Rapid detection of three quality parameters and classification of wine based on Vis-NIR spectroscopy with wavelength...

The feasibility of rapid detection of three quality parameters and classification of wines based on visible and near infrared spectroscopy (Vis-NIRs) was investigated. A modified ant colony optimization (ACO) algorithm for wavelength selection in Vis-NIR spectral analysis was proposed to improve the prediction performance of partial least squares regression (PLSR) model. The result proved that feature wavelengths/variables can be selected by the proposed method for building a high performance PLSR model. The root mean square error of total acid, total sugar and alcohol obtained by ACO-PLS were 0.00122 mol/l, 0.0893 g/l and 0.206 (v/v), respectively. Their correlation coefficients obtained by ACO-PLS reach to 0.973, 0.994 and 0.928, respectively. Compared with full-spectral PLS and PLS based on competitive adaptive reweighted sampling (CARS-PLS) method, the application of ACO wavelength selection provided a notably improved regression model. The prediction results were significantly better than the full-spectral PLS model and slightly better than the CARS-PLS method. Meanwhile, a classification study was also constructed based on the ACO-Principal component analysis (ACO-PCA) model showed that Vis-NIR spectra could be used to classify wines according to the geographical origins. Therefore, it can be concluded that the Vis-NIR spectroscopy technique based on ACO wavelength selection has high potential to differentiate the wine origins and predict the quality parameters in a nondestructive way.