Effect of microwave and conventional heat treatment on trypsin inhibitor activity and in vitro digestibility of edamame milk protein
Bibliographic record
Abstract
Abstract Edamame milk is a protein‐dense milk derived from green soybeans harvested before they mature. Being a legume of soy origin, it contains antinutritional factors for example, serine protease inhibitors, which hinder its in vitro digestibility. The objective of this study was to evaluate the effect of microwave processing techniques in improving the in vitro digestibility (IVPD %) of edamame milk protein by varying processing time and temperature. Conventional and microwave‐assisted processing was employed to investigate the effect on in vitro protein digestibility (IVPD %), using temperatures 70°Ϲ, 85°Ϲ, and 100°Ϲ for 5, 10, and 15 min, respectively. Fourier‐transform infrared (FTIR) data showed microwave and conventional treatments significantly modified the Amide I region of the edamame milk protein and the extent of modification varied with variation in the treatment temperature. In the FTIR analysis β‐sheet content was observed to change little with an increase in the temperature, suggesting similarity in the surface hydrophobicity of the protein leading to similar IVPD % values for all treatment temperatures. The experiment resulted in increased in vitro digestibility with increasing time and temperature during microwave processing conditions and conventional thermal conditions. It was also observed that the trypsin inhibitor activity decreased with an increase in processing time and temperature.
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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".