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While there are many different methods for peak detection, no automaticmethods for marking peak boundaries to calculate area under the curve (AUC) andsignal-to-noise ratio (SNR) estimation exist. An algorithm for the automationof liquid chromatography tandem mass spectrometry (LC-MS/MS) mass chromatogramquantification was developed and validated. Continuous wavelet transformationand other digital signal processing methods were used in a multi-step procedureto calculate concentrations of six different analytes. To evaluate theperformance of the algorithm, the results of the manual quantification of 446hair samples with 6 different steroid hormones by two experts were compared tothe algorithm results. The proposed approach of automating mass chromatogramquantification is reliable and valid. The algorithm returns less nondetectablesthan human raters. Based on signal to noise ratio, human non-detectables couldbe correctly classified with a diagnostic performance of AUC = 0.95. Thealgorithm presented here allows fast, automated, reliable, and validcomputational peak detection and quantification in LC- MS/MS.

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