Fast Detection and Classification of Ink by Ion Mobility Spectrometry and Artificial Intelligence

The ink can be a significant piece of evidence in the evaluation of written documents. In this work we present a new way of detection and classification of inks from ballpoint pens of different producers, using the ion mobility spectrometry (IMS) technique and artificial intelligence methods. IMS enables fast, sensitive, on-site collection of spectrometric data, which can be immediately analyzed using machine learning methods. In this study, an Advanced IMS (AIMS) instrument was employed to gather ink data, and several machine learning classifiers (including Extra Trees, Gradient Boosting, Random Forest, and Support Vector Classifier) were applied, some of which were integrated into an ensemble voting classifier for enhanced performance. The method demonstrated a high accuracy of 95,23% in classifying inks and provided promising results in estimating the age of written text without compromising the integrity of the sample. These findings highlight the potential applications of the proposed method in forensic investigations, offering a portable, efficient, and non-destructive solution for ink analysis and ageing.