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

• Published in: Vibrational spectroscopy 2022-09, Vol.122, p.103428, Article 103428
• Main Authors: Zhang, Yiming, Song, Weiran, Zhao, Shangyong, Zhou, Wen, Ruan, Cheng, Wang, Hui, Wang, Zhe, Wang, Ji, Wang, Xuebao, Zhao, Min
• Format: Article
• Language: English
• Subjects: Identification; Kernel extreme learning machine; Machine learning; Partial least squares discriminant analysis; Raman spectroscopy; Transparent intumescent fire retardant coating
• ISSN: 0924-2031; 1873-3697
• DOI: 10.1016/j.vibspec.2022.103428

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. •Portable Raman spectroscopy for brand identification of intumescent fire retardant coating.•Classification accuracy of 0.978–0.993 achieved by machine learning methods.•A fast, on-situ and low-cost approach for detecting intumescent fire retardant coating.