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Updated: May 24, 2026

Quantitative Analysis by Thermogravimetry-Mass Spectrum Analysis for Reactions with Evolved Gases
Published on: October 29, 2018
Multivariate statistical methods for the environmental forensic classification of coal tars from former manufactured
Laura A McGregor1, Caroline Gauchotte-Lindsay, Niamh Nic Daéid
1David Livingstone Centre for Sustainability, Department of Civil and Environmental Engineering, University of Strathclyde, Glasgow, United Kingdom. l.a.mcgregor@strath.ac.uk
Abstract:
Compositional disparity within a set of 23 coal tar samples (obtained from 15 different former manufactured gas plants) was compared and related to differences between historical on-site manufacturing processes. Samples were prepared using accelerated solvent extraction prior to analysis by two-dimensional gas chromatography coupled to time-of-flight mass spectrometry. A suite of statistical techniques, including univariate analysis, hierarchical cluster analysis, two-dimensional cluster analysis, and principal component analysis (PCA), were investigated to determine the optimal method for source identification of coal tars. The results revealed that multivariate statistical analysis (namely, PCA of normalized, preprocessed data) has the greatest potential for environmental forensic source identification of coal tars, including the ability to predict the processes used to create unknown samples.
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