Comparison of methodologies used to define the protein quality of human foods and support regulatory claims
Bibliographic record
Abstract
Protein quality (PQ) is the capacity of a protein to meet the amino acid (AA) requirements of an individual. There are several methodologies for determining the PQ of foods. The protein efficiency ratio is an animal growth bioassay. The protein-digestibility-corrected AA score considers the AA requirements of a reference population, and the true nitrogen digestibility coefficient for each ingredient. The digestible indispensable AA score is based on true ileal AA digestibility and better represents bioavailability of AAs. In vitro techniques for assessment of PQ are available but require validation against a greater range of protein sources. Isotopic methods, such as the indicator AA oxidation and dual tracer techniques measure AA relative bioavailability and digestibility, respectively, but require sophisticated equipment, and may not be cost nor time effective for the industry to adopt. The present review discusses advantages and disadvantages of methodologies for determining PQ of food for humans focused on methods that are or could be adopted by regulatory agencies. Understanding the framework and resources available for PQ determination will help in the selection of appropriate methods depending on the application. Novelty Understanding the framework and resources available for PQ determination will help in the selection of appropriate methods depending on the application.
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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 | 0.004 | 0.004 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.002 | 0.001 |
| Bibliometrics | 0.006 | 0.004 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.004 | 0.002 |
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".