Raman Spectroscopy–Machine Learning Integration: Advancing High-Precision Quantitative Analysis of Na <sub>2</sub> SO <sub>4</sub> and CaCO <sub>3</sub> in Simulated Mural Surface White Pigments
Bibliographic record
Abstract
Numerous precious ancient murals have seriously degraded due to long-term environmental impact and human damage. Prolonged environmental exposure renders them susceptible to salt-induced deterioration (exfoliation and cracking), damaging their physical integrity and artistic significance. In this study, Raman spectroscopy combined with partial least squares (PLS) was proposed for quantitatively analyzing the concentrations of sodium sulfate (Na 2 SO 4 ) and calcium carbonate (CaCO 3 ) of the surface white pigment of simulated mural samples. According to the salt concentration range of authentic murals, the particle size and gelatin concentration of pigments were optimized; 30 simulated mural samples were prepared; and Raman spectra were collected. Subsequently, the PLS calibration model was optimized by different spectral pretreatment methods and variable selection methods, and the predictive performance was evaluated using multiple statistical metrics, such as high coefficient of determination ( R 2 ), low values for root-mean-square error (RMSE), mean relative error (MRE), relative standard deviation (RSD), and satisfactory residual prediction deviation (RPD). The results demonstrated that two PLS calibration models of MSC-biPLS-PLS ( R p 2 = 0.9635, RMSE p = 0.0024, MRE p = 0.0709, RSD = 4.14%, and RPD = 8.6) and MSC-siPLS-PLS ( R p 2 = 0.9891, RMSE p = 0.0125, MRE p = 0.0449, RSD = 3.59%, and RPD = 10.9) showed superior predictive performance for the quantitative analysis of Na 2 SO 4 and CaCO 3, respectively. Additionally, the recovery of the two salts in a random sample was 106.7% and 111.3%, respectively. It enhances the efficiency and accuracy of mural microregion quantitative analysis and provides innovative technical support for cultural heritage preservation.
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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 | 0.001 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| 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".