Molecular biomarkers to discriminate pork quality classes based on sensory and technological attributes
Résumé
Meat quality (MQ) is a complex phenotype assed by different indicators measured by using costly and/or invasive analyses. Early post-mortem (p.m) biomarkers of MQ refer to single indicators, but not to the overall quality of pork samples. This study aimed at determining pork quality classes combining both sensory and technological dimensions. Then, combinations of biomarkers discriminating between quality classes were identified to further predict quality level of pork loins. Sensory, technological and gene expression data were collected on 100 pig Longissimus (8th dorsal to 2nd lumbar vertebrae level) samples exhibiting a wide and gradual variability in MQ. Scientific and statistical approaches were combined to select indicators and their thresholds specifying quality classes differing in sensory and technological attributes: low (=defective; L), acceptable (A) and extra (E) quality. Gene expressions were used as predictive variables in a generalized linear model to discriminate quality classes. The best model (selected with the Akaike information criterion) included expression levels of 12 genes (18% error rate on known data, 24% after cross validation). Besides, a classification tree to predict quality categories was developed, including six branches with only five genes but a higher error rate than the linear model. External validation of predictive models is currently undertaken using 250 commercial pig samples.
Domaines
Sciences du Vivant [q-bio]
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