Machine learning-based life cycle assessment for environmental sustainability optimization of a food supply chain.

Effective resource allocation in the agri-food sector is essential in mitigating environmental impacts and moving toward circular food supply chains. The potential of integrating life cycle assessment (LCA) with machine learning has been highlighted in recent studies. This hybrid framework is valuable not only for assessing food supply chains but also for improving them toward a more sustainable system.

Inclusion of microbiological food safety as a novel aspect in life cycle assessments of food production.

The life cycle assessment (LCA) methodology currently covers a limited number of human health-related impact categories. Microbiological food safety is an essential aspect for the selection of an appropriate food production system and has been neglected in the LCA so far. A framework for the inclusion of a microbiological food safety indicator, expressed as disability-adjusted life year (DALY) value of the consumed food product to the human health damage category (end-point) was created, and applied in a case study model on the cooked-chilled meals as the ready-to-eat meals can be associated with the occurrence of foodborne illness cases and outbreaks.