Use of electronic nose and trained sensory panel in the evaluation of tomato quality

In this paper the performances of an electronic nose based on metalloporphyrin-coated quartz microbalance sensors and of an experienced panel of seven human assessors in the evaluation of gases derived from degradation reactions in tomatoes are presented and discussed. The performances are measured in terms of the capability of both systems to distinguish between samples of different quality coming from conventional and organic production systems. The study deals with the application of pattern recognition techniques based on either multivariate statistical methods (PCA, GPA) or artificial neural networks using a self-organising map (SOM). The response pattern of the sensor array and the sensory data are analysed and compared using these methods. Similarities in the classification of the data by electronic nose and human sensory profiling are found.