Protein quality evaluation twenty years after the introduction of the protein digestibility corrected amino acid score method
Bibliographic record
Abstract
In 1989 the Joint FAO/WHO Expert Consultation on Protein Quality Evaluation recommended the use of the Protein Digestibility Corrected Amino Acid Score (PDCAAS) method for evaluating protein quality. In calculating PDCAAS, the limiting amino acid score (i.e., ratio of first limiting amino acid in a gram of target food to that in a reference protein or requirement) is multiplied by protein digestibility. The PDCAAS method has now been in use for 20 years. Research emerging during this time has provided useful data on various aspects of protein quality evaluation that has made a review of the current methods used in assessing protein quality necessary. This paper provides an overview of the use of the PDCAAS method as compared to other methods and addresses some of the key challenges that remain in regards to protein quality evaluation. Furthermore, specific factors influencing protein quality including the effects of processing conditions and preparation methods are presented. Protein quality evaluation methods and recommended protein intakes currently used in different countries vis-à-vis the WHO/FAO/UNU standards are further provided. As foods are frequently consumed in complement with other foods, the significance of the PDCAAS of single protein sources may not be evident, thus, protein quality of some key food groups and challenges surrounding the calculation of the amino acid score for dietary protein mixtures are further discussed. As results from new research emerge, recommendations may need to be updated or revised to maintain relevance of methods used in calculating protein quality.
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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 | 0.013 | 0.013 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.002 | 0.001 |
| Bibliometrics | 0.004 | 0.005 |
| Science and technology studies | 0.001 | 0.002 |
| Scholarly communication | 0.003 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.002 | 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".