Options for substantiating protein content claims for conventional foods
Bibliographic record
Abstract
In Canada and the United States, front-of-package protein content claims require data to support the quality of the protein. In general, protein quality reflects the product of the amino acid composition of the food protein relative to human amino acid requirements and a measure of digestibility. The currently accepted method in both jurisdictions is the protein digestibility-corrected amino acid score (PDCAAS) that requires the measurement of true fecal protein (nitrogen) digestibility. The latter must be measured in vivo using a rat model. This requirement for animal testing is inconsistent with international efforts to reduce the usage of animals in testing for regulatory purposes. The current commentary positions four options to remove the need to use animal testing for determining protein quality, when considering protein content claim substantiation. These options include ( i) a focus on protein quantity alone; ( ii) the use of the amino acid score alone, with no correction for digestibility; ( iii) the use of a fixed digestibility coefficient to estimate protein quality; and ( iv) the use of in vitro methods to measure protein and/or amino acid digestibility. The relative merits and deficiencies of the options are positioned with the goal of encouraging dialogue within the regulatory agencies to move towards alternative approaches for substantiating protein content claims on foods, including those derived from plant-based sources.
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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 | 0.070 | 0.113 |
| Meta-epidemiology (narrow) | 0.002 | 0.002 |
| Meta-epidemiology (broad) | 0.002 | 0.004 |
| Bibliometrics | 0.005 | 0.004 |
| Science and technology studies | 0.009 | 0.018 |
| Scholarly communication | 0.010 | 0.009 |
| Open science | 0.013 | 0.007 |
| Research integrity | 0.043 | 0.024 |
| Insufficient payload (model declined to judge) | 0.007 | 0.003 |
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".