Associated Changes in the Structural and Antioxidant Activity of Myofibrillar Proteins via Interaction of Polyphenolic Compounds and Protein Extracted from Lentil (Lens culinaris)
Bibliographic record
Abstract
This study evaluated the effects of different concentrations of green lentil acetone extract (GLA) (250, 500, 750, and 1000 μg/mL) and protein of green lentil (PGL) (1, 2, 3, and 4 g/100 g MP) on the functional attributes of myofibrillar protein (MP). GLA extract and PGL significantly affected the structure of MP by decreasing the carbonyl and sulfhydryl contents. Intrinsic fluorescence quenching studies showed that static quenching was involved in MP-GLA extract and MP-PGL complexes. Compared to the control (MP), the addition of GLA extract and PGL decreased the surface hydrophobicity, which correlated with the decrease in protein solubility. The MP-GLA and MP-PGL had lower cooking losses and slightly higher water-holding capacities <a:math xmlns:a="http://www.w3.org/1998/Math/MathML" id="M1"> <a:mfenced open="(" close=")" separators="|"> <a:mrow> <a:mi>P</a:mi> <a:mo><</a:mo> <a:mn>0.05</a:mn> </a:mrow> </a:mfenced> </a:math> . FTIR spectroscopy demonstrated changes in MP secondary structure with the addition of GLA extract and PGL. GLA extract and PGL also decreased the thermal stability of MP and showed significant synergism in enhancing the radical scavenging activity of MP. Taken together, the results indicated that a high concentration of GLA extract (1000 μg/mL) and PGL (4 g/100 g MP) improved the functional properties of MP, and GLA extract was the most effective.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| 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) | 0.000 | 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 teacher head, 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".