Comparative phytochemical profiling of different soybean ( Glycine max (L.) Merr) genotypes using GC–MS
Bibliographic record
Abstract
(L.) Merr) genotypes to explore their nutritional and medicinal values. Crude protein composition ranged between 35.63 and 43.13% in Argentinian and USA (Clark) genotypes, respectively. Total phenolic content varied from 1.15 to 1.77 mg GAE/g, whereas flavonoids varied from 0.68 to 2.13 mg QE/g. The GC-MS analysis resulted identification of 88 compounds categorized into aldehydes (5), ketones (13), alcohols (5), carboxylic acids (7), esters (13), alkanes (2), heterocyclic compounds (19), phenolic compound (9), sugar moiety (7) ether (4) and amide (3), one Alkene and one fatty acid ester. Indonesian genotypes (Ijen and Indo-1) had the highest phenolic compounds than others genotype having antioxidant activities, while the Australian genotype contains the maximum in esters compounds. The major phytocompounds identified in majority of genotypes were Phenol, 2,6-dimethoxy-, 2-Methoxy-4-vinylphenol, 3,5-Dimethoxyacetophenone, 1,2-cyclopentanedione and Hexadecanoic acid, methyl ester. The presence of phytochemicals with strong pharmacological actions like antimicrobial and antioxidants activities could be considered as sources of quality raw materials for food and pharmaceutical industries. This study further set a platform for isolating and understanding the characteristics of each compound for it pharmacological properties.
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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".