Accelerating food formulation with predictive AI

Overview

• Industry-academic collaboration opportunity in food & nutrition innovation
• Partnership with unnamed company to develop AI-enabled food formulation tools
• Opportunity to address critical formulation challenges in food product development
  Focus on reducing resource-intensive iterations in the product development cycle

Priority Areas

• AI Recipe Platforms: Rapid recipe iteration systems incorporating nutritional and regulatory constraints
• Automated Design Engines: AI-driven systems generating initial formulations from concept requirements
• Safety Prediction Models: Tools predicting allergens and sensitivities for safer product development
• Performance Prediction: Models forecasting taste, texture, and nutritional quality of formulations
• Ingredient Flexibility: Simulators for dynamic recipe adjustment and substitution management
• Variability Management: Machine learning approaches for monitoring/adjusting ingredient variability

Eligibility & Requirements

Must Have:

• Ability to automatically generate and optimize initial formulations based on required product attributes
• Capacity to predict product performance (taste, texture, nutrition) for optimization
• Functionality to rapidly iterate and refine recipes with minimal bench testing
• Capability to adjust formulations dynamically when ingredients are substituted or vary in quality
• Mechanism to flag/optimize potential compliance issues early (nutrition, regulatory, or cost-related)

Nice to Have:

• Support for integration into automated workflows and communication with existing AI models
• User-friendly platform enabling co-creation
• End-to-end integration of all necessary steps in new product development

Please note: Full RFP is attached in the "More Information" section of this page. Faculty and researchers interested in applying for these opportunities based on technologies developed or disclosed at Vanderbilt must submit their proposals through the CTTC.