Discriminant models based on sensory evaluations: single assessors versus panel average

Product classification based on sensory evaluations can play an important role in quality control or typicality assessment. Unfortunately its real world applications face the difficulties related to the cost of a proper sensory approach. To partially overcome these issues we propose to build discriminant models based on the evaluation of single assessors and develop an appropriate method to combine them. We compare this new strategy with the more traditional one based on the panel average. We consider as applicative examples two datasets obtained from the sensory assessment of diverse cheese typologies from North Italy by two different panels. Also, we apply diverse, innovative and noise resistant discriminant methods (Random Forest, Penalized Discriminant Analysis and discriminant Partial Least Squares) to show that our new strategy based on modeling each individual assessor is efficient and that this result is independent of the classifier being used. The main finding of our work is that using noise-resistant multivariate methods, product discrimination based on the combination of independent models built for each assessor is never worse than discrimination based on panel average and that the error reduction is higher in the case of low consonance between assessors. Experiments on the same datasets adding random uniform values (noise) with different intensities support these findings. We also discuss a demonstrative experiment using different sets of attributes for each assessor. Overall, our results suggest that, if the goal is product classification, the consonance among assessors or even the use of the same vocabulary seem not necessary, the key factor being the discrimination capability and repeatability of each judge.