Share Email Print
cover

Proceedings Paper

Estimation of lean and fat composition of pork ham using image processing measurements
Author(s): Jiancheng Jia; Allan P. Schinckel; John C. Forrest
Format Member Price Non-Member Price
PDF $14.40 $18.00

Paper Abstract

This paper presents a method of estimating the lean and fat composition in pork ham from cross-sectional area measurements using image processing technology. The relationship between the quantity of ham lean and fat mass with the ham lean and fat areas was studied. The prediction equations for pork ham composition based on the ham cross-sectional area measurements were developed. The results show that ham lean weight was related to the ham lean area (r equals .75, P < .0001) while ham fat weight was related tot the ham fat area (r equals .79, P equals .0001). Ham lean weight was highly related to the product of ham total weight times percentage ham lean area (r equals .96, P < .0001). Ham fat weight was highly related to the product of ham total weight times percentage ham fat area (r equals .88, P < .0001). The best combination of independent variables for estimating ham lean weight was trimmed wholesale ham weight and percentage ham fat area with a coefficient of determination of 92%. The best combination of independent variables for estimating ham fat weight was trimmed wholesale ham weight and percentage ham fat area with a coefficient of determination of 78%. Prediction equations with either two or three independent variables did not significantly increase the accuracy of prediction. The results of this study indicate that the weight of ham lean and fat could be predicted from ham cross-sectional area measurements using image analysis in combination with wholesale ham weight.

Paper Details

Date Published: 6 January 1995
PDF: 10 pages
Proc. SPIE 2345, Optics in Agriculture, Forestry, and Biological Processing, (6 January 1995); doi: 10.1117/12.198887
Show Author Affiliations
Jiancheng Jia, Nanyang Technological Univ. (Singapore)
Allan P. Schinckel, Purdue Univ. (United States)
John C. Forrest, Purdue Univ. (United States)


Published in SPIE Proceedings Vol. 2345:
Optics in Agriculture, Forestry, and Biological Processing
George E. Meyer; James A. DeShazer, Editor(s)

© SPIE. Terms of Use
Back to Top