Spectral non-destructive inspection of pigments via multivariate analysis

The inspection and identification of pigments is a critical task in forensic identification. Previously, investigators mainly analyzed them manually by comparing infrared spectra one by one, which was subject to significant subjective factors and time-consuming. This article proposes a non-destructive, fast, and accurate method for inspecting and identifying pigment evidence. In the experiment, 191 pigment samples from different brands were collected and analyzed for their infrared spectra. Multivariate scatter correction, Savitzky-Golay smoothing, and peak area normalization were used for preprocessing. Filter and multi-order derivative preprocessing were carried out, and feature extraction was performed using the CARS algorithm. Based on Fisher's discriminant, a classification model was established to distinguish between different brands of pigments accurately. The experimental results showed that infrared spectra combined with multivariate classification models could accurately identify pigment samples. The method was fast, non-destructive, and accurate, reducing inspection costs and improving efficiency. It can provide reference and guidance for inspecting and identifying relevant cases and other physical evidence inspections.