NIRS Prediction of Pork Intramuscular Fat for Future Use in Genetic Evaluation of Local Breeds
- Marie-José Mercat (Ifip-Institut du Porc)
- Antoine Vautier (Ifip-Institut du Porc)
- Philippe Ganier (French National Institute for Agricultural Research (INRAE))
- Sophie Dare (French National Institute for Agricultural Research (INRAE))
- Gwladys Flého (Ifip-Institut du Porc)
- Klavdija Poklukar (Agricultural Institute of Slovenia)
- Martin Škrlep (Agricultural Institute of Slovenia)
- Juliette Magadray (Ifip-Institut du Porc)
- Herveline Lenoir (Ifip-Institut du Porc)
- Bruno Ligonesche (Nucléus)
- Danijel Karolyi (University of Zagreb)
- Dubravko Škorput (University of Zagreb Faculty of Agriculture)
- Andreia Amaral (University of Évora)
- Meta Čandek-Potokar (Agricultural Institute of Slovenia)
- Bénédicte Lebret (French National Institute for Agricultural Research (INRAE))
Abstract
Within the H2020 GEroNIMO project, a collaborative initiative developed high-throughput phenotyping tools to assess intramuscular fat (IMF) content, a key indicator of pork sensory quality. This joint effort gathered 548 Longissimus muscle samples collected from 13 pig breeds, including nine local pig breeds and four high-performance genotypes. The reference IMF content was measured in duplicate by chemical extraction of minced Longissimus muscle slices sampled at the first lumbar vertebra. All analyses were performed in the same laboratory to avoid an inter-laboratory bias, with values ranging from 1.0% to 11.0% and a standard deviation (SD) of 1.9%. This wide phenotypic variation provided a robust basis for model calibration and validation of Near-Infrared Spectroscopy (NIRS) devices for the prediction of IMF content in a wide range of pig genetic types. Ground samples were later shared among collaborating partners for developing NIRS equations specific to each analytical device. Two NIRS devices in France (INRAE, IFIP) and one in Portugal (CIMO, still in progress at the time of writing) were newly calibrated for the prediction of IMF content. In Slovenia, the existing prediction equation of the KIS device was updated with additional GEroNIMO samples. Each device was calibrated separately, and calibration performance was assessed either with cross-validation or an external validation approach. The results demonstrated high predictive ability, with coefficients of determination (R²) ranging from 0.94 to 0.97 and errors (root mean square error of prediction or standard error of cross-validation) reported between 0.30 and 0.42 depending on the device and the dataset. The calibration equations were then applied to predict IMF content in 455 Krškopolje and 367 Gascon loin samples collected in Slovenia and France, respectively. The predicted IMF values were used to estimate genetic parameters, yielding heritability estimates of 0.44 (±0.10) in the Krškopolje pig and 0.61 (±0.19) in the Gascon breed. Currently, no heritability estimates based on chemically determined IMF are available for these local breeds. Nevertheless, the heritability values derived from NIRS-based predictions align closely with those reported in the literature for chemically determined IMF in other pig populations (usually between 0.2 and 0.7). These results confirm a substantial genetic component of IMF and demonstrate the potential for genetic improvement of this major meat quality trait. The use of the NIRS as a cost-effective, environmentally friendly, fast and easy phenotyping tool therefore provides new opportunities for implementing sustainable breeding strategies in local pig breeds, combining genetic improvement of key quality traits with the preservation of genetic diversity.
Keywords: 2026
How to Cite:
Mercat, M., Vautier, A., Ganier, P., Dare, S., Flého, G., Poklukar, K., Škrlep, M., Magadray, J., Lenoir, H., Ligonesche, B., Karolyi, D., Škorput, D., Amaral, A., Čandek-Potokar, M. & Lebret, B., (2026) “NIRS Prediction of Pork Intramuscular Fat for Future Use in Genetic Evaluation of Local Breeds”, World Congress on Genetics Applied to Livestock Production Digital Archive 2026(1): 2286016. doi: https://doi.org/10.31274/wcgalp.23839
Rights: 1
Downloads:
Download PDF
View PDF
73 Views
16 Downloads