The colorimetric determination of copper in wine: total copper
Bibliographic record
Abstract
Background and Aims The total concentration of copper (Cu) in wine cannot be routinely measured without relatively expensive equipment, reagents and/or without labour-intensive sample pretreatment. The following research describes an approach for the colorimetric measurement of total Cu in wine utilising a spectrophotometer and relatively cheap reagents. Method and Results The proposed method is based on using bicinchoninic acid as the complexing chromophore for Cu. Copper was measured directly and rapidly in white wine, while a digestion of red wine was required. A key step for the analysis in the non-digested white wine matrix was the addition of silver(I) to aid the dissociation of Cu in the wine from sulfide. For red and white wines, the limit of detection was 0.02 mg/L Cu, and the repeatability was 3 ± 1%. The accuracy of the method was assessed in terms of recovery experiments (102 ± 3%, n = 4), and also by comparing total Cu concentration in 84 wines determined by the colorimetric method with that obtained by inductively coupled plasma with optical emission spectroscopy (R2 = 0.95). Conclusions The colorimetric determination of total Cu was successfully optimised and validated in white and red wines. Significance of the Study The methodology will allow wineries and researchers to routinely measure total Cu concentration in wine with a spectrophotometer.
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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 | 0.001 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 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) | 0.000 | 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".