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

TL;DR

This paper introduces an automated algorithm for LC-MS/MS mass chromatogram quantification using wavelet transform and signal processing, enabling reliable peak detection, boundary marking, and SNR estimation, validated against expert manual analysis.

Contribution

The paper presents a novel automated method for peak boundary detection and quantification in LC-MS/MS, outperforming manual methods in reliability and speed.

Findings

Algorithm correctly classifies non-detectables with AUC=0.95.

Automated method reduces nondetectables compared to human raters.

Fast and reliable peak detection and quantification achieved.

Abstract

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.