Chromatographic preprocessing of GC–MS data for analysis of complex chemical mixtures

Abstract

Hyphenated analytical techniques such as gas chromatography-mass spectrometry (GC-MS) can provide extensive amounts of analytical data when applied to environmental samples. Quantitative analyses of complex contaminant mixtures by commercial preprocessing software are time-consuming, and baseline distortion and incomplete peak resolution increase the uncertainty and subjectivity of peak quantification. Here, we present a semi-automatic method developed specific for processing complex first-order chromatographic data (e.g. selected ion monitoring in GC-MS) prior to chemometric data analysis. Chromatograms are converted into semi-quantitative variables (e.g. diagnostic ratios (DRs)) that can be exported directly to appropriate softwares. The method is based on automatic peak matching, initial parameterization, alternating background noise reduction and peak estimation using mathematical functions (Gaussian and exponential-Gaussian hybrid) with few (i.e. three to four) parameters. It is capable of resolving convoluted peaks, and the exponential-Gaussian hybrid improves the description of asymmetric peaks (i.e. fronting and tailing). The optimal data preprocessing suggested in this article consists of estimation of Gaussian peak parameters and subsequent calculation of diagnostic ratios from peak heights. We tested the method on chromatographic data from 20 replicate oil samples and found it to be less time-consuming and subjective than commercial software, and with comparable data quality.