![Sample our Physical Sciences journals, sign in here to start your FREE access for 14 days]({"type":"image" , "src" : "/sda/110819/TOC-Subject-Sample-2021-Physical-Sciences.jpg"})
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.
Acknowledgments
The authors thank Dr. Xiumei Zhang and Dr. Yulu He for their thoughtful reviews of earlier drafts of this work.