TY - JOUR
T1 - Chromatographic preprocessing of GC-MS data for analysis of complex chemical mixtures
AU - Christensen, Jan H.
AU - Mortensen, John
AU - Hansen, Asger B.
AU - Andersen, Ole
PY - 2005/1/7
Y1 - 2005/1/7
N2 - 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.
AB - 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.
KW - Chemical fingerprinting
KW - Chemometrics
KW - Chromatographic peaks
KW - Diagnostic ratios
KW - Exponential-Gaussian hybrid
KW - Gaussian peak function
KW - PCA
UR - http://www.scopus.com/inward/record.url?scp=10644297508&partnerID=8YFLogxK
U2 - 10.1016/j.chroma.2004.11.037
DO - 10.1016/j.chroma.2004.11.037
M3 - Journal article
C2 - 15679149
AN - SCOPUS:10644297508
SN - 0301-4770
VL - 1062
SP - 113
EP - 123
JO - Journal of Chromatography
JF - Journal of Chromatography
IS - 1
ER -