LINEAR AND NONLINEAR MULTIVARIATE-ANALYSIS IN THE QUALITY-CONTROL OF INDUSTRIAL TITANIUM-DIOXIDE WHITE PIGMENT

In order to establish an adequate analytical system for the quality co ntrol of industrially produced titanium dioxide white pigments, two mu ltivariate linear calibration techniques, principal component regressi on (PCR) and partial least squares (PLS), are used to model the relati onship between the important pigment property, change of colour, and i ts chemical composition. The results, in terms of accuracy, precision, suitability for quality control and analysis time are compared to tho se obtained with artificial neural networks (ANNs). Two multivariate d isplay techniques, principal component analysis (PCA) and corresponden ce factor analysis (CFA) together with two hierarchical clustering tec hniques, divisive and Ward's agglomerative hierarchical clustering, ar e also applied to the X-ray fluorescence data of the pigments samples so as to extract as much information as possible. Correlation coeffici ents obtained by PCR and PLS are 0.92 and 0.94, respectively. Both of them are higher than the already achieved correlation coefficient by A NNs [I], but the precision of the model derived by ANNs is better. It should also be pointed out that some important additional information about the relations between independent variables (chemical compositio n of the pigment samples) and about the influence of different oxide c oncentrations on the pigment property, which could be used in the cont rolling of the production process, was found out.