Mid-Infrared Spectroscopy and Machine Learning for Nondestructive Detection of Inapparent Deterioration in Acrylic Waterborne Coatings for Wood

Abstract

This study presents an approach for nondestructive detection of inapparent deterioration in waterborne acrylic coatings (containing cellulose nanofibers (CNFs)) for wood by using mid-infrared spectroscopy and machine learning. The method evaluates films that mimic coatings before and after 500 h of accelerated weathering, equivalent to roughly 1 year of outdoor exposure. No noticeable transformation in film appearance is evident with a spectrophotometer following the accelerated weathering. Chemiluminescence analysis indicates oxidative degradation predominantly in the acrylic resin, an impact that the CNFs seem to mitigate. Whereas attenuated total reflectance (ATR)-Fourier transform infrared (FTIR) spectroscopy commonly identifies chemical changes in visibly degraded coatings, it does not clearly discern prior, inapparent deterioration. In this context, machine learning algorithms (such as k-nearest neighbors, decision tree, random forest (RF), and support vector machine (SVM)) categorize these nuanced changes by using the absorbance from 400 to 4000 cm⁻¹ as explanatory variables. The SVM model exhibits the highest predictive accuracy, and the RF recognizes crucial variables in some wavenumber zones. This approach has the potential for enhancing recoating schedules, cutting costs, and encouraging sustainable use of wood.