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.

中文翻译：

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基于选择性提取和随后的数字颜色和聚类分析的圆珠笔墨水分类

在这里，我们提出了一种新的蓝色圆珠笔墨水分类方法，该方法结合了从纸载体中选择性提取着色成分、剩余痕迹的数字颜色分析 (DCA) 和 DCA 结果的层次聚类分析。由于大多数高度重要的文件仍然以硬拷贝形式制作，因此所提出的方法具有很高的时间和成本效益，可能对鉴定手写文件领域的法医学做出重大贡献。几种常用的溶剂作为提取剂平行应用于每支笔产生的复制笔画。事实证明，可以将明确分类所需的萃取剂数量限制为三种。我们已经证明，凝聚聚类的最佳描述符是 RGB 颜色空间中原始和提取墨水痕迹之间的色度距离。从一组 16 支不同的笔中获得了五组独立于样品储存温度的墨水。主成分分析进一步证实了这一结论。开发的基于 DCA 的数据处理管道在多功能性方面优于基于高效液相色谱数据的聚类，为基于酞菁染料的墨水提供了更多信息分析。主成分分析进一步证实了这一结论。开发的基于 DCA 的数据处理管道在多功能性方面优于基于高效液相色谱数据的聚类，为基于酞菁染料的墨水提供了更多信息分析。主成分分析进一步证实了这一结论。开发的基于 DCA 的数据处理管道在多功能性方面优于基于高效液相色谱数据的聚类，为基于酞菁染料的墨水提供了更多信息分析。