首页|Researchers from University College Dublin Report Findings in Machine Learning ( Machine Learning Driven Methodology for Enhanced Nylon Microplastic Detection an d Characterization)
Researchers from University College Dublin Report Findings in Machine Learning ( Machine Learning Driven Methodology for Enhanced Nylon Microplastic Detection an d Characterization)
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By a News Reporter-Staff News Editor at Robotics & Machine Learning Daily News Daily News-A new study on Machine Learning is now available. According to news reporting originating in Dublin, Ireland, by NewsR x journalists, research stated, "In recent years, the field of microplastic (MP) research has evolved significantly; however, the lack of a standardized detecti on methodology has led to incomparability across studies. Addressing this gap, o ur current study innovates a reliable MP detection system that synergizes sample processing, machine learning, and optical photothermal infrared (O-PTIR) spectr oscopy." Funders for this research include Science Foundation Ireland, Science Foundation Ireland. The news reporters obtained a quote from the research from University College Du blin, "This approach includes examining high-temperature filtration and alcohol treatment for reducing non-MP particles and utilizing a support vector machine ( SVM) classifier focused on key wavenumbers that could discriminate between nylon MPs and non-nylon MPs (1077, 1541, 1635, 1711 cm-1 were selected based on the f eature importance of SVM-Full wavenumber model) for enhanced MP identification. The SVM model built from key wavenumbers demonstrates a high accuracy rate of 91 .33%. Results show that alcohol treatment is effective in minimizin g non-MP particles, while filtration at 70 degrees C has limited impact. Additio nally, this method was applied to assess MPs released from commercial nylon teab ags, revealing an average release of 106 particles per teabag."
DublinIrelandEuropeCyborgsEmergi ng TechnologiesMachine LearningUniversity College Dublin
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