J. Anim Sci.
HOME HELP FEEDBACK SUBSCRIPTIONS ARCHIVE SEARCH TABLE OF CONTENTS
QUICK SEARCH:   [advanced]


     

This Article
Right arrow Full Text (PDF)
Right arrow Alert me when this article is cited
Right arrow Alert me if a correction is posted
Services
Right arrow Similar articles in this journal
Right arrow Similar articles in PubMed
Right arrow Alert me to new issues of the journal
Right arrow Download to citation manager
Right arrow reprints & permissions
Citing Articles
Right arrow Citing Articles via HighWire
Right arrow Citing Articles via Google Scholar
Google Scholar
Right arrow Articles by Tan, F. J.
Right arrow Articles by Gerrard, D. E.
Right arrow Search for Related Content
PubMed
Right arrow PubMed Citation
Right arrow Articles by Tan, F. J.
Right arrow Articles by Gerrard, D. E.

Journal of Animal Science, Vol 78, Issue 12 3078-3085, Copyright © 2000 by American Society of Animal Science

JOURNAL ARTICLE

Assessment of fresh pork color with color machine vision

F. J. Tan, M. T. Morgan, L. I. Ludas, J. C. Forrest and D. E. Gerrard
Department of Agricultural and Biological Engineering, Purdue University, West Lafayette, IN 47907, USA.

Currently, fresh pork color is visually evaluated using either the Japanese Pork Color Standards (JPCS) or the National Pork Producers Council Pork Quality Standards (NPPC) as a reference. Although useful, visual evaluation of meat color can vary with evaluator and may be quite expensive. In this study, three separate studies were used to compare the ability of color machine vision (CMV) and untrained panelists to evaluate pork color. Panels visually evaluated over 200 pork loin chops using either the JPCS or NPPC reference standards. Results from each panel were used to evaluate the ability of the CMV to sort pork loin chops based on the same criteria. Representative samples, typical of each color class, were used to train neural-network-based image processing software. After training, the CMV system was used to evaluate quality classes of pork samples based on color distribution. Classification by CMV was compared with the average panel score, rounded to the nearest integer. Training the CMV system using images of actual meat samples resulted in a stronger correlation to panel scores than training with either set of artificial color standards. Agreement between the CMV system and the panels was as high as 90%. Agreement between individual panelists and the integer panel average (52 to 85%) was less than that observed for CMV classification. Finally, the on-line performance of CMV using a laboratory conveyor system was simulated by repeatedly classifying 37 samples at a speed of 1 sample per second. Collectively, these results demonstrate that CMV is a rapid and repeatable means of evaluating pork color.


This article has been cited by other articles:


Home page
 J ANIM SCIHome page
C. A. Stahl, H. Heymann, K. Adhikari, and E. P. Berg
Pork quality attributes associated with bilateral carcass variation
J Anim Sci, February 1, 2006; 84(2): 456 - 462.
[Abstract] [Full Text] [PDF]

Home page
 J ANIM SCIHome page
P. Carnier, L. Gallo, C. Romani, E. Sturaro, and V. Bondesan
Computer image analysis for measuring lean and fatty areas in cross-sectioned dry-cured hams
J Anim Sci, March 1, 2004; 82(3): 808 - 815.
[Abstract] [Full Text] [PDF]


HOME HELP FEEDBACK SUBSCRIPTIONS ARCHIVE SEARCH TABLE OF CONTENTS
Copyright © 2000 by the American Society of Animal Science.