Estimation of <i>cis</i>-9, <i>trans</i>-11 conjugated linoleic acid content in UK foods and assessment of dietary intake in a cohort of healthy adults
Bibliographic record
Abstract
Dietary conjugated linoleic acid (CLA) from ruminant-derived foods may be potentially beneficial to health. The quantity of cis-9, trans-11 CLA and trans-10, cis-12 CLA in a range of UK foodstuffs (112 foods) was determined using triple-column silver ion HPLC. The cis-9, trans-11 CLA content ranged from 1.9 mg/g lipid (mild Cheddar) to 7.3 mg/g lipid (processed cheese) in cheeses, from 0.9 mg/g lipid (ice cream) to 3.7 mg/g lipid (double cream) in non-cheese dairy products, and from 2.9 mg/g lipid (Swedish meatballs) to 6.0 mg/g lipid (minced lamb) in meat products. cis-9, trans-11 CLA concentrations for chocolate and sweets ranged from 0.1 mg/g lipid (hot chocolate) to 4.8 mg/g lipid (buttermint). The trans-10, cis-12 CLA isomer was undetected or negligible in the food samples examined. To provide information about dietary cis-9, trans-11 CLA intakes in the UK, a study was performed to estimate the daily intake of CLA in a cohort of eighteen healthy volunteers (nine female and nine male; aged 21-60 years; mean BMI = 24.0 kg/m2 (sd 2.2)) with a 7-d weighed food record. This information combined with the CLA isomer contents of UK foodstuffs was used to estimate the daily intake of the cohort. The mean daily intake of cis-9, trans-11 CLA was estimated to be 97.5 (sd 73.3) mg/d. Due to its potential health benefits, it is important to determine the CLA content of food and dietary intake as these data will be useful in determining the role of CLA in health and disease.
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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 | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.001 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 0.001 |
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".