首页|Stockholm University Reports Findings in Machine Learning (Closing the Organofluorine Mass Balance in Marine Mammals Using Suspect Screening and Machine Learning-Based Quantification)
Stockholm University Reports Findings in Machine Learning (Closing the Organofluorine Mass Balance in Marine Mammals Using Suspect Screening and Machine Learning-Based Quantification)
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New research on Machine Learning is the subject of a report. According to news originating from Stockholm, Sweden, by NewsRx correspondents, research stated, “High-resolution mass spectrometry (HRMS)-based suspect and nontarget screening has identified a growing number of novel per- and polyfluoroalkyl substances (PFASs) in the environment. However, without analytical standards, the fraction of overall PFAS exposure accounted for by these suspects remains ambiguous.” Our news journalists obtained a quote from the research from Stockholm University, “Fortunately, recent developments in ionization efficiency () prediction using machine learning offer the possibility to quantify suspects lacking analytical standards. In the present work, a gradient boosted tree-based model for predicting log in negative mode was trained and then validated using 33 PFAS standards. The root- mean-square errors were 0.79 (for the entire test set) and 0.29 (for the 7 PFASs in the test set) log units.” According to the news editors, the research concluded: “Thereafter, the model was applied to samples of liver from pilot whales (n = 5; East Greenland) and white beaked dolphins (n = 5, West Greenland; n = 3, Sweden) which contained a significant fraction (up to 70%) of unidentified organofluorine and 35 unquantified suspect PFASs (confidence level 2-4). -based quantification reduced the fraction of unidentified extractable organofluorine to 0-27%, demonstrating the utility of the method for closing the fluorine mass balance in the absence of analytical standards.”
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