Rapid Decomposition of Epoxy Resins via Raman Spectrometry in Combination with Machine Learning Algorithms

Abstract

Epoxy resins are a group of important materials that have been used everywhere, and development of new materials of this kind with optimal mechanical properties from either bio-resources or industrial precursors has drawn great focus from scientists and engineers. By reacting different kinds of epoxy adhesives and curatives, massive kinds of epoxy resins with different characteristics are produced. Determination of original mixing ratio of epoxy adhesives and corresponding curatives of their curing products is useful in controlling and examining these materials. Here in this work, we described an efficient method based on Raman spectrometry and machine learning algorithms for rapid molar composition determination of epoxy resins. Original mixing ratio of epoxy adhesives and curatives could be calculated simply via Raman spectra of the products. Raman spectral data scanned during curing procedure was fed to random forest (RF) classification to calculate weights of Raman shift features and reduce data dimensionality, then spectral data of selected features were processed by partial least squares regression (PLSR) for model training and composition ratio determination. It turned out that ratio predictions of our model fit well to their actual values, with a coefficient of determination (R2) of 0.9926, and a root mean squared error (RMSE) of 0.0305.

Keywords

• epoxy adhesives
• Raman spectrometry
• decomposition
• random forest classifier
• partial least squares regression
• machine learning