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Color and firmness classification of fresh market tomatoes
Year:
1997
Source of publication :
Journal of Food Science
Authors :
Fallik, Elazar
;
.
Grinberg, Shoshana
;
.
Pasternak, Hanoch
;
.
Volume :
62
Co-Authors:
Edan, Y., Dept. of Indust. Eng. and Management, Ben-Gurion Univ. of the Negev, P.O. Box 653, Beer-Sheva, 84105, Israel
Pasternak, H., Inst. of Agricultural Eng., A.R.O, Volcani Center
Shmulevich, I., Dept. of Agriculture Eng., Technion, Hebrew University
Rachmani, D., Dept. of Agriculture Eng., Technion, Hebrew University
Guedalia, D., Neural Computation Dept., Hebrew University
Grinberg, S., Inst. of Postharvest Technol., A.R.O, Volcani Center
Fallik, E., Inst. of Postharvest Technol., A.R.O, Volcani Center
Facilitators :
From page:
793
To page:
796
(
Total pages:
4
)
Abstract:
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 regresssion 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 (R2 = 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 (R2 = 0.89). Quality classification was successfully achieved using a vision and drop impact sensor.
Note:
Related Files :
classification
color
Color analysis
maturity
Solanum lycopersicum
Sorting
Tomatoes
Show More
Related Content
More details
DOI :
Article number:
0
Affiliations:
Database:
Scopus
Publication Type:
article
;
.
Language:
English
Editors' remarks:
ID:
28553
Last updated date:
02/03/2022 17:27
Creation date:
17/04/2018 00:40
Scientific Publication
Color and firmness classification of fresh market tomatoes
62
Edan, Y., Dept. of Indust. Eng. and Management, Ben-Gurion Univ. of the Negev, P.O. Box 653, Beer-Sheva, 84105, Israel
Pasternak, H., Inst. of Agricultural Eng., A.R.O, Volcani Center
Shmulevich, I., Dept. of Agriculture Eng., Technion, Hebrew University
Rachmani, D., Dept. of Agriculture Eng., Technion, Hebrew University
Guedalia, D., Neural Computation Dept., Hebrew University
Grinberg, S., Inst. of Postharvest Technol., A.R.O, Volcani Center
Fallik, E., Inst. of Postharvest Technol., A.R.O, Volcani Center
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 regresssion 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 (R2 = 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 (R2 = 0.89). Quality classification was successfully achieved using a vision and drop impact sensor.
Scientific Publication
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