Widely targeted metabolic profiling provides insights into variations in bioactive compounds and antioxidant activity of sesame, soybean, peanut, and perilla
Bibliographic record
Abstract
Oilseeds are important sources of diversified nutraceuticals with marked health attributes. Thus, a better understanding of metabolome differences between common oilseeds will be conducive to the food pharmacy. This study aimed to compare the metabolite profiles and antioxidant activity of sesame, soybean, peanut, and perilla seeds and reveal the variation in bioactive compounds. LC-MS-based widely targeted metabolic profiling identified a total of 975 metabolites, of which 753 were common to the four crops. Multivariate analyses unveiled a crop-specific accumulation of metabolites, with 298-388 DAMs (differentially accumulated metabolites) identified. Amino acid metabolism, phenylpropanoid biosynthesis, flavonoid biosynthesis, and lipid metabolism were the most differentially regulated pathways. Furthermore, we revealed the variation in the relative content of 48, 20, 18, 9, 18, 11, and 6 differentially accumulated bioactive flavonoids, phenolic acids, amino acids, vitamins, terpenoids, alkaloids, and coumarins, respectively. Most of the flavonoids accumulated highly in soybean, followed by perilla. Sesame exhibited a better amino acid profile than other oilseeds. DPPH and FRAP assays showed that the antioxidant activity of perilla seed extracts was the highest, followed by soybean, peanut, and sesame. Our results provide data support for the comprehensive use of sesame, perilla, soybean, and peanut seeds in food, and pharmaceutical industries.
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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 | 0.000 | 0.000 |
| 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".