Classification of ballpoint pen inks based on selective extraction and subsequent digital color and cluster analyses.

Abstract

Here, we propose a novel approach to the classification of blue ballpoint pen inks based on a combination of selective extraction of coloring components from a paper carrier, digital color analysis (DCA) of the remaining traces, and hierarchical cluster analysis of DCA results. Since most documents of high importance are still produced in hard copies, the proposed method, being highly time- and cost-efficient, could be a significant contribution to forensic science in the field of authenticating handwritten documents. Several commonly used solvents were applied in parallel as extractants to the replicate strokes produced by each pen. It turned out to be possible to limit the number of extractants required for an unambiguous classification to three. We have shown that the optimal descriptor for agglomerative clustering is the colorimetric distance between the original and extracted ink traces in the RGB color space. Five separate clusters of inks that are independent of sample storage temperature were obtained from a set of 16 different pens. This conclusion was further confirmed by the analysis of principal components. The developed DCA-based data processing pipeline outperformed the clustering based on the data of high-performance liquid chromatography in terms of versatility providing a more informative analysis with respect to the inks based on the phthalocyanine dyes.