In this work, scanning electron micrographs of copolymer beads based on methyl methacrylate (MMA), ethylene glycol dimethacrylate (EGDM) and vinyl triethoxysilane (VTES) were used to construct multivariate image regression models. Scanning electron microscopy (SEM) and swelling measurements indicated that increasing the silicone concentration (VTES) of the copolymers enhances the amount of pores and porosity percent. Scanning electron micrographs of the synthesized copolymers with different VTES concentrations were used to construct two soft models, which were able to relate the VTES concentration and the porosity percent of the copolymers with their SEM images. The constructed models were the three-way partial least squares analysis (N-PLS) and unfolded principal component regression (unfold-PCR). The predictive ability of the constructed models were evaluated by a test set. The results showed the models were able to effectively predict the VTES concentration and the porosity percent of the copolymers using their corresponding SEM images.
copolymers; scanning electron micrographs; N-PLS; unfold-PCR
