Modern methods for determining the time of document production

introduction: this work is devoted to modern methods of establishing the time of document production and bridges the gap between traditional physico?chemical approaches and new non?destructive and data?driven technologies. The relevance is determined by the high proportion of court disputes where confirmation of the true date of a record, printout, or stamp imprint is crucial, while the scientific novelty lies in the systematic comparison of chemical, optical, spectral, and AI chemometric techniques with an assessment of their validity and forensic admissibility. The aim is to classify approaches, clarify metrological capabilities and limitations, and propose principles for comprehensive application in expert practice. Materials and Methods. An analytical review and critical meta?analysis of publications, standards, and patent solutions was performed, comparing: gas chromatography–mass spectrometry of volatile ink components; thin?layer chromatography and spectrophotometry of dyes; optical?spectral techniques (VIS/UV, Raman, IR, XRF); radiocarbon measurements of paper based on the “bomb peak” of 14C; as well as chemometric and machine?learning models on GC?IMS, spectral, and image data. The methodology includes criteria of reproducibility, applicability boundaries, calibration against samples of known age, and evaluation of storage condition effects. Results. It was shown that solvent analysis (e.g., phenoxyethanol) is most informative for “fresh” entries in the 2-5 year range, allowing differentiation of aging intervals within a known error margin; optical and spectral methods are valuable as non?destructive identifiers of composition and degree of photodegradation but mainly provide relative estimates; radiocarbon is accurate for paper of the second half of the 20th century, but dates the medium rather than the act of inscription; for toner printing, dynamic dating is limited, whereas static identification of technology and materials reveals inconsistencies with the claimed dates; integration of GC?IMS with ML and digital color analysis increases sensitivity and classification accuracy. Combining channels reduces error risk and enhances evidential strength of conclusions.

forensic document examination, ink dating, gas chromatography–mass spectrometry, spectroscopy, machine learning