FTIR Analysis of Experimental Adhesives: Investigating Spectral Reproducibility, Chemometric Approaches, and Archaeological Applications
Discuss this preprint
Start a discussion What are Sciety discussions?Listed in
This article is not in any list yet, why not save it to one of your lists.Abstract
Reflectance-mode Fourier transform infrared (FTIR) spectroscopy is increasingly employed in archaeological residue studies, offering a non-destructive means to investigate Paleolithic adhesive technologies. This study evaluates the reproducibility and comparability of reflectance-mode FTIR spectra collected from experimental adhesives on flint substrates, analyzed across an eight-year interval using two different FTIR instruments. A comprehensive suite of natural resins, gums, glues, and admixtures was assessed to examine spectral variability introduced by instrument configuration, sample orientation, and residue composition. To evaluate classification accuracy and interpretive consistency, both analyst-defined and ingredient-defined grouping strategies were applied to processed spectra. Chemometric methods including Principal Component Analysis (PCA) and Hierarchical Clustering Analysis (HCA) were used to investigate compositional trends and clustering, supplemented by a blind validation set of pure adhesives. While key chemical features were preserved across instruments after standardized processing, minor spectral differences introduced variability in chemometric clustering. In contrast, analyst-based groupings following a Kramers-Kronig transformation remained largely consistent across instruments and sample conditions. The results highlight the value of integrating visual inspection with chemometric tools and underscore the importance of tailored preprocessing strategies, transparent classification criteria and realistic experimental references. Reflectance-mode FTIR, when paired with reproducible workflows and robust interpretive strategies, offers a promising approach for identifying archaeological adhesive residues, particularly in contexts where destructive sampling is limited.