COLOR AND FIRMNESS CLASSIFICATION OF FRESH-MARKET TOMATOES

A total of 370 tomatoes from two seasons were analyzed using a vision system and three mechanical properties sensors which measured firmness parameters. Multiple linear regression indicated classification based on color and firmness could be applied in practical sorting and impro ves overall classification. Hue values provided adequate information f or classification. The best model (R-2 = 0.96) based on 13 specific co lors yielded severe misclassification of 2.2% for classification into 12 maturity classes and 79% correct classification with all samples cl assified +/- one maturity stage according to USDA standards. A weighte d color parameter provided a stable model invariant to changes in ligh ting conditions and yielded excellent results (R-2 = 0.89). Quality cl assification was successfully achieved using a vision and drop impact sensor.