Integration of ATR-FTIR Spectroscopy and Machine Learning for Age Prediction Model of Black Gel Pen Inks

Ink analysis provides a crucial function in forensic document examination for authentication, forgery detection, and ink dating. Today, black gel inks are widely used in both legal and non-legal documents, however they are difficult to analyse due to their pigment formulations, which undergo subtle chemical changes during ageing. Destructive techniques such as Thin Layer Chromatography (TLC) and High-Performance Liquid Chromatography (HPLC) can discriminate ink but are unsuitable for evidentiary purposes. Non-destructive Attenuated Total Reflectance-Fourier Transform infrared (ATR-FTIR) spectroscopy offers an alternative, yet spectral similarities among black gel inks necessitate advanced computational models to facilitate discrimination, classification and age prediction. In this study, predictive ageing models for black gel inks was developed by integrating ATR-FTIR spectroscopy with machine learning (ML). Thirty black gel inks from 23 brands were analysed. Ink lines made using the black gel ink samples were aged for twelve months under three different environmental conditions, and their infrared (IR) spectral data were recorded monthly over the period of 12 months. For age prediction, four classifiers namely Discriminant Analysis(DA), Support Vector Machine(SVM), k-Nearest Neighbour (kNN), and Decision Tree(DT) were trained on full mid-IR, fingerprint region, and PCA (Principal Component Analysis)-derived datasets. Performances of the classifiers were evaluated using accuracy, precision, recall, F1-score, ROC (Receiver Operating Characteristics), and Area Under the Curve (AUC). For age prediction, DA achieved the best accuracy (81.5%) with PCA features, outperforming SVM (76.1%), kNN (48.8%), and DT (40.2%). ROC-AUC values exceeded 90% across all classes. This study demonstrates that ATR-FTIR spectroscopy integrated with machine learning provides a reliable, non -destructive framework for black gel ink classification and age prediction, addressing limitations of destructive methods and strengthening forensic document analysis