Automating LC–MS/MS mass chromatogram quantification: Wavelet transform based peak detection and automated estimation of peak boundaries and signal-to-noise ratio using signal processing methods.

Abstract

Background and ObjectiveWhile there are many different methods for peak detection, no automatic methods for marking peak boundaries to calculate area under the curve (AUC) and signal-to-noise ratio (SNR) estimation exist. An algorithm for the automation of liquid chromatography tandem mass spectrometry (LC–MS/MS) mass chromatogram quantification was developed and validated. MethodsContinuous wavelet transformation and other digital signal processing methods were used in a multi-step procedure to calculate concentrations of 6 different analytes. To evaluate the performance of the algorithm, the results of the manual quantification of 446 hair samples with 6 different steroid hormones by two experts were compared to the algorithm results. ResultsThe proposed approach of automating LC–MS/MS mass chromatogram quantification is reliable and valid. The algorithm returns less non-detectables than human raters. Based on signal to noise ratio, human non-detectables could be correctly classified with a diagnostic performance of AUC = 0.95. ConclusionsThe algorithm presented here allows fast, automated, reliable, and valid computational peak detection and quantification in LC–MS/MS. We provide an open source reference implementation of the proposed algorithm on the Open Science Framewok (https://osf.io/rfqkx).