Application of Competitive Adaptive Reweighted Sampling Method to Determine Effective Wavelengths for Prediction of Total Acid of Vinegar

In this paper, near-infrared (NIR) spectroscopy coupled with wavelength selection methods was used to predict total acid of vinegar. Three wavelength selection methods including competitive adaptive reweighted sampling (CARS), Monte Carlo uninformative variable elimination (MC-UVE), and moving window partial least squares (MWPLS) were employed to select the key wavelengths. Five wavelengths including 4,348, 4,694, 5,365, 7,104, and 7,236 cm(-1) were selected by CARS method. Least squares (LS) regression model was built on the selected wavelengths. Compared to the partial least squares regression models based on full spectrum and wavelengths selected by MC-UVE and MWPLS, the performance of LS model was better, with higher determination coefficient for test (r (2)) of 0.997, and lower root mean square error of prediction of 0.13 g/100 ml. Based on the results, it was concluded that NIR spectroscopy combined with CARS methods seem to be a rapid and effective alternative to the classical methods for the prediction of total acid of vinegar.