Effect of selenium biofortification on bioactive compounds and antioxidant activity in germinated black soybean
Bibliographic record
Abstract
Biofortification using inorganic selenium has become an effective strategy to enhance selenium content in crops. In the present study, the effects of selenium biofortification on the chemical composition and antioxidant capacity of black soybean (BS) during germination were studied. The contents of selenium, total sugar, vitamin C, γ-aminobutyric acid, total polyphenols, and total flavonoids in selenium biofortified germinated black soybeans (GBS-Se) significantly increased compared to germinated black soybeans (GBS). However, the contents of soluble protein, fat, and reducing sugar were decreased, while fatty acid composition was not significantly different between GBS and BS. HPLC analysis showed that 12 phenolic acids of all samples, which mainly existed in free forms. Their contents increased at low concentration of selenium and decreased along with the rise of selenium concentrations. The antioxidant activity of GBS-Se as analyzed by Pearson correlation analysis positively correlated with the accumulation of phenolic substances. Principal component analysis (PCA) showed that GBS and GBS-Se were significantly different from BS. Moreover, the physicochemical indexes of GBS showed regularly changes with increasing selenium content, and those of GBS-Se50 and GBS-Se75 were significantly different from GBS. The results provide a systematic evaluation on the effect of selenium fortification on the germination of seeds and useful information for the development of Se-enriched functional foods. PRACTICAL APPLICATION: The organic selenium black soybean (BS) produced by the germination method can be directly processed and eaten to improve human health. In addition, complexes of organic selenium, vitamin C, and γ-aminobutyric acid of germinated BS can be developed into functional substances and applied to food or health products as functional ingredient and/or natural antioxidant supplements.
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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) | 0.001 | 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) | 0.000 | 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".