Authenticating white grape must variety with classification models based on aroma sensors, FT-IR and UV spectrometry

Abstract

This paper aims at assessing the capability of high-speed analytical devices, such as aroma sensors (“electronic noses”), Fourier Transform InfraRed (FT-IR) and ultraviolet spectrometers to classify white grape musts (grape juices before fermentation) in variety categories. Due to the complexity of the signal generated, specific data processing techniques have been developed and are described here. First, a pre-processing technique, based on Genetic Algorithms, is applied to spectra to improve spectrometer efficiency without expert knowledge in spectrometry; by selecting the most discriminant subsets of wavelengths, this stochastic method tends to reduce over-fitting and improves classification results. Secondly, the Partial Least Squares Regression technique is adapted to a pattern recognition problem, using Partial Least Squares-Discriminant Analysis, a multivariate classification technique. These devices and data processing techniques are applied to more than 100 must samples. FT-IR spectrometry is the most satisfactory technique with a 9.6% classification error level. Finally, outputs of the three individual sensors are combined in a “low-level” fusion method, by concatenating the individual sensor signals. This straightforward fusion method does not significantly improve results.