** AWARDS ISSUED PRIOR TO JANUARY 20, 2025, WERE FUNDED UNDER PREVIOUS ADMINISTRATIONS AND MAY NOT REFLECT THE PRIORITIES AND POLICIES OF THE CURRENT ADMINISTRATION.** 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. THE PCA INCLUDES A PAPER SUBSTRATE IMPREGNATED WITH 22 CHROMOGENIC DYES, IN WHICH EXPOSURE TO VOLATILE ORGANIC COMPOUNDS RELEASED BY MICROORGANISMS OF INTEREST ELICITS COLOR CHANGES. THESE COLOR CHANGES ARE DIGITIZED AND USED TO TRAIN A MACHINE LEARNING (ML) ALGORITHM, INCLUDING A STATE-OF-THE-ART MULTI-LAYER CONVOLUTIONAL NEURAL NETWORK, GIVING IT STRAIN-SPECIFIC, HIGH-ACCURACY PATHOGEN DETECTION AND QUANTIFICATION CAPABILITIES. THE OUTPUTS OF AUTOMATED PATTERN RECOGNITION INCLUDE MICROBIAL IDENTITIES, STRAIN-SPECIFIC MICROBIAL POPULATION ESTIMATES, AND SPOILAGE STATUS OF THE FOOD SUBSTRATE. THE PROPOSED WORK INCLUDES THE DEVELOPMENT OF THE APPROACH FOR THE SEVEN MOST COMMON PATHOGENS AND SPOILAGE-CAUSING MICROBES FOR FOUR MODEL FOODS AND AN APPROACH FOR DATABASE CONSTRUCTION AND ML TRAINING THAT CAN BE EASILY EXTENDED TO OTHER MICROBIAL TARGETS AND FOOD COMMODITIES. THE SPEED, RELIABILITY, LOW COST, AND VERSATILITY OF THIS APPROACH GIVE IT THE POTENTIAL TO SIGNIFICANTLY ADVANCE NONDESTRUCTIVE IN-THE-FIELD DETECTION OF MICROBIAL CONTAMINATION IN FOOD.