Physico‐chemical, structural, and functional properties of protein concentrate from selected pulses: A comparative study
Bibliographic record
Abstract
Abstract The selected pulses viz., chickpea, faba bean, red lentil, and red gram were used for the extraction of protein concentrates using alkaline extraction followed by the isoelectric precipitation. The effect of processing on moisture, protein, lipid, ash, and carbohydrate content of pulse flours and respective protein concentrates (PC) ranged between 5.05% and 13.40%; 17.10% and 84.80%; 1.09% and 5.30%; 2.24% and 3.27%; 3.37 and 60.90%, respectively. The amino acid profile of the pulse protein concentrates (PPC) was on par with that of soybean PC and meets the amino acid requirements of children and adults as per the FAO (Food and Agriculture Organization) specifications. The PC had a smaller particle size (126.70–192.70 nm) than pulse flours (251.90–301.90 nm). Water holding capacity (WHC) and oil holding capacity (OHC) of PC were higher than pulse flours. The solubility of protein concentrates was high at acidic and alkaline pH and low at pH 4.5 (isoelectric pH). The significant reduction in anti‐nutritional factors and better protein digestibility resulted from processing of pulse flours. The processing of pulses into PC will serve as a potential functional food ingredient in various food formulations. Novelty impact statement Pulses are processed for the extraction of their fractions and utilized for better food product development. In this research, pulse protein concentrate was extracted from easily available pulses for better utilization and the properties were studied for the development of PPC‐incorporated food products similar to soy protein. In the future, all pulse proteins can be used as alternative proteins for soybean to meet our nutritional requirements in a cheaper manner.
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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 | 0.001 |
| 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".