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