Differentiation of white architectural paints by microscopic laser Raman spectroscopy and chemometrics

A feasible, effective and non-destructive method that could be used to differentiate architectural paints was proposed by Microscopic laser Raman spectroscopy and chemometrics. A total of 252 white architectural paints from 7 different manufacturers were prepared for evaluating the potential of differentiating them. 5th Newton interpolation polynomial combined with Savitzky-Golay 7-point and 1st or 2nd polynomial smoothing under the 1st-order derivative were considered as the optimal pre-processing method for MLRM data. The Bayes discriminant analysis model realized 100% accuracy based on discriminant functions Z(1), Z(2) and Z(3), which was the more useful and practical method for differentiating white architectural paints than that of multilayer perceptron and radial basis function neural network models. All samples were differentiated exactly, which was rapid and non-destructive. The designed method demonstrated the potential of Microscopic Laser Raman spectroscopy in combination with pre-processing and chemometrics as a universal, confirmatory, and accurate method for the white architectural paint differentiation in forensic science. (C) 2020 Elsevier B.V. All rights reserved.

Automated summary

A feasible, effective, and non-destructive method using Microscopic Laser Raman spectroscopy and chemometrics was proposed for differentiation of architectural paints. Experimental validation showed the optimal data processing method and discriminant model, demonstrating the significant potential of this method in forensic science.