine ， ELM ）모형，ELM모형대CAT함량예측효과최호，건모집상관계수（correlation coefficient of calibration ，Rc ）위0.916，예측집상관계수 Rp 위0.786；PLS모형대POD함량예측효과최가，Rc 위0.984，Rp 위0.876。채용련속투영산법（successive projections algorithm ，SPA）산법분별위CAT여POD예측선택료8개여19개특정파장，기우특정파장건립적PLS ，LS-SVM여ELM모형중，ELM모형대CAT여POD함량예측효과균최가，CAT함량예측적상관계수위 Rc ＝0.928， Rp ＝0.790；POD 함량예측적상관계수 Rc ＝0.965， Rp ＝0.941。기우전보여기우특정파장적회귀분석모형예측효과상당，차대POD함량적예측효과우우대CAT함량적예측효과，이저수요진일보연구이득도정도화은정성경고적예측모형。연구결과표명，채용가견/근홍외광보결합화학계량학방법가이실현대제초제협박하대맥협편CAT여POD함량적예측。
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 (Rc ) of 0.916 and correlation co-efficient of prediction (Rp ) of 0.786 .PLS model obtained best prediction results for POD ,with Rc of 0.984 and Rp 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 Rc of 0.928 and Rp of 0.790 for CAT and Rc of 0.965 and Rp 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-in-frared spectroscopy for CAT and POD content determination in barley leaves under the stress of ZJ 0273 .