2021/01/01 by Florian Rupprecht, Sören Enge, Rupprecht, Florian +7
Biochemistry, Genetics and Molecular Biology · Chemistry · #Analytical Chemistry and Chromatography #Applications (stat.AP) #FOS: Biological sciences #FOS: Computer and information sciences #J.3 #Metabolomics and Mass Spectrometry Studies #Quantitative Methods (q-bio.QM) #Spectroscopy and Chemometric Analyses
paper · pdf · doi:10.48550/arxiv.2101.08841
openalex publication_date 2021/01/01 · openalex created_date 2022/07/25 · openalex updated_date 2026/07/28
While there are many different methods for peak detection, no automatic\nmethods for marking peak boundaries to calculate area under the curve (AUC) and\nsignal-to-noise ratio (SNR) estimation exist. An algorithm for the automation\nof liquid chromatography tandem mass spectrometry (LC-MS/MS) mass chromatogram\nquantification was developed and validated. Continuous wavelet transformation\nand other digital signal processing methods were used in a multi-step procedure\nto calculate concentrations of six different analytes. To evaluate the\nperformance of the algorithm, the results of the manual quantification of 446\nhair samples with 6 different steroid hormones by two experts were compared to\nthe algorithm results. The proposed approach of automating mass chromatogram\nquantification is reliable and valid. The algorithm returns less nondetectables\nthan human raters. Based on signal to noise ratio, human non-detectables could\nbe correctly classified with a diagnostic performance of AUC = 0.95. The\nalgorithm presented here allows fast, automated, reliable, and valid\ncomputational peak detection and quantification in LC- MS/MS.\n