Interpretable machine learning model to detect chemically adulterated urine samples analyzed by high resolution mass spectrometry.
Gabriel L StreunAndrea E SteuerLars C EbertAkos DobayThomas KraemerPublished in: Clinical chemistry and laboratory medicine (2021)
With interpretable retention time aligned liquid chromatography high-resolution mass spectrometry data, a reliable machine learning model could be established that rapidly uncovers chemical urine manipulation. The incorporation of our model into routine clinical or forensic analysis allows simultaneous LC-MS analysis and sample integrity testing in one run, thus revolutionizing this field of drug testing.