Two Modeling Methods for Near Infrared Spectroscopy Nondestructive Quantitative Analysis of the Polysaccharide Contents in Cordyceps Gunnii Mycelia Powder

This paper presents a comparative study between Partial Least Squares (PLS) method and support vector regression (SVR) in modeling the relationship between the near infrared spectra (NIRS) and the polysaccharide contents in Cordyceps gunnii mycelia powder samples. Both of the models were optimized by selecting the suitable spectra preprocessing methods and the best modeling parameters. And then the optimum models were obtained. The results demonstrated that the SVR model was superior to PLS model. The root mean square error of cross-validation (RMSECV), the coefficient relation between actual values and predictive values obtained by cross-validation (Rv) and root mean square error of prediction set (RMSEP) of the optimum SVR model were 4.1157, 0.9846 and 3.7871, which indicated that the stability, the fit and the predictive capability of the model were satisfied.