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：Lou, Weijian; Yang, Kai; Zhu, Miaoqin; Wu, Yongjiang; Liu, Xuesong; Jin, Ye^*

来源：Journal of Innovative Optical Health Sciences, 2014, 7(6): 1450011.

DOI：10.1142/S1793545814500114

• Near infrared spectroscopy
• extraction
• particle swarm optimization
• least square support vector machines

A particle swarm optimization (PSO)-based least square support vector machine (LS-SVM) method was investigated for quantitative analysis of extraction solution of Yangxinshi tablet using near infrared (NIR) spectroscopy. The usable spectral region (5400-6200 cm(-1)) was identified, then the first derivative spectra smoothed using a Savitzky-Golay filter were employed to establish calibration models. The PSO algorithm was applied to select the LS-SVM hyper-parameters (including the regularization and kernel parameters). The calibration models of total flavonoids, puerarin, salvianolic acid B and icariin were established using the optimum hyper-parameters of LS-SVM. The 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 SVM. For PSO-based LS-SVM models, the determination coeffcients (R-2) 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 models. The PSO-based LS-SVM algorithm employed in this study exhibited excellent calibration performance and prediction accuracy, which has definite practice significance and application value.

• 出版日期2014-11
• 单位浙江外国语学院; 浙江大学