Desenvolvimento de metodologia analítica baseada em ft-nir e quimiometria para caracterização e monitoramento de monômeros acrílicos na indústria de resinas e espessantes

Abstract

The resin and thickener industries are essential for sectors such as paints, coatings, adhesives, and cosmetics, requiring formulations that ensure properties like viscosity and resistance. In this context, acrylic monomers, such as ethyl acrylate, butyl acrylate, and 2-ethylhexyl acrylate, are fundamental raw materials, directly affecting the final quality of resins and thickeners. The quality of these inputs, including purity, optimal inhibitor range, and contaminant control, directly impacts the performance of final products. This study proposes the development of an analytical methodology based on Fourier transform near-infrared spectroscopy (FT-NIR), combined with chemometric tools, for process control and monitoring in the industrial use of acrylate monomers. Initially, principal component analysis (PCA) was used to identify and classify monomers, applying Mahalanobis distance as the classification criterion. For the construction of a model to determine the purity and concentration of the inhibitor hydroquinone monomethyl ether (MEHQ), partial least squares (PLS) regression was applied, evaluating the best combinations of spectral preprocessing techniques and latent variables. Normal spectra, first derivative, and second derivative spectra, adjusted by the Savitzky-Golay method, were investigated to optimize values for the coefficient of determination (R²), root mean square error of calibration (RMSEC), and root mean square error of prediction (RMSEP). The optimized PLS models demonstrated high performance, with coefficients of determination (R²) exceeding 0.9 and RMSEC and RMSEP errors close to 9%. PCA analysis explained 96,7% of the total variance using only three principal components, highlighting the model’s efficiency.