Production Method and Wine Style Influence Metal Profiles in Sparkling Wines
Bibliographic record
Abstract
The elemental composition of wine provides important information about origin, authenticity, and sensory considerations. Although various wine regions and varieties of still wines have been extensively studied, limited research has evaluated the metal profiles of sparkling wines, which can be produced via the bottle-fermented traditional method (TM) or the tank-fermented Charmat method (CM) in both rosé and non-rosé styles. In this study, 73 commercial sparkling wines from Canada’s Niagara Peninsula were analyzed by inductively coupled plasma-optical emission spectrometry and inductively coupled plasma-mass spectrometry to quantify 28 metal ions (silver [Ag], aluminum [Al], arsenic [As], boron [B], barium [Ba], beryllium [Be], calcium [Ca], cadmium [Cd], cobalt [Co], chromium [Cr], copper [Cu], iron [Fe], potassium [K], magnesium [Mg], manganese [Mn], molybdenum [Mo], sodium [Na], nickel [Ni], lead [Pb], antimony [Sb], selenium [Se], tin [Sn], strontium [Sr], titanium [Ti], thallium [Tl], uranium [U], vanadium [V], zinc [Zn]). All metal levels were below internationally regulated maximum limits. Higher mean levels of Cr, Ni, and Sr (0.021 ± 0.008 mg/L, 0.018 ± 0.004 mg/L, and 0.32 ± 0.07 mg/L, respectively) and lower mean levels of B (3.0 ± 0.6 mg/L) were identified in CM compared with TM wines. Cr and Ni are of particular interest because of their association with stainless steel contact during CM production. In addition, the results identified higher mean levels of K (613 ± 153 mg/L) and lower mean levels of Cu (0.034 ± 0.036 mg/L) in rosé wines compared with non-rosé style wines. These results represent the first investigation of metal content in Canadian sparkling wines and identify important elemental differences related to production technique that can inform future authenticity assessments.
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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) | 0.000 | 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".