Infrared Spectroscopy and Machine Learning for Classification of Red Stamp Inks on Questioned Documents

ABSTRACT This study demonstrates that integrating infrared spectroscopy with machine learning enables highly accurate, nondestructive classification of red‐stamp ink manufacturers. We evaluated five classifiers—partial least squares discriminant analysis (PLS‐DA), k‐nearest neighbor (k‐NN), support vector machine (SVM), random forest (RF), and a feed‐forward neural network (FNN)—across multiple spectral regions. The FNN trained on second‐derivative spectra in the 1700–900 cm −1 region achieved perfect test metrics (F1 = 1.000; AUC = 1.000), while PLS‐DA and RF also performed robustly (F1 ≥ 0.933). Variable importance in projection (VIP) analysis identified the 1650–1100 cm −1 subrange as most informative, streamlining feature selection and model training. Applied to three unknown samples, the optimized FNN produced high‐confidence manufacturer predictions consistent with expected origins. These results confirm that targeted spectral selection combined with derivative preprocessing markedly enhances nondestructive ink classification for forensic applications.