Individual and Total Sugar Contents of 83 Malaysian Foods
Bibliographic record
Abstract
As part of the effort in updating and expanding the carbohydrate data in Malaysian Food Composition Databases, 83 foods were selected based on the most commonly consumed foods and food products by Malaysian. The samples include 31 cereal products, 9 starchy roots and tubers products, 4 legume products, 11 nut and seed products, 4 vegetables, 5 fruits, 15 sugar and syrup products, 2 meat products and 2 oil and fat products. Individual sugars (fructose, glucose, sucrose, lactose and maltose) were analysed usingHigh Performance Liquid Chromatography with Refractive Index Detector. Most of the cereal products contained sucrose, glucose, fructose, lactose and maltose. Four starchy root and tuber products contained sucrose, glucose and fructose. Sucrose was detected in all legume, nut and seed products. Most vegetables contained fructose while all fruits contained glucose and fructose. In addition, all syrups contained sucrose except for kiwi and lime cordial. Overall, sugar and syrup products contained the highest total sugar content (15.00-65.52 g/ 100g) while vegetables were the lowest for total sugar content (2.74-4.83 g/ 100g).
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.002 | 0.002 |
| 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) | 0.001 | 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".