Multistep Optimization of β-Glucosidase Extraction from Germinated Soybeans (Glycine max L. Merril) and Recovery of Isoflavone Aglycones
Bibliographic record
Abstract
Epicotyls from germinated soybeans (EGS) have great potential as sources of endogenous β-glucosidase. Furthermore, this enzyme may improve the conversion of isoflavones into their corresponding aglycones. β-Glucosidase may also increase the release of aglycones from the cell wall of the plant materials. Therefore, the aim of this work was to optimize both the extraction of β-glucosidase from EGS and to further examine its application in defatted soybean cotyledon to improve the recovery of aglycones, which were evaluated by ultra-high performance liquid chromatography (UHPLC). A multistep optimization was carried out and the effects of temperature and pH were investigated by applying a central composite design. The linear effect of pH and the quadratic effect of pH and temperature were significant for the extraction of β-glucosidase and recovery aglycones, respectively. Optimum extraction of β-glucosidase from EGS occurred at 30 °C and pH 5.0. Furthermore, the maximum recovery of aglycones (98.7%), which occurred at 35 °C and pH 7.0⁻7.6 during 144 h of germination, increased 8.5 times with respect to the lowest concentration. The higher bioaccessibility of aglycones when compared with their conjugated counterparts is well substantiated. Therefore, the data provided in this contribution may be useful for enhancing the benefits of soybean, their products, and/or their processing by-products.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| 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) | 0.000 | 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 teacher head, 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".