Source: FLORIDA STATE UNIVERSITY submitted to NRP
DEVELOPING AN AI4FOOD (ARTIFICIAL INTELLIGENCE FOR FOOD SCIENCE) RESEARCH AND EDUCATION INITIATIVE
Sponsoring Institution
National Institute of Food and Agriculture
Project Status
ACTIVE
Funding Source
OTHER GRANTS
Reporting Frequency
Annual
Accession No.
1032926
Grant No.
2024-70001-43485
Cumulative Award Amt.
$150,000.00
Proposal No.
2024-02865
Multistate No.
(N/A)
Project Start Date
Sep 1, 2024
Project End Date
Aug 31, 2026
Grant Year
2024
Program Code
[NLGCA]- Capacity Building Grants for Non Land Grant Colleges of Agriculture
Recipient Organization
FLORIDA STATE UNIVERSITY
118 N. WOODWARD AVE
TALLAHASSEE,FL 32306
Performing Department
(N/A)
Non Technical Summary
This Regular Grant project belongs to Food Sciences/Technology and Manufacturing (academic code, F). The integrated research and education initiative applies artificial intelligence (AI) to address global food safety challenges, particularly antimicrobial resistance (AMR). The overall research goal is to develop an AI-enabled portable sensor to detect AMR in food supply chains rapidly. The educational goal is to formulate a comprehensive learner-centered program, equipping students with cutting-edge AI, sensing techniques, and food safety expertise. The education plan encompasses an experiential learning-based research practicum, a novel curriculum for a summer camp, and apprenticeship-based research training for graduate and undergraduate students.
Animal Health Component
50%
Research Effort Categories
Basic
0%
Applied
50%
Developmental
50%
Classification

Knowledge Area (KA)Subject of Investigation (SOI)Field of Science (FOS)Percent
71250101100100%
Knowledge Area
712 - Protect Food from Contamination by Pathogenic Microorganisms, Parasites, and Naturally Occurring Toxins;

Subject Of Investigation
5010 - Food;

Field Of Science
1100 - Bacteriology;
Goals / Objectives
The goal is to improve our capacity to educate a diverse group of students in food science to obtain multidisciplinary knowledge and leadership in food safety.
Project Methods
This project consists of a research component and an education component. The methods of research component: (1) Design a portable microfluidic device for bacterial identification and antimicrobial resistance (AMR) testing. (2) Train machine learning algorithms for data analysis in AMR detection. (3) Validate the performance of machine learning-enabled sensors for AMR detection using meat and fresh produce as food models. The method of education component: (1) Recruit undergraduate and graduate students for the summer camp and research training. (2) Conduct an experiential learning-based research practicum for graduate students to gain expertise in artificial intelligence (AI)-based AMR detection. (3) Organize a summer camp on the topic of AI for food safety. (4) establish a research training program in AI-enabled AMR detection through an apprenticeship model among faculty mentors, graduate students, and undergraduate students.