Comparative Study of the Effects of Processing on the Nutritional, Physicochemical and Functional Properties of Lentil
Bibliographic record
Abstract
The effects of processing (dry-milling, cooking, isoelectric precipitation) on the physicochemical, functional and nutritional properties of lentil were evaluated. Protein, moisture, lipid and ash contents of raw lentil flour (RLF), cooked lentil flour (CLF) and lentil protein isolate (LPI) ranged between 29.2 and 90.6%; 0.5 and 6.7%; 0.1 and 0.7%; and 2.4 and 3.4%, respectively. LPI contained smaller particles with narrower size distribution than CLF or RLF. RLF contained less lysine but had more determined sulfur-containing amino acids than the CLF. LPI and CLF, respectively, showed the highest and lowest solubility between pH 1 and 12. Water holding and fat absorption capacities were highest for LPI followed by CLF and RLF. Circular dichroism and FTIR spectroscopy showed minimal secondary structural changes in RLF and LPI compared with CLF. Anti-nutritional factors content and thermal properties revealed distinct variations between the two flours and protein isolate. Processing of lentils could be explored to modify its functionality for various food applications. Practical Applications This article presents simple processing methods (dry-milling, cooking and isoelectric precipitation) to modify and obtain value-added lentil products with improved physicochemical, functional and nutritional characteristics. Such processing approaches could markedly influence the value of lentil, diversify it use, and help to improve the competitiveness of the pulse sector. Research Highlights Milling, cooking and protein extraction modified the properties of lentil The protein-rich lentil isolate had low phytic acid and trypsin inhibitor content Protein solubility, water holding and fat absorption were improved in the isolate Amino acid content and quality of protein were highest in the protein isolate
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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".