Microbiome-Informed Computational Models and Decision Support Tools to Predict Fresh Produce Spoilage: Spinach as a Model System

Microbial food spoilage is a significant economic, environmental and societal problem: 40% of food in the US is reported to go to waste, with 2/3 of this spoilage being estimated to result from unwanted microbial growth. The goal of this project is to develop a computational model of microbiome interactions and perturbations during processing, transportation and retail for predicting shelf life of fresh spinach. Prediction of food spoilage in the food industry to date is typically based on limited laboratory experiments and shelf-life studies conducted under a single or very few conditions. Actual product however is produced and distributed under a range of very different conditions throughout the supply chain. Hence, there is a need for transformative solutions to reduce food waste using a systems approach, in which innovative technologies are integrated across each stage of the supply chain to reduce the volume of food wasted. In this study, we will construct computational models and decision support tools to predict shelf life using both classical microbiological and metagenomics data. This work will serve as a basis to later develop and pilot transformational strategies to reduce food waste through more accurate shelf-life prediction.

Leave a Reply

Your email address will not be published. Required fields are marked *

Become a Fellow

Join the Cornell Institute for Digital Agriculture and become a participating member in advancing research, thought, policy and practice to advance the field of digital agriculture and help build stronger, more resilient agri-food systems.

Stay up to Date

Receive our newsletter for announcements of events, opportunities, digital ag news, Cornell news, and more.

CIDA - Cornell Institute for Digital Agriculture

If you have a disability and are having trouble accessing information on this website or need materials in an alternate format, contact [email protected] for assistance.


CIDA Copyright 2023 | CIDA is an equal opportunity employer | Terms of Use | Privacy Policy