MACHINE LEARNING ENABLED DETECTION OF SPOILAGE AND FOODBORNE PATHOGENS USING PAPER CHROMOGENIC ARRAYS OF DYE-IMPREGNATED POROUS NANOSILICA

We address the following priorities in USDA-NIFA-AFRI-006609 and the program of Nanotechnology for Agricultural and Food Systems (A1511): 1) Innovative ideas and fundamental sciences to develop nanotechnology-enabled solutions for food security through improved productivity, quality, and reducing food waste/loss; Enhanced food safety and biosecurity. 2) Nanotechnology-enabled smart sensors for accurate, reliable, and cost-effective early and rapid detection of pathogens and contaminants in foods. Portable, field-deployable, and agriculturally affordable sensors and devices for real-time detection and screening to identify targets requiring no additional laboratory analyses; The tools of big data. Fast and reliable pathogen detection in food is critical to public health, and in particular, in preventing foodborne illness outbreaks. Here, we propose a novel system to detect viable spoilage and pathogenic microorganisms in complex food matrices using a paper chromogenic array (PCA) enabled by machine learning (ML). There are four main goals: 1) Streamline dye selection and dye optimization processes using general-purpose mixed-integer nonlinear optimization; Standardize PCA assembly using photolithography and paper microfluidic fabrication techniques; 2) Construct a PCA database and training ML algorithm for multiplex identification of viable microbial targets; 3) Assess specificity and sensitivity of the PCA-ML platform and report training and testing accuracy; 4) Validate the PCA-ML platform as a nondestructive surveillance tool on real food models.