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Development of high throughput methods to predict pork quality at industrial scale

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Abstract.

This project aimed at providing new precocious and non-invasive predictors of pork quality usable in abattoirs, in order to orientate use of carcasses and cuts and optimize their economic value. For this purpose, the project included development, testing and validation of various methods under industrial conditions on both purebred and crossbred pigs. Usual meat quality traits (ultimate pH, colour, drip loss) were available on the Longissimus muscle (LM) from 2,679 carcasses. In addition, Magnetic Resonance Imaging (MRI) technology was used to estimate the LM intramuscular fat (IMF) content and marbling. Image analysis method was automated to improve measurement rate (70 samples scanned per hour) and ensure data traceability. This automatic image processing was validated on a large scale (2,679 samples). MRI was also used to study the representativeness of IMF content determined at the 13th rib to assess average IMF of the whole LM (38 entire loins scanned). Results showed high repeatability and good predictive ability of LM average IMF content with determination at the 13th rib level (R²=0.88). Near Infrared Spectroscopy (NIRS) was used to predict the LM cooking yield. NIRS measurements were recorded on 157 fresh loins, of which 98 were individually processed to estimate cooking and slicing yields and structural defects. NIRS technology could predict slicing losses caused by paste-like and cohesion defects on processed loin slices. Finally, the expression level of 22 genes previously identified as quality biomarkers was quantified on LM samples taken early after slaughter on the same 157 carcasses to validate these biomarkers differentiating 3 pork quality categories: low, acceptable and extra technological and sensory quality levels.

PDF icon 66th EAAP, Varsovie, Pologne, 31 août-04 septembre 2015
2015