Brand identification of transparent intumescent fire retardant coatings using portable Raman spectroscopy and machine learning

Abstract

Transparent intumescent fire retardant coatings (IFRC) are widely used to protect ancient buildings and high-end furniture from fire. Currently, IFRC fraud has attracted considerable attention due to the lack of efficient techniques to detect the identity of IFRC products. In this paper, we use Raman spectroscopy and machine learning to identify IFRC brands. A total of 135 transparent IFRC samples of 6 common brands were prepared and scanned using a portable Raman spectrometer. The obtained spectra were pre-processed to remove unwanted variation and classified using partial least squares discriminant analysis (PLS-DA) and kernel extreme learning machine (K-ELM). The accuracies achieved by PLS-DA and K-ELM were 0.978 and 0.993, respectively. In addition, important variables were determined based on the regression coefficients of PLS-DA. The results demonstrate that portable Raman spectroscopy combined with machine learning is a viable approach for rapid, on-situ and low-cost identification of IFRC brands.