  While 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. Continuous wavelet transformation
and other digital signal processing methods were used in a multi-step procedure
to calculate concentrations of six 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. The proposed approach of automating mass chromatogram
quantification is reliable and valid. The algorithm returns less nondetectables
than human raters. Based on signal to noise ratio, human non-detectables could
be correctly classified with a diagnostic performance of AUC = 0.95. The
algorithm presented here allows fast, automated, reliable, and valid
computational peak detection and quantification in LC- MS/MS.
