Recipient Organization
UNIVERSITY OF FLORIDA
G022 MCCARTY HALL
GAINESVILLE,FL 32611
Performing Department
(N/A)
Non Technical Summary
(N/A)
Animal Health Component
50%
Research Effort Categories
Basic
50%
Applied
50%
Developmental
0%
Goals / Objectives
This project seeks to develop machine learning-enabled pathogen detection platforms using nano technology. Specifically, we will develop nondestructive sensing platforms for foodborne human pathogens using bioinspired nanomaterials including photonic crystals and other nontoxic or food grade chromogenic dyes; and 2) develop machine learning algorithms to enable pathogen detection in the presence of natural background microbiome on food matrices.
Project Methods
The proposed work will advance paper chromogenic array (PCA) technology for multiplex viable pathogen detection using novel nontoxic or food grade dyes as sensing elements. Specifically, this new approach will investigate the potential of bioinspired photonic crystals derived from natural sources. The sensors will incorporate ordered nanostructures to generate constructive and destructive interference which allows for the reflection of different wavelengths in the visible spectrum. In this configuration, the sensors can undergo a specific colorimetric response upon detection of target volatile organic compounds (VOC) that are indicative of microbial pathogens in the headspace. This sensor array will be coupled with advanced machine learning (ML) algorithms for multiple objectives, including differentiating VOC categories and pathogen targets. The system will be validated using fresh produce models (such as lettuce, spinach, cantaloupe, etc.) against prominent food-borne pathogens, like E. coli O157:H7, Salmonella spp., Listeria spp., etc. in the presence of typical background microbiome.