Coupling ATR-FTIR spectroscopy with multivariate analysis for polymers manufacturing and control of polymers’ molecular weight

Abstract

Acrylate-based polymers are commonly used in the chemical industry. Consistent manufacturing of these polymers with the help of Process Analytical Technology (PAT) is very desirable. The capability of monitoring polymers’ molecular weight in real-time reduces operation time and eliminates the frequent samplings needed for quality control. Herein, molecular weight (Mw) of glycidyl methacrylate-co-methyl methacrylate (GMA-co-MMA) copolymer was monitored in real-time using mid-infrared ATR-FTIR spectroscopy. The Principal Component Analysis (PCA) and Partial Least Square (PLS) models were then utilized to examine, improve the latent space, and select high-quality spectra. We show that acquiring highly correlated spectra enhances the robustness of the regression model. The developed models demonstrate a satisfied R2 correlation up to ∼87%. This study suggests the potential of robust data-driven techniques for the development of predictive Mw analytics tools.

Keywords

• GMA-co-MMA
• Molecular weight
• Principal Component Analysis
• Partial Least Squares