Assessment of Proximate Chemical Composition, Nutritional Status, Fatty Acid Composition and Antioxidants of Curcumin (Zingiberaceae) and Mustard Seeds Powders (Brassicaceae)
Bibliographic record
Abstract
Curcumin is a spice grown in India, which belongs to Zingiberaceae family. Mustard seeds plant belonging to brassica family which also includes cabbage, mustards are cultivated in Canada, India, China. curcumin powder had a high content of total carbohydrates (67.91%) and low contents of fat (2.46%), fiber (4.02%) and protein (9.34%). curcumin powder is rich source of Ca, P, K and Mg as well as total phenols, flavonoids and vitamin C. Mustard seeds powder had a high content of fat (43.85%), protein (25.81%) and fiber (7.00 %) and low contents of total carbohydrates (16.38%). It is rich source of Fe, Mg and Na as well as flavonoids, lycopene and vitamin E. Curcumin and mustard seeds powders oil consisted mainly of Oleic and Linoleic acid recording ( 0.7 and 0.9g/100gm) and ( 0.9 and 0.8g/100gm); respectively. Curcumin and mustard seeds powders consisted amino acids mainly of lysine (0.05 and 0.08g/100gm protein). Curcumin and mustard seeds powders had high nutritional value due to its high dietary fiber and antioxidants compounds. The soluble fibers exert a preventative role against heart disease and lowering serum cholesterol.
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 | 0.001 | 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".