TY - JOUR
T1 - Multivariate statistical methods for the environmental forensic classification of coal tars from former manufactured gas plants
AU - McGregor, Laura A.
AU - Gauchotte-Lindsay, Caroline
AU - Nic Daéid, Niamh
AU - Thomas, Russell
AU - Kalin, Robert M.
PY - 2012/4/3
Y1 - 2012/4/3
N2 - 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.
AB - 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.
UR - http://www.scopus.com/inward/record.url?scp=84859335696&partnerID=8YFLogxK
U2 - 10.1021/es203708w
DO - 10.1021/es203708w
M3 - Article
C2 - 22335394
AN - SCOPUS:84859335696
SN - 0013-936X
VL - 46
SP - 3744
EP - 3752
JO - Environmental Science and Technology
JF - Environmental Science and Technology
IS - 7
ER -