Combination and Comparison of Chemometric Methods for Determination of pH of Rice Vinegars Using Visible and Near Infrared Spectroscopy

Visible and near infrared (Vis/NIR) spectroscopy combined with a hybrid calibrations of partial least squares (PLS), BP neural network (BPNN) and least squares-support vector machine (LS-SVM) was investigated to determine the pH values of rice vinegars. Five varieties of rice vinegars and 300 samples were prepared. After some preprocessing, PLS was implemented for calibration as well as the extraction of principal components, which would be used as the inputs of BPNN and LS-SVM models. Finally, the BPNN and LS-SVM regression models were developed with a comparison of PLS. The correlation coefficient (r), root mean square error of prediction (RMSEP) and bias for prediction were 0.982, 0.042, and -0.012 by PLS, 0.992, 0.027, and 0.004 by BPNN, while 0.996, 0.018, and 0.005 by LS-SVM, respectively. The results indicated that Vis/NIR spectroscopy combined with chemometrics could be applied as a high precision way for the determination of pH of rice vinegars.