Application of Particle Swarm Optimization Based Least Square Support Vector Machine in Quantitative Analysis of Extraction Solution of Safflower Using Near-infrared Spectroscopy

A novel particle swarm optimization(PSO) based least squares support vector machine(LS-SVM) method was investigated for quantitative analysis of extraction solution of safflower using near-infrared(NIR) spectroscopy. The usable spectral region(5400-6500 cm(-1)) was identified, spectral preprocessing of Norris derivative smoothing was employed, and spectral dimension was also reduced through principal component analysis(PCA). In this paper, the PSO algorithm was applied to select the LS-SVM hyper-parameters(including the regularization and kernel parameters). The calibration models of total solid content and hydroxysafflor yellow A(HSYA) were established using the optimum hyper-parameters of LS-SVM. The performance of LS-SVM models was compared with partial least squares regression(PLSR) and back-propagation artificial neural networks(BP-ANN). The feasibility of these three methods was examined on the unknown sample set. Experimental results showed that the calibration results of BP-ANN were superior to PSO-LS-SVM and PLSR, however, the prediction accuracy of validation and unknown sample set was inferior. For PSO-LS-SVM and PLSR models, the correlation coefficients of the calibration and validation set were above 0.987, the RMSEC and RMSEP values were close to each other and less than 0.074, residual predictive deviation(RPD) values were all above 6.26, and the RSEP values were controlled within 5.70%. For the unknown sample set, the RPD values of PSO-LS-SVM models were above 8.06, the RMSEP and relative standard errors of prediction(RSEP) values were less than 0.07 and 5.84% respectively, which were much lower than BP-ANN and PLSR models. The PSO-LS-SVM algorithm employed in this paper exhibited excellent model robustness and prediction accuracy, which has definite practice significance and application value.