In vitro protein digestibility and physico-chemical properties of flours and protein concentrates from two varieties of lentil (Lens culinaris)
Bibliographic record
Abstract
The chemical composition of whole lentil flours and lentil protein concentrates prepared by alkaline extraction and iso-electric precipitation from Blaze and Laird varieties of lentil were studied. The protein composition of the flours and concentrates, determined by sodium dodecyl sulphate polyacrylamide gel electrophoresis (SDS-PAGE) and size-exclusion high-performance liquid chromatography (SE-HPLC) showed that the extracted proteins were composed mainly of globulins and albumins. Trypsin inhibitor activity ranged between 0.94 and 1.94 trypsin inhibitor units (TIU) mg(-1) for the flours, but was markedly lower in the protein concentrates ranging between 0.17 and 0.66 TIU mg(-1). In vitro protein digestibility ranged between 75.90 and 77.05% for the flours, whereas significantly (P < 0.05) higher values, ~82.80 to 83.20%, were determined for the concentrates. Significant (P < 0.05) differences in colour (ΔE) were observed between the flours and the concentrates from both varieties. Thermal properties of both flours as studied by differential scanning calorimetry (DSC) were comparable. However, the endothermic parameters of the two protein concentrates were significantly (P < 0.05) different. Overall, the results show that in vitro protein digestibility of lentil protein concentrates is higher than that of the flours, however, both lentil flours and protein concentrates contain useful proteins that could serve as value-added ingredients in food formulations.
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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".