Fenugreek (<i>Trigonella foenum graecum</i>) seed protein isolate: extraction optimization, amino acid composition, thermo and functional properties
Bibliographic record
Abstract
BACKGROUND: With increasing demand for new protein sources, research on plant protein extraction and evaluation of the functional properties of protein isolates is necessary. In this study, pH and NaCl concentration, as two parameters affecting protein extraction of fenugreek seed, was investigated and the condition of fenugreek protein isolate (FPI) extraction was optimized using response surface methodology. RESULTS: FPI had significantly (P< 0.05) higher protein and essential amino acid content (891.00 and 387.41 g kg(-1) , respectively) compared with soy protein isolate (SPI). FPI was rich in Asp and Glu, confirming the presence of bands in the acidic region (30-39 kDa) of its electrophoretic pattern. Differential scanning calorimeter thermography of both FPI and SPI showed two peaks with high denaturation temperature, confirming the presence of high protein content and hydrophobic amino acids. Protein solubility, foaming capacity, foam stability and emulsion stability of FPI were higher than SPI; moreover, both FPI and SPI showed pH-dependent protein functionalities. CONCLUSION: Fenugreek seed protein extraction was optimized by control of pH and NaCl concentration. FPI could be used as a protein source with remarkable functional properties.
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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) | 0.001 | 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) | 0.000 | 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".