Discrimination and source correspondence of black gel inks using Raman spectroscopy and chemometric analysis with UMAP and PLS-DA

Abstract

In the examination of forged documents, ink analysis plays an important role and the forensic scientist is required to opine on the origin of ink and colorants when the physical appearance is similar. Also required is to link the ink with its source as this has an important bearing in solving cases involving documents. In this study, we have tried to explore a relatively new type of writing instrument, the gel ink pen, which is commonly used by perpetrators of fraud. The favorable approach in ink analysis is the non-destructive technique such as Raman spectroscopy combined with chemometrics for objective and automated examinations. Most of the studies so far used unsupervised chemometrics for data exploration like PCA and HCA without any use of supervised methods for classification and source prediction of inks. In recent years, more complex unsupervised algorithms such as t-Distributed Stochastic Neighbor Embedding (t-SNE) and Uniform Manifold Approximation and Projection (UMAP) have emerged, which are frequently employed in big data scenarios. These strategies are also appropriate for the type of data we used in this study. Partial Least Square-Discriminant Analysis (PLS-DA) is a supervised classification technique commonly used to classify samples into known groups and predict the class of unknown samples. The performance of PLS-DA has been reported to be near perfect for a dataset of few classes, however, its applicability for large datasets remains to be explored. In this study, we report the application of PLS-DA for the classification of black gel ink samples (n ​= ​140) from 14 different brands. To demonstrate the applicability of PLS-DA in a forensic investigation involving unknown ink deposited on documents, we have tested 11 unknown samples to the trained PLS-DA model, and have achieved 91% correct classification rate. We also demonstrated misclassification due to large datasets can be mitigated by UMAP exploration and then applying PLS-DA to a reduced number of classes datasets. The procedure of using UMAP and PLS-DA may prove useful for unveiling the identity of black gel ink deposited on forged documents.