An SWIR-MIR Spectral Database of Organic Coatings Used on Historic Metals
Bibliographic record
Abstract
Surface organic coatings (SOCs) composed of drying oils, resins, and bitumen were commonly applied to small Renaissance bronze sculptures to enhance their visual and physical properties, producing dark, lustrous surfaces that were both esthetic and protective. Yet, the identification of these coatings remains challenging due to aging, conservation interventions, and the damage caused by physical sampling. This study presents a reproducible, non-destructive protocol for characterizing SOCs on metal substrates using external reflection Fourier transform infrared spectroscopy (ER-FTIR) and fiber optic reflectance spectroscopy (FORS). Twenty-seven reference coating mock-ups of linseed oil, walnut oil, mastic resin, pine resin, and bitumen were stoved onto bronze coupons and artificially aged. Spectra were analyzed across the visible/near-infrared (VIS-NIR) (~400–1000 nm), short-wave-infrared (SWIR) (~1000–2500 nm), and mid-infrared (MIR) (~2.5–25 µm) ranges, with key diagnostic features identified for each component and blend, including primary absorptions, combination bands, and overtones. ER-FTIR proved highly effective in detecting oil–resin mixtures and later wax coatings through characteristic bands in the MIR, while FORS, enhanced by first-derivative processing, successfully differentiated triterpenoid and diterpenoid resins and identified multi-component SOCs in the SWIR region. The reference spectral database generated in this study is intended to serve as a comparative tool for future non-invasive analysis of organic coatings on metal surfaces and to demonstrate that ER-FTIR and FORS, used in tandem, offer a practical and scalable framework for the non-destructive identification of SOCs.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.005 | 0.000 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one teacher head, not a consensus.
How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".