Amino acid profiles and anti-nutritional contents of traditionally consumed six wild vegetables
Bibliographic record
Abstract
The present study was designed to evaluate the amino acid profiles and anti-nutritional contents of six wild vegetables viz.Cardamine hirsuta, Melothria perpusilla, Cryptolepis sinensis, Persicaria chinensis, Lippia javanica and Polygonum perfoliatum from Assam, India.The total amino acid detected was found the highest in P. chinensis as 25.92 mg/g dry weight (DW) followed by P. perfoliatum (19.68 mg/g DW) and M. perpusilla (14.57mg/g DW), and the lowest amino acid was observed in L. javanica (0.62 mg/g DW).However, the highest nonessential amino acids (NEAA) were detected in P. perfoliatum (3.89 mg/g DW).Among the NEAA, aspartic acid and glutamic acid were detected in all the six plant species which ranged from 0.01 to 0.33 mg/g DW and 0.02 to o.75 mg/g DW, respectively.In this study, the highest concentration of essential amino acids (EAA) was detected in C. hirsuta (1.72 mg/g DW) followed by M. perpusilla (0.95 mg/g DW) and P. chinensis (0.67 mg/g DW).Besides EAA and NEAA, some other amino acids such as phosphoserine, OH-proline, amino adipic acid, phosphoenolamine, and taurine are the most common which were detected.In the study, variable amounts of anti-nutritional contents were found and discussed herein.
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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 | 0.000 | 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".