Semi-supervised Clustering Algorithm for Retention Time Alignment of Gas Chromatographic Data

Abstract

Gas chromatography (GC) is an effective tool for the analysis of complex mixtures with a huge number of components. To keep tracking the chemical changes during the processes like plastic waste pyrolysis usually different sample states are profiled, but retention time drifts between the chromatograms make the comparability difficult. The aim of this study is to develop a fast and simple method to eliminate the time drifts between the chromatograms using easily accessible priori information. The proposed method is tested on GC chromatograms obtained by analysis of pyrolysis product (Mg/Y catalyst) of shredded real waste HDPE/PP/LDPE mixture. A modified k-means algorithm was developed to account the retention time drifts between samples (different sample states). The outcome of the retention time alignment is an averaged retention time for each peak from all the chromatograms which makes the comparison and further analysis (such as "fingerprinting") easier or possible.