The mechanism of freeze‐thawing induced accumulation of <i>γ</i>‐aminobutyric acid in germinated soybean
Bibliographic record
Abstract
BACKGROUND: γ-Aminobutyric acid (GABA) is a non-protein amino acid with several functions in the human body. Although freeze-thawing could effectively accumulate GABA in soybean sprouts, the mechanism has not been revealed. The mechanism by which freeze-thawing enhances GABA accumulation in germinated soybean was revealed by evaluating GABA content, the activity of related synthesis enzymes, and the microstructure of the tissues and cells of sprouts. The germinated soybeans were treated at different temperatures (from -196 °C to 25 °C) for 12 h and then thawed at 25 °C for 6 h. RESULTS: The results showed that GABA content in frozen soybean sprouts did not change significantly before thawing. After thawing, the GABA content of sprouts increased by 83.9% and 82.9% when treated by liquid nitrogen flash freeze at - 80 °C for 12 h compared with the control (4 °C treatment for 12 h). The results indicated that GABA formation mainly occurred during thawing. However, glutamate decarboxylase (GAD), diamine oxidase (DAO), and aminoaldehyde dehydrogenase (AMADH) activity decreased during thawing. Based on the malonaldehyde (MDA) content and microstructure of sprouts, it was suggested that freezing at lower temperatures (< -20 °C) maintained the integrity of the cell structure, while the tissues and cell membranes were broken during thawing. CONCLUSION: These results could provide evidence for the hypothesis that GABA formation resulted from full contact between enzymes and substrates during thawing, rather than the contribution of higher enzyme activity. © 2019 Society of Chemical Industry.
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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 | 0.001 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.001 | 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".