Quantitative impurity profiling by principal component analysis of high-performance liquid chromatography-diode array detection data.
Related organic impurities generally have approximately similar molar absorption coefficients (epsilon) due to their structural similarities. On the assumption that all peaks in an impurity profiling chromatogram have approximately the same maximum molar absorption coefficients (epsilon(max)) and the chromatogram contains one major peak and several much smaller ones, all of which are completely separated, integration of the summed score vectors from the principal component analysis (PCA) decomposition of high-performance liquid chromatography-diode array detection (HPLC-DAD) data will give areas that are quantitatively proportional to the actual content of the compounds. Due to the sequential nature of PCA, the first principal component (PC) will primarily be related to the main compound and all peaks showing a similar spectrum, while the second PC will be related to the impurities with a spectrum different from the main peak. Summing the two score vectors thus makes it possible to take account of different spectra in the score chromatogram, which make the method proposed give better quantitative estimates of the impurities than any single wavelength chromatogram. Multivariate curve resolution alternating least squares (MCR-ALS) is used for comparison. The results are presented for two examples of simulated HPLC-DAD data as well as for three examples of real HPLC-DAD data from impurity profiling. The results show that integration of the score chromatograms can handle differences in the unknown epsilon(max) of the peaks and take account of the different spectra of the impurity peaks, giving quantitative estimates of the content of the impurities that closely correspond to the reference values. The results obtained are also better than integration with the best possible separate wavelength. The method could be a straightforward approach to impurity profiling in order to obtain a good estimate of the content or relative response factors of small chromatographic impurity peaks without knowledge of their molar absorption coefficients and without any precalibration.
Wiberg
Kent
K
AstraZeneca R&D Södertälje, Analytical Development, SE-151 85 Södertälje, Sweden. kent.wiberg@astrazeneca.com
D002851:Chromatography, High Pressure Liquid / Q000379:methods*; D003198:Computer Simulation; D004563:Electrochemistry; D016018:Least-Squares Analysis; D011318:Prilocaine / Q000592:standards; D025341:Principal Component Analysis / Q000379:methods; D013056:Spectrophotometry, Ultraviolet
D011318:Prilocaine