Research In The Application Of Near Infrared Reflected Spectroscopy To Quantitative Analysis Of Active Components In Trametes Versiolor (L.Fr.) Pilat

With the rapid development of computation technique and chemometrics, the analytical problems which are aroused by the poor effected information in the near infrared spectroscopy (NIRS), difficulties for regression because of overlapped and conlinear spectra have been solved. Near infrared spectroscopy has become one of the fast (rapid) developments and most mentioned analytical technologies in this century. In this paper, the application of partial least squares regression (PLSR) method for the relationship between the NIR spectra and the protein and polysaccharides in the Coriolus versciclor mycelium samples has been studied. The results demonstrated that this method was good at extracting the effect information from the NIR spectra and parsed the problem of overlapped and conlinear spectra. With its advantages of fast analyzing, simple sample preparation, various analysis from a single multi-spectral, non-destructibility and no pollution, Near Infrared Spectroscopy has been widely used in various areas of quantitative analysis and process monitoring, was described as a kind of green, fast, efficient and suitable for on-line analysis of the test means.Applied data pretreatment approaches including Savitzky-Golay smoothing, first derivative and second derivative to dispose the NIR spectra of Coriolus versciclor mycelium samples, and introduced a novel method using the RSD of the spectra and the correlation coefficient between the absorbance at every wavelength and the protein and polysaccharides in the Coriolus versciclor mycelium samples to choose the optimal wavelength regions. The original spectra and every pretreated spectrum at each spectra region were applied to develop the quantitative analysis models for determination the the protein and polysaccharides of the Coriolus versciclor mycelium samples respectively. Suitable numbers of the factor for he developed models were selected depended on the root mean squares error of calibration set by cross-validation method (RMSECV) and Predicted Residual Error Sum of Squares (PRESS), and then, the optimum models were selected according as RMSECV, root mean squares error of calibration set (RMSEC), the correlation coefficient between the predicted values by cross-validation and actual values (Rv), the correlation coefficient between the predicted values and actual values (Rc)and the root mean square error of predictive set (RMSEP). The optimum model that for determining the polysaccharides of the Coriolus versciclor mycelium samples applied the Savitzky-Golay smoothing spectra with the region of 800~2500 nm, its suitable number of factor were 5, Rvwere 0.92527, RMSECV were 0.01541, Rc were 0.95608, RMSEC were 0.01220, RMSEP were 0.01220. The optimum model that for determining the protein of the Coriolus versciclor mycelium samples applied the Savitzky-Golay smoothing spectra with the region of 1815~2500 nm, its suitable number of factor were 3, Rvwere 0.95910, RMSECV were 0.01085, Rc were 0.96738, RMSEC were 0.00945, RMSEP were0.00453. In summary, Coriolus versicolor mycelium powder sample near-infrared spectroscopy pretreatment method has been studied in this paper. Partial least squares method is used in drug near-infrared diffuse reflectance spectroscopy nondestructive quantitative analysis.From the limited work in this paper, the near infrared spectroscopy with partial least squares method applied to the non-destructive fungal fermentation quality analysis and control is entirely feasible and has practical application value.