Targeted, data-driven interventions in poultry production can reduce meat waste dramatically
According to the cluster organization and ZeroW-project partner ASINCAR, prevention and reduction of food waste (FLW) is at the top of industrial agendas within the meat industry.
According to the cluster organization and ZeroW-project partner ASINCAR, prevention and reduction of food waste (FLW) is at the top of industrial agendas within the meat industry. This is not only a challenge for moral reasons in a world in which the FAO estimated a 28,3% of the World’s population in 2022 is moderately or severely food insecure, but also because meat represents a high value.
In fact, the meat sector is a cornerstone in the EU economy and labour market. Numbers from Food Drink Europe show that it accounts for the highest subcategory share in F&D turnover (20%) and second in relation to employment. Yet the existing practices in the meat value system contribute to avoidable food waste.
NIR applications reduces FLW early in the production phase
Currently, ASINCAR explains, much of the process monitoring and control in food industry is based on the expertise and subjective analysis of the concrete operator as well as a limited number of food tests, yet the manual process is vulnerable and the current testing methods are time-consuming and product destructive. Besides, with current analytics, results are obtained after several hours or days. Altogether, this limits the possibilities of timely interventions to remove incompliances, leading to avoidable food waste.
ASINCAR, who has long experience in the development of NIR (Near Infrared Spectroscopy) applications for the food, is suggesting exactly this non-distructive and quasi-real time technology to reduce FLW early in the production phase of meat processing.
More specifically, as a contribution to the ZeroW project, the ASINCAR-team is developing and piloting advanced digital technologies for the control and optimization of key products and processes in the poultry production lines.
The main goal is through targeted interventions in critical steps in the processing line to reduce food losses and boost sustainability.
The team will implement three cutting-edge computational modules that focus on crucial product and process parameters that contribute to food losses. Two of these modules will use portable Near Infrared technology and AI-driven chemometrics, while the third will intelligently analyze data from classical sensors using novel AI tools.
The final prototypes will be tested in the Spanish meat producing company Aldelís’ facilities, and the team expects to see a 20% increase in food streams passing quality standards for food consumption by 2025, and a 25% reduction in food loss.
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