Functional properties of sesame (Sesamum indicum Linn) seed protein fractions
Bibliographic record
Abstract
Abstract This work evaluated the functional properties of sesame protein fractions in order to determine their potential in food applications. Sesame seed protein fractions were prepared according to their solubility: water-soluble (albumin), salt-soluble (globulin), alkaline-soluble (glutelin) and ethanol-soluble (prolamin). Globulin was the most abundant fraction, consisting of 91% protein, followed by glutelin, albumin and prolamin in decreasing order. Non-reducing sodium dodecyl sulfate-polyacrylamide gel electrophoresis (SDS-PAGE) showed polypeptides of sizes ≥20 kDa for albumin while glutelin and globulin had similar polypeptide sizes at 19, 85 and 100 kDa. Prolamin had polypeptide sizes 20, 40 and 100 kDa. The albumin and globulin fractions had higher intrinsic fluorescence intensity (FI) values than the glutelin. Albumin had a higher solubility (ranging from 80 to 100%) over a wide pH range when compared with the other fractions. Water holding capacity (g/g) reduced from 2.76 (glutelin) to 1.35 (prolamin) followed by 0.42 (globulin) and 0.08 (albumin). Oil holding capacity (g/g) reduced from: 4.13 (glutelin) to 2.57 (globulin) and 1.56, 1.50 for albumin and prolamin respectively. Gelling ability was stronger for prolamin and glutelin than albumin and globulin, while higher emulsion ( p < 0.05) quality was obtained for prolamin and albumin than for glutelin and globulin. Graphical abstract
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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".