Oxalate Content of Egyptian Grown Fruits and Vegetables and Daily Common Herbs
Bibliographic record
Abstract
Egyptian dieticians typically rely on foreign databases to find out oxalate content of food due to unavailability of local databases. The soil, fertilizers, climate and cultivars are often very different. Therefore, the purpose of this study is to establish a local database of oxalate content in Egyptian grown fruits and vegetables and selected daily common herbs. The current study analysed the total and the soluble oxalate in 37 Egyptian grown fruits, vegetables and 9 commonly used herbs. Two methods were used for screening the Egyptian foods for oxalate concentration; the first method was AOAC 1999 and the second was enzymatic method. Total oxalate varied greatly among the vegetables examined, ranging from 4 to 917 mg/100 g F.W. Total oxalate of analysed fruits ranged from 9 to 50 mg/100 g F. W. There is a strong correlation found between the two methods used. Vegetables were classified into 4 categories; low oxalate concentration containing less than 10 mg of oxalic acid /100 g F.W., such as cabbage, courgette, cucumbers, garlic, spring onions and turnip. Moderate oxalate concentration vegetables containing 10-25 mg/100 g F. W., such as aubergine, field bean, corn, peppers and watercress. High oxalate concentration vegetables containing 26-99 mg/100g F.W., such as f?l, green beans, celery, mallow, okra and sweet potatoes. Very high oxalate concentration containing 100-900 mg/100g F.W. such as Swiss chard, molokhia, purslane and vine leaves (fresh). Extensive amounts of total oxalate (201-4014 mg/100 g D.W.) were found in daily common herbs such as caraway seed, green cardamom, cinnamon, coriander seeds, cumin, curry powder, ginger and turmeric powder.
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".