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.

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