The transmission profiles of one- to three-component particle suspension mixtures were analyzed by multivariate methods such as principal component analysis (PCA) and partial least-squares regression (PLS). The particles mimic the solids present in oil-field-produced water. Kaolin and silica represent solids of reservoir origin, whereas FeS is the product of bacterial metabolic activities, and Fe3O4 corrosion product (e.g., from pipelines). All particles were coated with crude oil surface active components to imitate particles in real systems. The effects of different variables (concentration, temperature, and coating) on the suspension stability were studied with Turbiscan LAb(Expert). The transmission profiles over 75 min represent the overall water quality, while the transmission during the first 15.5 min gives information for suspension behavior during a representative time period for the hold time in the separator. The behavior of the mixed particle suspensions was compared to that of the single particle suspensions and models describing the systems were built. The findings are summarized as follows: silica seems to dominate the mixture properties in the binary suspensions toward enhanced separation. For 75 min, temperature and concentration are the most significant, while for 15.5 min, concentration is the only significant variable. Models for prediction of transmission spectra from run parameters as well as particle type from transmission profiles (inverse calibration) give a reasonable description of the relationships. In ternary particle mixtures, silica is not dominant and for 75 min, the significant variables for mixture (temperature and coating) are more similar to single kaolin and FeS/Fe3O4. On the other hand, for 15.5 min, the coating is the most significant and this is similar to one for silica (at 15.5 min). The model for prediction of transmission spectra from run parameters gives good estimates of the transmission profiles. Although the model for prediction of particle type from transmission parameters is able to predict some particles, further improvement is required before all particles are consistently correctly classified. Cross-validation was done for both models and estimation errors are reported. (C) 2009 Elsevier Inc. All rights reserved.