首页|Researchers from University of Illinois Discuss Findings in Escherichia coli O157:H7 (Machine Learning and Taguchi Doe Combined Approach for Modeling Dynamic Ultrasound-assisted Freshcut Leafy Green Sanitation)
Researchers from University of Illinois Discuss Findings in Escherichia coli O157:H7 (Machine Learning and Taguchi Doe Combined Approach for Modeling Dynamic Ultrasound-assisted Freshcut Leafy Green Sanitation)
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By a News Reporter-Staff News Editor at Robotics & Machine Learning Daily News Daily News-Researchers detail new data in Gram-Negative Bacteria-Escherichia coli O157:H7. According to news reporting originating in Urbana, Illinois, by NewsRx journalists, research stated, "Chlorinebased fresh produce sanitation is a dynamic process, and sanitation efficiency is limited due to chlorine degradation. Here, ultrasound was coupled with a benchtop sanitation system to enhance chlorine sanitizer efficiency in fresh-cut leafy green sanitation." Financial supporters for this research include National Institute of Food and Agriculture, AFRI Competitive Grant, United States Department of Agriculture (USDA). The news reporters obtained a quote from the research from the University of Illinois, "Taguchi design of experiments (DOE) and machine learning (ML) were combined to model the relationship between sanitation condition parameters and sanitation outcomes. Multiple ML algorithms were fitted, tuned, and compared for performance using 127 experimental trials (training-to-validation ratio = 3:1). Gaussian process regression (GPR) models showed the best performance in predicting sanitation outcomes of chemical oxygen demand (COD, R-2 = 0.73), remaining Escherichia coli O157:H7 on the leaf surface (‘Surface Microbe', R-2 = 0.88), and E. coli O157:H7 concentration in sanitation water (‘Water Microbe', R-2 = 1.00). Cut size and agitation speed were identified as the most critical input parameters. An initial free chlorine concentration over 20 mg/L was recommended to minimize the E. coli O157:H7 concentration in sanitation water. This work showcases the combined approach of ML and DOE in optimizing fresh-cut produce sanitation."
UrbanaIllinoisUnited StatesNorth and Central AmericaChlorineCyborgsEmerging TechnologiesEnterobacteriaceaeEscherichia coli O157:H7Gram-Negative BacteriaHalogensMachine LearningProteobacteriaUniversity of Illinois
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