Species identification of animal glue adhesives on polychrome cultural heritage: an integrated analytical approach combining gel sampling, ATR-FTIR spectroscopy, and machine learning.

The accurate identification of protein adhesive sources in polychrome relics is critical for their conservation. Conventional infrared spectroscopy faces limitations in the cross-species discrimination of such adhesives. To address this, we developed a high-resolution analytical system integrating attenuated total reflection Fourier-transform infrared spectroscopy (ATR-FTIR) with multidimensional chemometrics. Spectral fingerprints (4000-500 cm-1) were acquired from simulated mural samples containing collagen-based adhesives derived from bovine, fish, porcine, and rabbit. A detailed analysis was performed in the 1800-1600 cm-1 range, which encompassed the diagnostically critical amide I band. After dimensionality reduction via principal component analysis (PCA), classification models developed based on support vector machines (SVMs), bayesian neural network (BNN), and random forest (RF) achieved cross-validated accuracies exceeding 91.8% for the four-species classification in unaged samples and >82.5% after 168 hours of UV-accelerated aging. To address the contact limitations of conventional ATR probes on non-planar surfaces, a polyethylene glycol (PEG)-based hydrogel sampling strategy was introduced, enabling the effective extraction of trace collagen signals from complex substrates. This method yielded above 86% accuracy in well-preserved samples. However, classification performance decreased significantly with extended aging, with severely degraded samples, such as those subjected to 336 hours of UV-accelerated aging, becoming indistinguishable. This work established a novel protocol that integrates minimally invasive sampling with machine learning for the species-specific identification of protein adhesives in cultural heritage. The proposed framework proves particularly effective for well-preserved relics, offering significant potential for advancing provenance studies and tailored conservation strategies.