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Color and Firmness Classification of Fresh Market Tomatoes
Author(s) -
EDAN YAEL,
PASTERNAK H.,
SHMULEVICH I.,
RACHMANI D.,
GUEDALIA D.,
GRINBERG S.,
FALLIK E.
Publication year - 1997
Publication title -
journal of food science
Language(s) - English
Resource type - Journals
SCImago Journal Rank - 0.772
H-Index - 150
eISSN - 1750-3841
pISSN - 0022-1147
DOI - 10.1111/j.1365-2621.1997.tb15457.x
Subject(s) - hue , mathematics , artificial intelligence , pattern recognition (psychology) , sorting , color measurement , maturity (psychological) , linear regression , invariant (physics) , statistics , computer science , algorithm , psychology , developmental psychology , mathematical physics
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 improves overall classification. Hue values provided adequate information for classification. The best model (R 2 = 0.96) based on 13 specific colors yielded severe misclassification of 2.2% for classification into 12 maturity classes and 79% correct classification with all samples classified ± one maturity stage according to USDA standards. A weighted color parameter provided a stable model invariant to changes in lighting conditions and yielded excellent results (R 2 = 0.89). Quality classification was successfully achieved using a vision and drop impact sensor.