首页|University of Massachusetts Reports Findings in Machine Learning (A machine lear ning-guided modeling approach to the kinetics of a-tocopherol and myricetin syne rgism in bulk oil oxidation)
University of Massachusetts Reports Findings in Machine Learning (A machine lear ning-guided modeling approach to the kinetics of a-tocopherol and myricetin syne rgism in bulk oil oxidation)
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By a News Reporter-Staff News Editor at Robotics & Machine Learning Daily News Daily News – New research on Machine Learning is th e subject of a report. According to news reporting from Amherst, Massachusetts, by NewsRx journalists, research stated, “The shelf-life and quality of food prod ucts depend heavily on antioxidants, which protect lipids from free radical degr adation. a-Tocopherol and myricetin, two potent antioxidants, synergistically en hance the prevention of oxidative rancidity in bulk oil systems. Understanding t heir degradation kinetics is essential for deepening our knowledge of their mech anisms and developing strategies to predict shelf-life before expiration.” The news correspondents obtained a quote from the research from the University o f Massachusetts, “This paper introduces a generalized mathematical model to desc ribe the degradation kinetics of atocopherol in the presence of myricetin. Usin g direct differential methods guided by a machine learning approach based on neu ral differential equations, we uncover two distinct phases of a-tocopherol degra dation when coexisting with myricetin at varying concentration ratios. These fin dings inform the development of a mixed Weibull model that accurately captures t he degradation process.”
AmherstMassachusettsUnited StatesN orth and Central AmericaCyborgsEmerging TechnologiesMachine Learning
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