Application of particle swarm optimization-based least square support vector machine in quantitative analysis of extraction solution of yangxinshi tablet using near infrared spectroscopy

作     者：Weijian Lou Kai Yang Miaoqin Zhu Yongjiang Wu Xuesong Liu Ye Jin 

作者机构：Department of Pharmacy Sir Run Run Shaw Hospital of School of Medicine Zhejiang UniversityHangzhou 310016P.R.China College of Pharmaceutical Sciences Zhejiang UniversityHangzhou 310058P.R.China Department of Chemistry Zhejiang International Studies University Hangzhou 310012P.R.China 

出 版 物：《Journal of Innovative Optical Health Sciences》 (创新光学健康科学杂志（英文）)

年 卷 期：2014年第7卷第6期

页      面：40-48页

核心收录：

学科分类：08[工学] 0812[工学-计算机科学与技术（可授工学、理学学位）] 

主　　题：Near infrared spectroscopy extraction paurticle swarm optimization least square support vector machines 

摘      要：A particle swarm optimization(PSO)-based least square support vector machine(LS-SVM)method was investigated for quantitative analysis of extraction solution of Y angxinshi tablet using near infrared(NIR)*** usable spectral region(5400-6200cm^(-1))was identified,then the first derivative spectra smoothed using a Savitzky-Golay filter were employed to establish calibration *** PSO algorithm was applied to select the LS-SVM hyper-parameters(including the regularization and kernel parametens).The calibration models of total flavonoids,puerarin,salvianolic acid B and icarin were established using the optimumn hyper-parameters of LS *** performance of LS SVM models were compared with partial least squares(PLS)regression,feed forward back propagation network(BPANN)and support vector machine(SVM).Experimental results showed that both the calibration results and prediction accuracy of the PSO-based LS SVM method were superior to PLS,BP-ANN and *** PSO-based LS-SVM models,the determination cofficients(R2)for the calibration set were above 0.9881,and the RSEP values were controlled within 5.772%.For the validation set,the RMSEP values were close to RMSEC and less than 0.042,the RSEP values were under 8.778%,which were much lower than the PLS,BP-ANN and SVM *** PSO-based LS SVM algorithm employed in this study exhibited excellent calibration performance and prediction accuracy,which has definite practice significance and application value.