Histamine in Australian wines—a survey between 1982 and 2009
Bibliographic record
Abstract
Biogenic amines are found in a range of fermented foods and beverages, including wine. Absorption of these compounds in elevated concentrations may induce headaches, gastro-intestinal and respiratory distress. The main biogenic amines found in wine are histamine, tyramine, cadaverine and putrescine. Even though concentrations of histamine in wine are generally ten-fold lower than found in some fresh and other fermented foods, their presence may contribute to an adverse reaction when consumed in combination with other histamine-containing foods. It is well established that the main contribution of biogenic amines in wines is from lactic acid bacteria metabolism, especially during or after malolactic fermentation (MLF). A survey for histamine content of Australian red and white wines produced during 1982–1990 demonstrated a wide range of concentrations (mean 1.58 and 0.21 mg/L, respectively). A second survey of histamine content in red and white wines produced during 2003–2009 (mean 1.75 and 0.59 mg/L, respectively) showed that there were minimal changes in the mean histamine concentration over the period of the two sets of wines. All 238 Australian wines from 1982–1990 and 99 of 100 wines from 2003–2009 were below the former regulatory recommended limit of 10 mg/L for histamine in wine and were low compared to other wine-producing countries. Seven other biogenic amines measured in the Australian wines from 2003–2009 also had low means compared to other wine-producing countries.
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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 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.001 |
| 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".