Factors Influencing the Quality of Dietary Proteins: Implications for Pulses
Bibliographic record
Abstract
Protein content has been a leading trend in product development in recent years. Similarly, a growing desire for non‐animal‐based protein sources has led to an interest in plant‐based protein such as cereals and pulses. Pulses constitute the dried seeds of nonoilseed legume crops, including dried peas, chickpeas, beans, and lentils. Their crude protein content (typically 21–26% by weight) positions pulses as plant‐based alternatives to meats within international dietary guidelines. A major consideration with respect to the inclusion of pulses in processed foods relates to the quality of the dietary protein. Protein quality is generally assessed as a function of the ability of the constituent amino acids found within the food to meet the biological needs of the consumer. Different methods exist to determine the quality of dietary proteins, each with their own advantages and disadvantages. Because preparation methods also alter the product's protein quality, these factors must also be considered. This review will discuss recent advances in the determination of protein quality and the factors that influence the quality of pulse proteins for use in human foods.
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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 | 0.001 | 0.001 |
| 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 | 0.001 | 0.001 |
| 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".