Effect of drying methods on the structure, thermo and functional properties of fenugreek (<i>Trigonella foenum graecum</i>) protein isolate
Bibliographic record
Abstract
BACKGROUND: Different drying methods due to protein denaturation could alter the functional properties of proteins, as well as their structure. So, this study focused on the effect of different drying methods on amino acid content, thermo and functional properties, and protein structure of fenugreek protein isolate. RESULTS: Freeze and spray drying methods resulted in comparable protein solubility, dynamic surface and interfacial tensions, foaming and emulsifying properties except for emulsion stability. Vacuum oven drying promoted emulsion stability, surface hydrophobicity and viscosity of fenugreek protein isolate at the expanse of its protein solubility. Vacuum oven process caused a higher level of Maillard reaction followed by the spray drying process, which was confirmed by the lower amount of lysine content and less lightness, also more browning intensity. ΔH of fenugreek protein isolates was higher than soy protein isolate, which confirmed the presence of more ordered structures. Also, the bands which are attributed to the α-helix structures in the FTIR spectrum were in the shorter wave number region for freeze and spray dried fenugreek protein isolates that show more possibility of such structures. CONCLUSION: This research suggests that any drying method must be conducted in its gentle state in order to sustain native structure of proteins and promote their functionalities. © 2017 Society of Chemical Industry.
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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".