Polypeptide Profile, Amino Acid Composition and Some Functional Properties of Calabash Nutmeg (<i>Monodora myristica</i>) Flour and Protein Products
Bibliographic record
Abstract
Abstract The aim of this work was to compare the physicochemical and functional properties of calabash nutmeg (Monodora myristica) seed protein flour with those of protein‐enriched products (albumin, globulin, and protein isolate). Defatted M. myristica seed flour (MMF) was used to prepare various protein products. A NaCl extract of MMF was dialyzed against water to obtain the soluble albumin fraction (MMA) and a precipitated globulin fraction (MMG). MMF was also extracted with NaOH, the extract adjusted to pH 4.0 and the precipitated proteins collected as the isolate (MMI). Non‐reducing gel electrophoresis showed that the MMF, MMG and MMI had similar composition that was dominated by 55 and 110 kDa polypeptides while MMA consisted mainly of smaller (<35 kDa) polypeptides. However, under reducing conditions, the 110 kDa polypeptide was not observed. Amino acid composition revealed an Arg/Lys ratio that increased in the extracts (1.92, 2.28 and 2.11 for MMA, MMG and MMI, respectively) relative to that in MMF (1.85). MMA had 67.5–86.5% protein solubility in the pH 4.0–6.0 range while those of MMF, MMG, and MMI were 37.7–63.8, 2.7–69.4 and 3.8–55.1%, respectively. MMA, MMG and MMI were found to be better emulsifiers based on their smaller oil droplet sizes (8–14 μm) compared with the 14–33 μm for MMF emulsion. Maximum foaming capacity was highest for MMI (205%) when compared with MMA or MMG (150%) and MMF (89%). We conclude that protein enrichment led to significantly enhanced emulsion and foam‐forming properties but high solubility may have contributed to reduced emulsion stability.
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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 | 0.001 | 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".