**AWARDS ISSUED PRIOR TO JANUARY 20, 2025, WERE FUNDED UNDER PREVIOUS ADMINISTRATIONS AND MAY NOT REFLECT THE PRIORITIES AND POLICIES OF THE CURRENT ADMINISTRATION.** THE GLOBAL PREVALENCE OF FOOD-RELATED HEALTH CHALLENGES HAS INCREASED DRAMATICALLY IN RECENT YEARS. THERE IS AN OPPORTUNITY TO ADDRESS THESE CHALLENGES THROUGH ADVANCEMENT OF EVIDENCE-BASED TOOLS THAT STREAMLINE DEVELOPMENT OF HEALTH-PROMOTING FOODS. HOWEVER, THERE IS A CRUCIAL DEFICIENCY OF STANDARDIZED METHODS TO PREDICT FOOD BEHAVIOR AFTER CONSUMPTION (E.G. DURING DIGESTION) THAT CAN BE CORRELATED TO PHYSIOLOGICAL OUTCOMES, SUCH AS LOWERING BLOOD GLUCOSE, INCREASING FULLNESS/SATIETY, OR MODIFYING NUTRIENT ABSORPTION. THE OVERALL GOAL OF THIS PROJECT IS TO ADVANCE A MECHANISM-BASED CLASSIFICATION FRAMEWORK THAT WILL IDENTIFY VARIATIONS IN FOOD BREAKDOWN DURING DIGESTION THAT CAN BE UTILIZED IN PROCESSING AND DESIGN OF NOVEL FOODS WITH HEALTHY BENEFITS. FOODS WILL BE PROCESSED WITH ULTRASOUND TO PRODUCE DIFFERENT STRUCTURES; IT IS HYPOTHESIZED THESE STRUCTURES WILL RESULT IN DIFFERENT CLASSES WITHIN THE CLASSIFICATION FRAMEWORK. FOOD INITIAL PROPERTIES AND BREAKDOWN DURING DIGESTION WILL BE MEASURED IN VITRO, AND WILL BE USED TO DEVELOP A DEEP LEARNING MODEL TO PREDICT FOOD CLASS BASED ON INITIAL PROPERTIES. DEVELOPMENT OF AN EXPERIMENTAL METHOD AND DEEP LEARNING MODEL WILL ALLOW FOOD MANUFACTURERS TO INCLUDE THIS INFORMATION AS PART OF THEIR PRODUCT DEVELOPMENT PROCESS, SIMILAR TO ROUTINE PROPERTY OR SENSORY TESTING. AS A RESULT, FOOD MANUFACTURERS CAN OPTIMIZE FORMULATION AND PROCESSING CONDITIONS BASED ON INFORMATION RELATED TO HEALTH BENEFITS. THIS WILL PROVIDE CONSUMERS WITH NOVEL FOODS THAT HAVE EVIDENCE-BASED HEALTHY BENEFITS.