MICROBIAL SPOILAGE OF FRUIT AND VEGETABLE JUICES IS A CHRONIC THREAT TO FOOD QUALITY AND CAUSES FOOD WASTE. YEASTS ARE THE PREDOMINANT CONTAMINANTS IN JUICES AS THEY CAN PROLIFERATE IN ACIDIC PH AND HIGH SUGAR CONDITIONS. DETECTING YEASTS IS NECESSARY TO MONITOR PROCESSING EFFICACY AND REDUCE SPOILAGE RISKS. HOWEVER, CONVENTIONAL DETECTION METHODS ARE EITHER TIME-CONSUMING OR REQUIRE SOPHISTICATED EQUIPMENT AND WELL-TRAINED PERSONNEL. WE AIM TO DEVELOP AN ARTIFICIAL INTELLIGENCE (AI)-BASED IMAGING APPROACH FOR THE EARLY DETECTION OF SPOILAGE YEASTS WITH LOW LABOR INPUTS. THE SUCCESS OF THIS STUDY WILL PROVIDE FOOD INDUSTRIES WITH A RAPID AND EASY-TO-USE MONITORING TOOL TO VERIFY AND OPTIMIZE THE EFFICACY OF PROCESSING AND SANITATION, LEADING TO BETTER PREVENTATIVE CONTROL MEASURES AND ENSURING THE MICROBIOLOGICAL QUALITY AND SAFETY OF FOOD PRODUCTS.

Oregon State University, Corvallis OR

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