Application of Near Infrared Spectroscopy Combined with Partial Least Squares in Quantitative Analysis of Polysaccharide in Irpex Lacteus Fr. Mycelia

Abstract

Near infrared spectroscopy (NIRS) combined with partial least squares (PLS) was applied to establish model for quantitative analysis of the Polysaccharide in Irpex lacteus Fr. mycelia. Savitzky-Golay smoothing, First derivative, second derivative, fast Fourier transform (FFT) and standard normal variate (SNV) transformation methods were applied to preprocess NIRS. The efficacious spectral regions and the models’ parameters were chosen by leave-one-out cross-validation method. The root mean square error of cross-validation (RMSECV) of the optimum PLS models was 4.962. Using this model for predicting the polysaccharide contents in prediction set, the root mean square error of prediction set (RMSEP) was 0.450. The coefficient correlation of actual values and predictive values obtained by cross-validation (Rv) was 0.872. It was feasible to apply NIR combined with PLS to non-destructive quantitative analysis of the Polysaccharide in Irpex lacteus Fr. Mycelia.