Temporal evolution stages classification and aging time prediction of gel-pen ink using GC-IMS combined with machine learning for forensic science applications.

Determining the temporal evolution of inks remains a critical challenge in forensic document analysis. The temporal evolution stages classification and aging time prediction of gel-pen ink were investigated by integrating gas chromatography-ion mobility spectrometry (GC-IMS) with machine learning algorithms. Ink-specific volatile markers were correlated with aging mechanisms through kinetic modeling and heatmap analysis. Three distinct temporal evolution stages were categorized into the rapid evaporation stage, slow-release stage, and chemical stabilization stage through multivariate analysis of volatiles. Furthermore, six tree-based machine learning algorithms were systematically evaluated, with the Categorical Boosting (CatBoost) model achieving superior performance (accuracy=100 %) in classifying five detailed aging stages of gel-pen ink. The decision tree regression model demonstrated high temporal prediction accuracy (test R²=0.954) through interpretable feature engineering. A stepwise strategy combining classification and regression models was proposed, enabling simultaneous ink characterization and age estimation. This methodology is expected to provide a validated approach for classifying temporal evolution stages and predicting aging time, significantly improving the efficiency of forensic analysis in judicial investigations.