A comparative study of the functionality and protein quality of a variety of legume and cereal flours
Bibliographic record
Abstract
Abstract Background and objectives The functionality of legume and cereal flours is difficult to compare within the literature due to the lack of standardized methodologies and differences in processing methods. The aim of this research was to investigate the functional (pasting, water/oil holding, foaming, and emulsification) attributes and protein quality of flours derived from a wide range of cereal and legume market classes (Canada) for comparative purposes. Findings Overall, legume flours (mean 1.77 g/g) had slightly higher oil holding capacities than cereal flours (mean 1.50 g/g), whereas their water hydration capacities were similar. In general, legume flours produced more foam with better stability than cereal flours. All legume flours had similar emulsifying properties, whereas for the cereals, oat flour had much lower emulsion stability (52.5%) than the other cereals examined (77.3%–97.7%). The in vitro protein digestibility‐corrected amino acid score (IV‐PDCAAS) of oat flour (62.46%) was much higher than that of wheat (~42%), whereas hull‐less barley (54.29%) was in between these values. Of the legumes studied, soybean and desi and kabuli chickpea flours had high protein quality (IV‐PDCAAS 72%–82%); red lentil was inferior to the aforementioned flours with an IV‐PDCAAS of 43.63%. Conclusions Legume and cereal flours differed mostly in terms of their oil holding, foaming properties, emulsion activity and pasting properties. Selection of a cereal or legume flour will depend on the attributes desired. Significance and novelty Information relating to various legume and cereal flour functionality and nutritional quality will enable for better ingredient selection for various food applications.
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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 | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 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".