In silico prediction of spoilage phenotypes using metagenomics approaches

Models are an effective tool to assess microbiological spoilage in food systems. Accurate identification and phenotypic characterization of spoilage bacteria can aid in the construction of these models. This project focuses on the development of an in silico method to predict spoilage phenotypes based on allelic types (ATs) allowing for quick identification of bacterial characteristics that influence spoilage. A database built from collected data from spinach and milk will allow for rapid assessment of spoilage phenotypes in food products using targeted sequencing methods.

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Your email address will not be published.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