NonDestructive Discrimination of Ship Deck Paint Using Attenuated Total Reflection – Fourier Transform Infrared (ATR-FTIR) Spectroscopy with Chemometric Analysis

Abstract

A method is reported using attenuated total reflection – Fourier transform infrared (ATR-FTIR) and chemometrics analysis for the forensic discrimination of ship deck paint. The automatic baseline correction, peak area normalization, multiple scattering correction and Savitzky-Golay algorithm using smoothing were adopted to preprocess the spectral data. Several pattern recognition methods including principal component analysis (PCA), Fisher discriminant analysis (FDA), and K-nearest neighbor analysis (KNN) were adopted as the algorithms for constructing classifiers. The results showed that in the principal component analysis model, the scores of 5 brands of samples were different from each other. The derivative spectroscopy revealed hidden differences in the original spectra with improved resolution. In the Fisher discriminant analysis model, samples achieved a more ideal discrimination result. In K-nearest neighbor analysis model, 1 was selected to be the optimal K value to construct the classification model and the discrimination result was ideal. Fisher discriminant analysis was better than principal component analysis and the K-nearest neighbor analysis in the ability to discriminant between samples. It is important to use multiple indicators to evaluate and assess the classification results instead of a single indicator. The precision rate, recall rate, and F-measure may be considered except for the total accuracy in evaluation and assessment.