Our laboratory. Empa's Simulating Biological Systems Group aims to reduce food loss in postharvest supply chains by understanding and steering these systems in-silico. We do this by pioneering physics-based modeling at multiple scales, bridging the virtual to the real world by multi-parameter sensing, and creating digital twins that can live together with their real-world counterparts. We are an interdisciplinary team of mechanical, biomedical, and agricultural engineers, food scientists, and environmental scientists.We are part of the Laboratory for Biomimetic Membranes and Textiles in Empa's Research Focal Area Health and Performance.
Background. Up to one‐third of the world's plant-based foods are lost on their way from farm to consumer. We still do not exactly understand when or why this postharvest quality loss occurs within each of the hundreds of shipments in a supply chain, let alone how to reduce it. One challenge is that each fruit has its own preharvest physiological history when starting its postharvest journey, depending on the growing and harvest conditions. Another challenge is that no two shipments evolve the same in the cold chain, due to unpredictable technical and logistical conditions and stakeholder handling. These pre-and postharvest factors affect the physical, biochemical, physiological, and microbiological processes in these heat-sensitive products. As a result, the quality of each fruit inside each shipment evolves uniquely before landing on the consumer's table, which we still know little about. The extensive hygrothermal monitoring that is done during agricultural production, postharvest cooling, transport, and storage provides a part of the answer. We currently miss the link between these data and the resulting quality loss for every single fruit inside each cargo.
Objective.This project has the aim to identify where fruit quality loss occurs, to quantify how large this is, and to optimize the supply chain. The work will enable to better identify how individual perishable products react inside a cargo of millions in cold chain unit operations, and to pinpoint why some products decay faster than others. This information will be used to quantify the environmental impact of optimized cold chain solutions.