首页|University of Connecticut Reports Findings in Foodborne Diseases and Conditions (Machine learning supported single-stranded DNA sensor array for multiple foodbo rne pathogenic and spoilage bacteria identification in milk)
University of Connecticut Reports Findings in Foodborne Diseases and Conditions (Machine learning supported single-stranded DNA sensor array for multiple foodbo rne pathogenic and spoilage bacteria identification in milk)
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By a News Reporter-Staff News Editor at Robotics & Machine Learning Daily News Daily News – New research on Foodborne Diseases and Conditions is the subject of a report. According to news reporting originating from Storrs, Connecticut, by NewsRx correspondents, research stated, “Ensuring f ood safety through rapid and accurate detection of pathogenic bacteria in food p roducts is a critical challenge in the food supply chain. In this study, a non-s pecific optical sensor array was proposed for the identification of multiple pat hogenic bacteria in contaminated milk samples.” Our news editors obtained a quote from the research from the University of Conne cticut, “Fluorescencelabeled single-stranded DNA was efficiently quenched by tw o-dimensional nanoparticles and subsequently recovered by foreign biomolecules. The recovered fluorescence generated a unique fingerprint for each bacterial spe cies, enabling the sensor array to identify eight bacteria (pathogenic and spoil age) within a few hours. Four traditional machine learning models and two artifi cial neural networks were applied for classification. The neural network showed a 93.8 % accuracy with a 30-min incubation. Extending the incubati on to 120 min increased the accuracy of the multiplayer perceptron to 98.4 % .”
StorrsConnecticutUnited StatesNort h and Central AmericaCyborgsEmerging TechnologiesFood PoisoningFood Safe tyFoodborne Diseases and ConditionsMachine Learning
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