Least-Squares Support Vector Machines to Correct Temperature-Induced Spectral Variation in Multivariate Calibration

Abstract

This paper reports the use of least-squares support vector machines (LS-SVM) for non-linear multivariate calibration in the determination of the alcohol content in the Brazilian spirit “cachaça” using near infrared spectroscopy. Fifty cachaça samples, with alcohol contents in the range of 20.9% to 46.5% v/v were used and the spectra were obtained at five different temperatures: 15°C, 20°C, 25°C, 30°C and 35°C. Two models were proposed: in the first, a single model was built, using the spectra from all five temperatures. In the second, the calibration set was composed of the spectra taken at four temperatures and the validation set was composed of spectra of the other temperature. All the combinations were made. In four of them, LS-SVM produced better predictions than PLS and in the other, the results were the same. These results indicate that LS-SVM can be an alternative when there is an influence of some physical variations, such as temperature, on near-infrared spectra.