Abstract
Principal component analysis is applied to the detection of impure and pure zones in the chromatograms and the spectra obtained by high-performance liquid chromatography coupled with diode-array detection. Transformation of the raw data is one of the most important steps in multivariate analysis techniques. Different preprocessing methods: no transformation, column centering, column standardization, selective normalization, log column centering, log row centering, log double centering, and double closure have been applied to the data. The biplots obtained for the two first principal components are discussed and particular attention is paid to the results obtained by the logarithmic transformation methods.
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