基于高光谱和化学计量学方法的除草剂残留分类识别研究

本研究基于高光谱技术和化学计量学方法,对薄荷叶上的异丙甲草胺、烟嘧(莠去津、敌草胺和砜嘧(精喹4类除草剂残留进行种类判别.高光谱成像光谱范围为450nm～950nm的可见-近红外区域.为降低噪音对光谱数据的干扰、提升判别精度,采用SG平滑和多元散射校正对高光谱曲线进行处理.利用主成分分析算法(PCA)对原始数据进行降维...

Identification of herbicide residues based on hyperspectral imaging and chemometrics

In this study,a hyperspectral method was established to analyze the herbicide residues of Metolachlor,Nicosulfuron&Atrazine,Napropamide and Rimsulfuron&Quizalofop-P on mint leaves.The spectra ranged from 450nm to 950nm.In order to reduce the interference of noise on spectral data and improve the discrimination accuracy,the hyperspectral curves were processed by SG smoothing and multiple scattering correction..The dimension of original data were reduced by principal component analysis(PCA),a support vector machine(SVM)model with radial basis function(RBF)as kernel function was established.The grid search(GS),particle swarm optimization(PSO)and gray wolf optimizer(GWO)were used to optimize the SVM models parameters,and the discrimination accuracy of different models were compared.The precision,recall rate,Youden’s index,ROC and AUC were used to evaluate the models discrimination and generalization ability.The experimental results show that the accuracy of SG-PSO-SVM,SG-PCA-GWO-SVM and MSC-GS-SVM is 100%.SG-PCA-GWO-SVM has the shortest computation time,while MSC-GS-SVM has the best generalization ability.