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Prediction of foal carcass composition and wholesale cut yields by using video image analysis

  • J. M. Lorenzo (a1), C. M. Guedes (a2), R. Agregán (a1), M. V. Sarriés (a3), D. Franco (a1) and S. R. Silva (a2)...

Abstract

This work represents the first contribution for the application of the video image analysis (VIA) technology in predicting lean meat and fat composition in the equine species. Images of left sides of the carcass (n=42) were captured from the dorsal, lateral and medial views using a high-resolution digital camera. A total of 41 measurements (angles, lengths, widths and areas) were obtained by VIA. The variation of percentage of lean meat obtained from the forequarter (FQ) and hindquarter (HQ) carcass ranged between 5.86% and 7.83%. However, the percentage of fat (FAT) obtained from the FQ and HQ carcass presented a higher variation (CV between 41.34% and 44.58%). By combining different measurements and using prediction models with cold carcass weight (CCW) and VIA measurement the coefficient of determination (k-fold-R 2) were 0.458 and 0.532 for FQ and HQ, respectively. On the other hand, employing the most comprehensive model (CCW plus all VIA measurements), the k-fold-R 2 increased from 0.494 to 0.887 and 0.513 to 0.878 with respect to the simplest model (only with CCW), while precision increased with the reduction in the root mean square error (2.958 to 0.947 and 1.841 to 0.787) for the hindquarter fat and lean percentage, respectively. With CCW plus VIA measurements is possible to explain the wholesale value cuts yield variation (k-fold-R 2 between 0.533 and 0.889). Overall, the VIA technology performed in the present study could be considered as an accurate method to assess the horse carcass composition which could have a role in breeding programmes and research studies to assist in the development of a value-based marketing system for horse carcass.

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Corresponding author

E-mail: jmlorenzo@ceteca.net

References

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Keywords

Prediction of foal carcass composition and wholesale cut yields by using video image analysis

  • J. M. Lorenzo (a1), C. M. Guedes (a2), R. Agregán (a1), M. V. Sarriés (a3), D. Franco (a1) and S. R. Silva (a2)...

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