Comparison between visual assessments and different variants of linear discriminant analysis to the classification of Raman patterns of inkjet printer inks

• NCJ Number: 304026
• Journal: Forensic Chemistry Volume: 24 Issue: 100336 Dated: 2021
• Author(s): P. Buzzini; et al
• Date Published: 2021
• Grant Number(s): 2016-DN-BX-0164
• Agencies: NIJ-Sponsored

Annotation

This article reports on the use of Raman spectroscopy to analyze the three main colored dot components (cyan, magenta, and yellow) of inkjet printed documents.

Abstract

Inkjet printers are devices that are frequently used in illicit activities like threats, extorsions, or the production of counterfeited currency. In these cases, scientific information may play a critical role in developing investigative leads and assist investigators with the designation of potential printer source candidates. In the current study, three variants of linear discriminant analysis (LDA) were evaluated on 231 Raman spectra of a set of 11 inkjet printer ink samples, which were previously compared visually. The three LDA variants were: 1) Principal component analysis (PCA) followed by LDA, 2) Partial least square discriminant analysis (PLSDA) , and 3) sparse LDA. The classification accuracy of the selected classifiers was evaluated, as well as their detection sensitivity to the level of detail that analysts should typically use during spectral visual comparisons. These evaluations were made for each dot color and also for their joint use. Results showed that although spectral visual comparisons are still superior to differentiate Raman spectra on the basis of minor peaks, sparse LDA provided the highest classification potential (i.e., highest accuracy) for individual colors, and that the three methods performed equivalently when the spectral data from the three colors were combined. It was also determined that baseline correction was not a factor that affected the performance of the used classifiers, but it was found that normalization was a necessary step prior to data analysis. (publisher abstract modified)