2022/04/26 by Normand Jérôme, Bruno Meunier, Normand, Jérôme +9
Medicine · #Analyse d’image #Artificial neural network #Composition de la côte #Computer image analysis #Marbling #Nutritional Studies and Diet #Persillé #Rib composition #Réseau de neurones artificiels
paper · doi:10.17180/ciag-2022-vol85-art16
openalex publication_date 2022/04/26 · openalex created_date 2022/08/30 · openalex updated_date 2026/07/27
Fat has a major economic importance in the beef industry. It affects all the meat food chain steps: from the farmer to the consumer, through the slaughterer-processor or the distributor. At the beginning of the project, no tool was able to measure fat in meat in real time, in a reliable, economical and non-destructive way. The Meat@ppli project aimed to predict the fat content of beef from its photo, both at the carcass and sliced beef stage, based on image analysis methods. The results are encouraging, with correlations with reference methods varying from 0.5 to 0.9. The prediction models were embedded in the Meat@ppli application, developed for fat measurement at the carcass stage. It remains a proof-of-concept that, in the future, could be used by the beef industry to route carcasses to the most suitable distribution channels and to perform massive phenotyping for the selection of bovines with appropriate marbling.