Characterisation of Carotenoid and Total Retinol Equivalent Content in Ulam and Medicinal Species as Alternative Food Intervention to Combat Vitamin A Deficiency
Bibliographic record
Abstract
Vitamin A deficiency (VAD) is one of the continuous leading causes of children and pregnant women death. To overcome this malnutrition which currently affected one-third of the world population, there is always renewed interest in exploring numerous dietary sources rich in carotenoids which some of them serve as pre-cursors to vitamin A (pro-vitamin A). It is important that affordable staple foods be as nutritious as possible because poverty limits food access for much of the developing world’s population. Therefore, this study was aimed to explore various dietary sources for carotenoids in 28 ulam and medicinal species which are commonly consumed by the local folks. Carotenoid extraction using organic solvents was performed and analysis employed in this study through High Performance Liquid Chromatography revealed seven types of carotenoids in the food matrices; neoxanthin, violaxanthin, lutein, zeaxanthin, β-cryptoxanthin, α-carotene and β-carotene. Interestingly, these carotenoids profiles were found in varying concentration and composition in different species as well as in different period or season. Total carotenoids content quantified in all of the samples lies between 1.315 ± 0.007 to 190.301 ± 3.427 µg/g DW where cekur manis has the highest content. The total vitamin A activity (in terms of retinol equivalent, RE) of every species is also included in this study. The results suggested that at least 20 of the ulam and medicinal species may be used as alternative food intervention to eliminate VAD as a public health concern.
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 | 0.001 |
| 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) | 0.001 | 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".