Variable selection in visible and near-infrared spectra: Application to on-line determination of sugar content in pears

Informative variable (or wavelength) selection plays an important role in quantitative analysis by visible and near-infrared (Vis-NIR) spectroscopy. Four different variable selection methods, namely, stepwise multiple linear regression (SMLR), genetic algorithm-partial least squares regression (GA-PLS), interval PLS (iPLS), and successive projection algorithm-multiple linear regression combined with GA (GA-SPA-MLR), were studied to determine the sugar content of pears. The results derived by these techniques were then compared. The calibration model built using GA-SPA-MLR on 18 selected wavelengths (2% of the total number of variables) exhibited higher coefficient of determination (R-2) = 0.880 and root mean square error of prediction (RMSEP) = 0.459 degrees Brix for the validation set. Results show that the accuracy of the quantitative analysis conducted by Vis-NIR spectroscopy can be improved through appropriate wavelength selection. Despite the RMSEP value of GA-SPA-MLR was a slightly higher than that of GA-PLS, considering that this model was simpler and easier to interpret, GA-SPA-MLR can be used for industrial applications. (C) 2011 Elsevier Ltd. All rights reserved.