DocumentCode :
2757911
Title :
Ham quality control by means of fuzzy decision trees: a case study
Author :
Adorni, G. ; Bianchi, D. ; Cagnoni, S.
Author_Institution :
Parma Univ., Italy
Volume :
2
fYear :
1998
fDate :
4-9 May 1998
Firstpage :
1583
Abstract :
Lean muscle color strongly influences consumer impressions of quality. A bright-reddish pork is sought as an ideal; some variation of color is normal as can be observed if different pork muscles are considered. However, muscle color changes quite easily, and as a result can be indicative of meat quality. Assessment of meat quality is crucial both in cooked ham and raw ham processing plants. A good meat classification system should allow porks of uniform meat to be processed in a uniform way. This would result in a uniform parcel of ham (cooked or raw) and reduce cost and rejects. We present a classification methodology of fresh pork meat based on computer vision color analysis techniques and fuzzy decision trees. The discussed methodology has been tested on site and the obtained classifications have been compared with human experts´ ratings giving interesting results
Keywords :
computer vision; food processing industry; fuzzy logic; fuzzy set theory; image classification; image colour analysis; quality control; bright-reddish pork; computer vision color analysis techniques; consumer impressions; cooked ham; fuzzy decision trees; ham quality control; lean muscle color; meat classification system; meat quality; pork muscles; raw ham; Classification tree analysis; Color; Computer vision; Costs; Decision trees; Fuzzy control; Humans; Muscles; Quality control; Testing;
fLanguage :
English
Publisher :
ieee
Conference_Titel :
Fuzzy Systems Proceedings, 1998. IEEE World Congress on Computational Intelligence., The 1998 IEEE International Conference on
Conference_Location :
Anchorage, AK
ISSN :
1098-7584
Print_ISBN :
0-7803-4863-X
Type :
conf
DOI :
10.1109/FUZZY.1998.686355
Filename :
686355
Link To Document :
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