Finally, the overlap of different sources of variation was studied. In order to improve the energy scale correction and to speed up this step of data pretreatment, a data processing method based on PCA was used. Indeed, minor shifts of energy channels lead to the PCA being performed on incorrect variables and consequently to misleading information. A second issue was encountered with spectra suffering of an even slightly inaccurate binding energy scale correction. To weaken the effect of variations of minor interest, a data normalization strategy was developed and tested. First attempts to use the method led to poor results, which showed mainly the distance between series of samples analyzed at different moments. PCA was shown to improve the identification of chemical shifts of interest and to reveal correlations between peak components.

Examples allowed highlighting the contribution of PCA to data treatment by comparing the results of this data analysis with those obtained by the usual XPS quantification methods. Given the relevance of principal component analysis (PCA) to the treatment of spectrometric data, we have evaluated potentialities and limitations of such useful statistical approach for the harvesting of information in large sets of X-ray photoelectron spectroscopy (XPS) spectra.