Hyperspectral Raman imaging and multivariate statistical analysis for the reconstruction of obliterated serial numbers in polymers

Abstract

AbstractAs recent years have shown an increasing use of polymers for the fabrication of firearms, it is necessary to develop techniques for the reconstruction of obliterated serial numbers that are stamped in these materials. Hyperspectral Raman imaging has proven to be a suitable technique for this purpose, as it is sensitive to residual strain. The extraction of relevant information however requires an advanced two‐step fitting procedure (i.e., the identification of strain‐sensitive peaks followed by the fitting itself) that may be somewhat time consuming. In this study, principal component analysis (PCA), an exploratory method of the Raman data, is proposed to overcome this deficit. The results show that PCA offers better visual contrast in comparison to the previously reported mathematical modeling technique, as it is able to highlight pertinent variance in the original dataset, for multiple polymers, such as polycarbonate, polyethylene, nylon, and nylatron. Results obtained by limiting acquisition times and spectral ranges have displayed no significant loss of information and therefore reconstruction abilities in polyethylene. A normal density function model and receiver operating characteristic (ROC) curves have been used to show that score matrices obtained from PCA are suitable for separation of distinct strained and unstrained pixel populations. Additionally, binary images favoring minimization of the false positive rate are created to enhance observable contrast allowing for easier character recognition. Finally, a recommended routine analysis is offered to forensic scientists wanting to apply these methods in order to aid criminal investigations or trials.