On the discrimination of inkjet, laser and photocopier printed documents using Raman spectroscopy and chemometrics: Application in forensic science

The potential of Raman spectroscopy combined with chemometrics has led to the development of rapid and nondestructive discrimination of inkjet, laser and photocopier printed documents. The present study is a novel approach to determine the source of origin of unknown printed documents based on its classification into laser or others (inkjet or photocopier) devices. We report the first ever use of Raman Spectroscopy combined with the Principal Component Analysis (PCA) and Partial Least Squares Discriminant Analysis (PLS-DA) to differentiate 387 samples (90 laser, 163 ink-jet, 134 photocopier) of printed document samples. Preliminary assessment through visual inspection of Raman spectra revealed the presence of C?C (aliphatic), aromatic bond and titanium dioxide in all toner samples. Comparison through spectral library of RS identified pigment carbon black (CI77266) for laser and inkjet printout samples whilst pigment violet 19 (CI-73900) was found in photocopier samples. The unsupervised PCA correctly segregated the samples into 3 clusters and the supervised PLS-DA correctly classified them into three classes. A total of fifteen (15) unknown printed samples were correctly assigned to their source via the trained PLS-DA model. The chemometric approach used in this study showed a potential for the classification and individualisation of printed documents that can be applied to real forensic casework when a document is recovered from a crime scene during forensic investigation.

Automated summary

The combination of Raman spectroscopy and chemometrics has shown great potential in rapidly and non-destructively discriminating between inkjet, laser, and photocopier printed documents. This study successfully differentiated between different types of printed documents, accurately identifying different pigments in laser, inkjet, and photocopier print samples. The chemometric approach used in this study holds promise for classification and individualization of printed documents, with potential applications in forensic investigations at crime scenes.