Fatty Acids in Chinese Edible Oils: Value of Direct Analysis as a Basis for Labeling
Bibliographic record
Abstract
Edible oil is an important element in the diet of most transitional countries; nevertheless, little is known about the fatty acid composition of these oils. We examined the consumption of edible oils and the fatty acid composition of these oils obtained from a market survey conducted in seven Chinese provinces and in Beijing. Three days of measured household food intake from the 1997 China Health and Nutrition Survey households provided data on the consumption of edible oils. Edible oils sold in the capital cities of eight provinces were purchased. One hundred twenty-six samples, representing 14 different oils according to their labels, were assayed for their fatty acid content in 2001. Fatty acids were analyzed by standard gas chromatographic methods. More than 76% of households in China consume edible oil, providing an average of 29.6 g of edible oil per day to persons aged two years or older. Rapeseed was consumed by one-quarter of individuals. Rapeseed is rich in C22:1n9 cis (erucic acid). About 33% of edible oils differed from their labeled identification. Rapeseed oil, identified by the presence of C22:1n9 (erucic acid), was most frequently not labeled as such. In another 28% of the samples, trans isomers of linolenic acid were detected. Deviations from the label identification were more common in southern than in northern provinces. Regulations requiring complete labeling of mixed edible oils in China might help prevent unintentional consumption of fatty acids associated with adverse health outcomes. In particular, consumption of erucic acid and trans fatty acids might be reduced. The results suggest the need for closer control of food oil labeling in China, especially in the South.
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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 | 0.001 |
| 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".