1. bookVolume 15 (2015): Issue 4 (October 2015)
Journal Details
License
Format
Journal
eISSN
2300-8733
First Published
25 Nov 2011
Publication timeframe
4 times per year
Languages
English
Open Access

Prediction of Pork Belly Composition Using the Computer Vision Method on Transverse Cross-Sections

Published Online: 29 Oct 2015
Volume & Issue: Volume 15 (2015) - Issue 4 (October 2015)
Page range: 1009 - 1018
Received: 10 Sep 2015
Accepted: 30 Apr 2015
Journal Details
License
Format
Journal
eISSN
2300-8733
First Published
25 Nov 2011
Publication timeframe
4 times per year
Languages
English
Abstract

The objective of this study was to identify the pig belly characteristics and to develop regression equations predicting its composition. Based on video image and chemical analysis of 216 bellies, the predictive variables were selected according to their relation to chemically determined belly lipid contents. To estimate the belly fat percentage (BF%), the two best equations constructed were: Equation 1: BF% = 49.960 - 0.7174 × SHME2 + 0.5047 × HE2A (R2 = 0.66, RMSE = 3.22); Equation 2: BF% = 43.888 - 0.6014 × SHME2 + 0.4769 × HE2A + 0.0014 × ARTO2 - 0.2697 × HE3A (R2 = 0.70, RMSE = 2.25), where: SHME2 = lean meat percentage area of the belly 2 from total cut area, HE2A = the Belly2 height at point 1, ARTO2 = the Belly2 total cut area, HE3A = the Belly3 height at point 1. Compared to lean meat, the percentage of belly fat (BF%) appears to be a more appropriate criterion for the objective evaluation of belly composition due to the simplicity and accuracy of the final regression equation (higher R2).

Keywords

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