Rapid prediction of multiple wine quality parameters using infrared spectroscopy coupling with chemometric methods

Infrared spectroscopy (IRs) coupling with chemometric methods were used to predict principal quality parameters in wine. A new strategy of variable (wavelength) selection named as Fisher Discriminant-Variable Selection (FD-VS) model was constructed. Characteristic variables were selected from Infrared spectra based on the absolute values of eigenvector obtained by Fisher Discriminant Function. The FD-VS method was combined with quantitative models including Principal Component Regression (PCR), Partial Least Squares (PLS) and Least Squares Support Vector Regression (LSSVR), which were utilized for prediction of multiple principal quality parameters of red wine. It is shown that FD-VS method obviously improves the performances of PCR, PLS and LSSVR models. Then four variable selection methods based on PLS regression including Competitive Adaptive Reweighted Sampling (CARS)-PLS, Uninformative Variable Elimination (UVE)-PLS, Interval Partial Least Squares (IPLS) and Moving Windows Partial Least Squares (MWPLS) were also compared. The results also show good performance of FD-VS-LSSVR in terms of prediction accuracy or robustness. Therefore, the FD-VS method provides an effective and credible variable selection way for IR spectrum to predict quality parameters of wine.