Determination of polysaccharides of Auricularia auricula using visible/near-infrared spectroscopy and chemometrics

Abstract

Visible and near infrared (Vis/NIR) spectroscopy combined with least squares-support vector machine (LS-SVM) was investigated for the determination of polysaccharides of auricularia auricula. A total of 240 samples were prepared from four different geographical origins. The calibration set was consisted of 180 samples (45 samples for each origin) and the remaining 60 samples for the validation set. Different preprocessing methods were compared in partial least squares (PLS) models including smoothing, multiplicative scatter correction (MSC), standard normal variate (SNV), the first and second derivative. PLS analysis was employed for the calibration models as well as extraction of certain latent variables (LVs). Simultaneously, some effective wavelengths (EWs) extracted by regression coefficients of LS-SVM were used as the inputs of LS-SVM compared with LVs. The optimal prediction results were achieved by LV-LS-SVM, and the correlation coefficient (r), root mean square error of prediction (RMSEP) and bias for validation set were 0.9413, 0.6893 and -0.0729, respectively. The results were slightly better than PLS. The prediction results of EW-LS-SVM using 3 EWs were acceptable with r=0.9290, RMSEP=0.7714 and Bias=-0.1737. The results indicated that Vis/NIR spectroscopy could be utilized as an efficient way for the determination of polysaccharides of auricularia auricula based on LS-SVM method.