PREPROCESSING OF CHROMATOGRAPHIC DATA FOR PRINCIPAL COMPONENT ANALYSIS

This paper examines the selection of the appropriate representation of chromatogram data prior to using principal component analysis (PCA), a multivariate statistical technique, for the diagnosis of chromatogra m data sets. The effects of four process variables were investigated; flow rate, temperature, loading concentration and loading volume, for a size exclusion chromatography system used to separate three componen ts (monomer, dimer, trimer). The study showed that major positional sh ifts in the elution peaks that result when running the separation at d ifferent flow rates caused the effects of other variables to be masked if the PCA is performed using elapsed time as the comparative basis. Two alternative methods of representing the data in chromatograms are proposed. In the first data were converted to a volumetric basis prior to performing the PCA, while in time second, having made this transfo rmation the data were adjusted to account for the total material loade d during each separation. Two datasets were analysed to demonstrate th e approaches. The results show that by appropriate selection of the ba sis prior to the analysis, significantly greater process insight can b e gained from the PCA and demonstrates the importance of pre-processin g prior to such analysis.