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

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dc.creatorGranitto, Pablo M.
dc.creatorBiasioli, Franco
dc.creatorEndrizzi, Isabella
dc.creatorGasperi, Flavia
dc.date.accessioned2011-03-08T19:49:49Z
dc.date.available2011-03-08T19:49:49Z
dc.date.issued2008-09
dc.description.abstractProduct 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.es
dc.description.peerreviewedPeer reviewedes
dc.description.sponsorshipWork partially supported by PAT projects MIROP and SAMPPA and ANPCyT grant PICT 11-15132es
dc.identifier.citationP.M. Granitto, F. Biasioli, I. Endrizzi, F. Gasperi, Discriminant models based on sensory evaluations: Single assessors versus panel average, Food Quality and Preference, Volume 19, Issue 6, September 2008, Pages 589-595, ISSN 0950-3293, DOI: 10.1016/j.foodqual.2008.03.006. (http://www.sciencedirect.com/science/article/B6T6T-4SJ9521-1/2/b81bde4977bff9bcad49c49f90d26644)es
dc.identifier.issn0950-3293
dc.identifier.urihttp://hdl.handle.net/2133/1699
dc.language.isoenes
dc.publisherElsevier B.V.es
dc.relation.publisherversionhttp://dx.doi.org/10.1016/j.foodqual.2008.03.006es
dc.rightsOpen accesses
dc.rights.textCopyright © 2008 Elsevier Ltd All rights reservedes
dc.subjectSensory profilinges
dc.subjectDiscriminant analysises
dc.subjectDiscriminant partial least squareses
dc.subjectRandom Forestes
dc.subjectPenalized discriminant analysises
dc.titleDiscriminant models based on sensory evaluations: Single assessors versus panel averagees
dc.typeArticlees

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