Nutritional composition of commonly consumed composite dishes from rural villages in <scp>E</scp>mpangeni, <scp>K</scp>wa<scp>Z</scp>ulu‐<scp>N</scp>atal, <scp>S</scp>outh <scp>A</scp>frica
Bibliographic record
Abstract
BACKGROUND: Accurate nutrient composition data for composite dishes unique to a population is essential for the development of a nutrient database and the calculation of dietary intake. The present study aimed to provide the nutritional composition of composite dishes frequently consumed in rural KwaZulu-Natal, South Africa. METHODS: Commonly consumed composite dishes were identified using 24-h recalls collected from 79 randomly selected community members. Multiple recipes were collected for each reported dish. The mean nutritional composition of each dish was calculated per 100 g using the nutribase clinical nutrition manager (Cybersoft Inc., Phoenix, AZ, USA). RESULTS: A total of 79 recipes were collected for 16 commonly consumed dishes (seven meat-based, five starch-based and four legume/vegetable-based). 'Fried chicken' contained the most energy [1469 kJ (351 kcal)], protein (29.7 g), fat (23.7 g), cholesterol (123 mg) and niacin (8.4 mg). 'Fried beef' contained the most potassium (495 mg) and zinc (6.4 mg), whereas 'fish stew' had the most vitamin D (4.2 μg) and calcium (215 mg). 'Fried cabbage' and 'fried spinach' contained the largest percent energies from fat, at 79% and 76%, respectively. A traditional sweet bread, 'jeqe', made with fortified flour contributed significantly to iron (4.6 mg), niacin (4.5 μg) and folate (129 μg). The sodium content of dishes ranged from 88 to 679 mg per 100 g. CONCLUSIONS: The nutritional composition data for commonly consumed dishes in rural KwaZulu-Natal is presented. Although the dishes are good sources of protein, vitamins and minerals, they also contain substantial amounts of fat. This culturally appropriate information will enable the calculation of dietary intake and can be used to encourage the consumption of recipes rich in key nutrients.
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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 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 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) | 0.002 | 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".