<abstract xmlns="http://www.w3.org/1999/xhtml"> 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).</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).

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

Department of Animal Husbandry, Czech University of Life Sciences Prague, Kamýcká 129, 165 21 Prague 6-Suchdol, Czech Republic

Annals of Animal Science

This work is licensed under the Creative Commons Attribution-NonCommercial-NoDerivatives 4.0 License.

The objective of this study was to identify the pig belly characteristics and to develop regression equations predicting its composition. Based on video image...

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

Article Category: Quality and Safety of Animal Origin Products

Pubblicato online: 29 ott 2015

Pagine: 1009 - 1018

Ricevuto: 10 set 2015

Accettato: 30 apr 2015

DOI: https://doi.org/10.1515/aoas-2015-0034

Parole chiave
pig, pork belly, belly composition, image analysis

© by Jaroslav Čítek

This work is licensed under the Creative Commons Attribution-NonCommercial-NoDerivatives 4.0 License.

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

Article Category: Quality and Safety of Animal Origin Products

Pubblicato online: 29 ott 2015

Pagine: 1009 - 1018

Ricevuto: 10 set 2015

Accettato: 30 apr 2015

DOI: https://doi.org/10.1515/aoas-2015-0034

Parole chiavepig, pork belly, belly composition, image analysis

© by Jaroslav Čítek

This work is licensed under the Creative Commons Attribution-NonCommercial-NoDerivatives 4.0 License.

Parole chiave
pig, pork belly, belly composition, image analysis