The use of principal component analysis for the modelling of high performance liquid chromatography

Principal component analysis (PCA) was used to analyse the behaviour of a chromatographic separation as its scale increased. Three 4.6 mm diameter columns identical in every respect except for column length (25, 15 and 5 cm), were used to generate the data from a test system based on the reversed-phase HPLC separation of crude erythromycin on a polystyrene matrix (PLRP 1000) having a particle diameter of 8 mu;m and a pore diameter of 100 nm. The species were separated with an isocratic solvent composed of 45/55 acetonitrile/water at about pH 7. An experimental design technique was used to investigate the effects of four process variables (load volume, load concentration, temperature and pH of buffer) on the chromatogram shapes. Following appropriate pre-processing of the chromatographic data, subsets of critical chromatograms were selected which sufficiently characterised the entire data set. From this subset, the corresponding runs were performed on the different sized columns and principal component models were generated for each. At 5 and 15 cm a single principal component was sufficient to characterise all the variance in the chromatograms which the range of process variables introduced, but at 25 cm two principal components were required, particularly to characterise the chromatograms with small loads. Excellent correlations were observed between the first principal components at the three scales. The possibility of predicting the separations on the 25 cm column from an analysis of the separations observed at 5 cm was investigated. The study revealed that good predictions could be made at high loads (>92%) , but the model was not effective at low loads because of the need to incorporate a second principal component which was not defined by the range of variables applied to the 5 cm column.