Mathématiques et Informatique Appliquées
du Génome à l'Environnement




Agence de moyen
Titre du projet
Réduction des pertes alimentaires par la prédiction des altérations microbiologiques
Nom de l'appel d'offre
ANR blanche
Défi/axe ANR
Sécurité alimentaire et défi démographique
Monique Zagorec (INRA-SECALIM)
Participants de MaIAGE
V. Loux, O. Rué
Partenaires (hors MaIAGE)
Année de démarrage - Année de fin de projet
Date de fin du projet
Food spoilage leads to significant wastes and losses, and is an important economic issue in food industry. In the case of meat, a large part of spoilage is the consequence of bacterial growth and subsequent metabolic activities causing organoleptic spoilage of the final product (defects in texture, color, odor, or aspect), leading finally to products that are lost because they do not fit the quality standards. In addition, meat production chain requires energy, water and cost consuming operations (i.e. animal breeding, slaughtering, and transformation and storage which are usually performed at low temperature). Therefore meat product spoilage that appears at the end of the process or during shelf life affects the whole production chain performances as well as the sustainability label of the meat sector. The objective of the project is to reduce food losses by predicting, early in the production process, the onset of bacterial spoilage during storage in order to propose decision-support tools for directing process. Pork and poultry meat, the two main meats consumed in France will be studied. The economic impact of losses of these products will be assessed. Dynamics of bacterial communities will be monitored during processing steps (from primary cuts to end products at use-by-date and beyond) and various descriptors of spoilage will be measured. The natural variability between batches and that associated with production processes will be considered. Data will be used to identify accurate spoilage markers and to compute innovative mathematical models for predicting spoilage occurrence as a function of the initial composition of the microbiota (diversity and abundance) and some abiotic factors (storage temperature, modified atmosphere packaging). The models will be validated on meat products, including the economic aspect in order to propose decision-support tools for the food producers.
Année de soumission