Application of genetic algorithm-support vector regression (GA-SVR) for quantitative analysis of herbal medicines

In this paper, a genetic algorithm-support vector regression (GA-SVR) coupled approach was proposed for investigating the relationship between fingerprints and properties of herbal medicines. GA was used to select variables so as to improve the predictive ability of the models. Two other widely used approaches, Random Forests (RF) and partial least squares regression (PLSR) combined with GA (namely GA-RF and GA-PLSR, respectively), were also employed and compared with the GA-SVR method. The models were evaluated in terms of the correlation coefficient between the measured and predicted values (Rp), root mean square error of prediction, and root mean square error of leave-one-out cross-validation. The performance has been tested on a simulated system, a chromatographic data set, and a near-infrared spectroscopic data set. The obtained results indicate that the GA-SVR model provides a more accurate answer, with higher Rp and lower root mean square error. The proposed method is suitable for the quantitative analysis and quality control of herbal medicines. Copyright (c) 2012 John Wiley & Sons, Ltd.