S23 - Session O4 - Machine Learning for the Molecular Evaluation of Fresh Produce Quality

Authors: Tie Liu *

Fresh fruits and vegetables are invaluable for human health, but their quality deteriorates during distribution before reaching consumers due to ongoing biochemical processes and compositional changes. The current lack of any objective indices for defining " freshness " of fruits or vegetables limits our capacity to control product quality and leads to food loss and waste. In this work, we undertook interdisciplinary research to address plant science challenges related to food security and human health. It will leverage machine learning technologies and multi-omics tools to understand postharvest senescence and microbial spoilage of fresh produce to evaluate the quality. We therefore propose a comprehensive research program to identify proteins and compounds as "freshness-indicators" and to aid development of an innovative and easy-to-use accessibility tool to accurately estimate the freshness of produce and or contamination of produce. The goal of the proposed research will advance in both basic research and applied science. Such a tool would allow a new level of postharvest logistics, supporting availability of high-quality, nutritious, fresh produce.