Chemical and functional characterization of major protein fractions extracted from nontoxic <i>Jatropha curcas</i> byproduct meals
Bibliographic record
Abstract
Abstract Jatropha curcas seeds are a suitable source of oil for biofuel, among other use. A protein‐rich meal is obtained after oilseed extraction. The goals of this study were to determine the physicochemical and functional properties of a nontoxic genotype of J. curcas defatted meal (JCDM) and the seed storage protein fractions to identify future applications. Both glutelin and globulin were the predominant protein fractions obtained from JCDM (42.03 and 20.17 g/100 g of protein, respectively). Leucine, phenylalanine + tyrosine, and histidine content of JCDM and protein fractions met the Food and Agriculture Organization/World Health Organization recommendation for children. The protein solubility (PS) profiles showed minimum values (5.3%–59.7%) at pH 5–6 and maximum at pH 2 (79.7%–81.6%) and above pH 10 (84.6%–89.8%). These findings suggest that JCDM proteins could be used in the formulation of juice or protein‐based beverages. All the proteins showed the highest values for foam expansion (231%–285%) at pH 9. JCDM and the albumin fraction formed highly stable foams at pH 9, while the globulin and glutelin foams were stable at pH 3 and 2, respectively. Protein with stable foams, like those from jatropha are suitable for application in ice cream, mousse, among others. The emulsion activity index had similar behavior as foam expansion, but did not follow a specific trend. Thus, the proteins are suitable for use in salad dressing, sausages, comminuted meats, and mayonnaise. Taken together, JCDM protein and its soluble protein fractions have strong promise as alternative proteins for food structuring.
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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".