Characterizing Organic Coatings on Renaissance Bronzes with Short-Wave Infrared and Mid-Infrared Spectroscopy
Bibliographic record
Abstract
This thesis investigates the historical composition, application, and identification of surface organic coatings (SOCs) used on small Renaissance bronze sculptures. During the Renaissance, sculptors and founders employed oil- and resin-based coatings to enhance the visual and physical properties of bronzes, producing dark, lustrous surfaces that were both aesthetic and protective. While these coatings were central to workshop practice, their material composition remains difficult to reconstruct due to limited historical documentation, degradation over time, and the practical constraints of non-invasive analysis on museum objects. To address these challenges, this research developed a reproducible protocol for characterizing SOCs using non-destructive techniques—specifically external reflection Fourier transform infrared spectroscopy (ER-FTIR) and fibre optic reflectance spectroscopy (FORS)—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. Twenty-six reference mixtures were prepared using historic materials: linseed oil, walnut oil, mastic resin, pine resin, and bitumen. These were heat-treated (“stoved”) onto prepared metal coupons to replicate the hard, insoluble coatings historically used. Artificial ageing was then applied to simulate long-term oxidation and polymerization. A spectral reference database was developed by analyzing coatings at each stage—raw, stoved, and aged—using ER-FTIR, FORS, and, selectively, pyrolysis gas chromatography–mass spectrometry (Py-GC-MS) to validate spectral interpretations. Key spectral features were identified for each material and blend. Additionally, the application of wax and shellac was explored, and unique ER-FTIR and FORS markers were determined for the identification of non-original re-coatings. This protocol was applied in situ to small bronzes from the Royal Ontario Museum: Venus after the Bath, Lion Attacking Bull, and St. Jerome. These case studies revealed that, used in tandem, ER-FTIR and FORS can narrow down plausible compositions and detect significant modifications or additions, such as waxes or resins. In all three objects, results pointed toward complex coatings composed of drying oils blended with natural resins, along with later additions of wax. Overall, this research demonstrates that the combined use of ER-FTIR and FORS, supported by a reference library, can offer meaningful insights into the composition and condition of SOCs.
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| 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) | 0.001 | 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 source (direct Gemma or distilled Codex), 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".