In vitro protein digestibility to replace in vivo digestibility for purposes of nutrient content claim substantiation in North America's context
Bibliographic record
Abstract
The reliance by North American regulatory authorities on in vivo rodent bioassays—Protein Correct-Amino Acid Score (PDCAAS) in the U.S. and Protein Efficiency Ratio (PER) in Canada—to measure the protein quality for protein content claim substantiation represents a major barrier for innovation in the development and marketing of protein foods. Although FAO in 2013 proposed a new method (Digestible Indispensable Amino Acid Score, DIAAS), it is still not used for protein content claim substantiation in any jurisdiction. Together with public health efforts to increase the consumption of plant-based foods, removing hurdles is key to incentivizing the food industry to measure protein digestibility in making food formulation decisions as well as in claiming protein content on product labels. To address this issue, a pathway has been proposed to position alternative methods for in vitro protein digestibility in collaborative studies to generate the data necessary for method approval by a certifying body. The latter is critical to the potential recognition of these methods by both Health Canada and the US FDA. The purpose of this article is to briefly summarize the state-of-the-art in the field, to inform the research community of next steps, and to describe the path engaging collaborative laboratories in a proficiency test as the first step in moving forward toward acceptance of in vitro digestibility methods. Throughout, a consultative and iterative process will be utilized to ensure the program goals are met. Success will be achieved when the proposed path results in the acceptance of an in vitro methods for protein digestibility used for PDCAAS determinations, which will enable increased protein analyses and improved nutrition labeling of protein foods.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 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) | 0.000 | 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 teacher head, 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".