Fusion of aroma, FT-IR and UV sensor data based on the Bayesian inference. Application to the discrimination of white grape varieties

Abstract

The objective of this study is to present a fusion method based on the Bayesian inference to combine the outputs of various sensors. The sensors studied here are aroma sensors, FT-IR and UV spectrometers. The application deals with classifying musts of white grapes according to their variety. The fusion procedure is not based on the combination of the signals, but of the class assignments provided individually by each sensor. Two methods have been developed based on the Bayesian inference: the Bayesian minimum error fusion rule and the minimum risk rule. The latter involves both experimental knowledge, in computing error probability values, and expert knowledge, through the level of error costs. The paper presents the mathematical theory concerning the Bayesian approach and the results obtained on white grape classification. This effective fusion method leads to a significant improvement in the grape variety discrimination: the final misclassification error is 4.7%, whereas the best individual sensor (FT-IR) gave a misclassification error twice as high, i.e. 9.6%. Bayesian fusion proved to be very well suited to the combination of all kinds of analytical measurements or sensors (curves or single value outputs), as long as they provide individual classification outputs. Furthermore, Bayesian fusion is able to cope with sensors providing large, noisy and redundant data as well as sensors showing very dissimilar efficiency levels.