GROUP A: DEVELOPING AN AI-ENABLED TOOLKIT FOR ROUTINE INTEGRATION OF NUTRITIONAL QUALITY AND AROMA TRAITS INTO MOLECULAR BREEDING STRATEGIES

SIGNIFICANCE

This project directly addresses Theme 2 as it aims to create AI tools to support human health through nutritious food (in the form of crop varieties). The project is primarily focused on the Molecular Breeding cluster, but brings together experts from across four AIFS clusters.

GOALS

Aim 1. Create predictors of nutritional quality or aroma from genetic and phenotypic features.

Aim 2. Develop ML tools to evaluate the quality (or ‘total value’) of new varieties based on their nutritional or aromatic molecular compositions.

GROUP B: COMPOUND-DRIVEN IDENTIFICATION AND PREDICTION OF FLAVOR AND HEALTH

SIGNIFICANCE

The project is well aligned to the AIFS mission to (a) create AI solutions for food systems that are explainable and generalizable, (b) leverage AI to organize and democratize knowledge related to food and (c) advance the state of the art in algorithms to create a more nutritious food system.

GOALS

The goal of this project is to create predictors for flavor and health effects from chemical compounds. In this proposal we will focus on three major classes of compounds that play a significant role in food flavor: phenols, terpenes, and peptides.