Forensic Classification of Pigments by Attenuated Total Reflectance – Fourier Transform Infrared Spectroscopy and Chemometrics

Abstract

The rapid, nondestructive, and accurate classification of pigments in forensic science is important and indispensable. Here, a method for distinguishing different brands and types of pigments was developed by attenuated total reflectance Fourier transform infrared spectroscopy (ATR-FTIR) with chemometrics. A total of 48 pigment samples were collected, and the corresponding infrared spectra were obtained. Baseline correction, multivariate scatter correction, standard normal variable analysis and Savitzky-Golay smoothing were used to preprocess the infrared spectra. Principal component analysis (PCA), factor analysis (FA), Laplacian eigenmaps (LE) and linear discriminant analysis (LDA) were used to extract the characteristic variables of the spectra for the samples. The results were classified by Bayesian discriminant analysis (BDA) and the K-nearest neighbor (KNN) method. The results show that BDA provided a more efficient and accurate model than KNN and the overall classification accuracy was almost 100.0%. Additionally, the classification model was more accurate after extracting the characteristic variables than with the direct use of BDA or KNN. The classification accuracy of gouache and acrylic pigments was 100.0% based on BDA and characteristic variables. The classification accuracy of the BDA and PCA model was 97.2% for two types of gouache pigments and two brands of Picasso gouache pigments. The results indicate that the combination of ATR-FTIR and BDA with a dimensionality reduction method is a potential tool for the classification of different brands and types of pigments.

Keywords:

• Attenuated total reflectance Fourier transform infrared spectroscopy
• characteristic variable
• pigment classification

Additional information

Funding

This work was supported by the 2019 Basic Research Project of the People's Public Security University of China (2019JKF223).