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Published:Journal of Chromatographic Science,
ISSN 0021-9665 Volume 45,
Number 4, April 2007, pp.169-176
Peak Alignment and Robust Principal Component Analysis
of Gas Chromatograms of Fatty Acid Methyl Esters and Volatiles
Stina Frosch Møller, Bo M. Jørgensen
Danish Institute for Fisheries Research, DTU Build. 221, DK-2800
Kgs. Lyngby, Denmark
Gas chromatograms of fatty acid methyl esters and
of volatile lipid oxidation products from fish lipid extracts
are analyzed by multivariate data analysis [principal component
analysis (PCA)]. Peak alignment is necessary in order to include
all sampled points of the chromatograms in the data set. The ability
of robust algorithms to deal with outlier problems, including
both sample-wise and element-wise outliers, and the advantages
and drawbacks of two robust PCA methods, robust PCA (ROBPCA) and
robust singular value decomposition when analysing these GC data
were investigated. The results show that the usage of ROPCA is
advantageous, compared with traditional PCA, when analysing the
entire profile of chromatographic data in cases of sub-optimally
aligned data. It also demonstrates how choosing the most robust
PCA (sample or element-wise) depends on the type of outliers present
in the data set.
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