Multivariate calibration of ToF-SIMS and XPS data from plasma-treated polypropylene thin films

The multivariate analysis techniques of principal components analysis (PCA), principal component regression (PCR), and partial least squares regression (PLSR) were used to calibrate time-of-flight secondary ion mass spectrometry (ToF-SIMS) data against X-ray photoelectron spectroscopy (XPS) data obtained from plasma-treated polypropylene. This establishes correlations between quantitative information obtained from XPS with the molecular information indicated by ToF-SIMS, allowing the relative concentration of CO functional groups and C:O atomic concentration ratio on the surfaces of plasma-treated polypropylene to be predicted from ToF-SIMS data alone. A four-factor prediction model was constructed, and was deemed as adequate to predict the concentrations of the surface CO functional groups, and of the C:O atomic ratio with root mean square error of prediction (RMSEP) values of 0.445 and 0.671 at%, respectively. ToF-SIMS data with semi-quantitative XPS data were correlated in a model that can be used to predict chemical properties quantitatively. A model constructed using PLSR with XPS and ToF-SIMS data obtained from the same plasma-treated polypropylene surfaces showed high calibration and prediction ability. Low RMSEP values for the concentrations of CO groups and C/O ratio were obtained using a 4-component PLSR model.