Computational evaluation of the effect of processing on the trypsin and alpha‐amylase inhibitor from Ragi (<scp><i>Eleusine coracana</i></scp>) seed
Bibliographic record
Abstract
Ragi, also known as Finger millet, has been promoted as a healthy alternative to major cereals such as rice and wheat. It is a well‐known source of various minerals, dietary fiber, and a primary source of carbohydrate in parts of Asia and Africa. Ragi is a drought resistant crop, hence, useful for adapting to the current climate change and depleting water resource conditions in various parts of the world. However, the Ragi is known to have poor digestibility, primarily due to the presence of antinutritional compounds like alpha‐amylase and trypsin inhibitor. We have studied temperature, static electric fields (SEFs) and oscillating electric fields (OEFs) to evaluate the secondary structure changes of alpha‐amylase and trypsin inhibitor molecule in Ragi. This was simulated at three temperatures: 300, 343, and 373 K with SEF and OEF at an intensity of 1 V/nm with a frequency of 2.45 GHz (for OEF). STRIDE analysis exhibits various changes in the secondary structure of the protein, especially the loop connecting the alpha helices together. Thermal processing alone has also affected the second alpha helix of the molecule. Overall, the SEFs of 1 V/nm are found to have resulted in the most secondary structure deviations in terms of root mean square deviation and radius of gyration.
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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 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.004 | 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".