How different amino acid scoring patterns recommended by <scp>FAO</scp> / <scp>WHO</scp> can affect the nutritional quality and protein claims of lentils
Bibliographic record
Abstract
Abstract As a nutritious pulse and protein source, lentils play an important role in the plant‐based protein market. Pulses' nutritional quality is influenced by their protein content and amino acid composition. Recommended scoring patterns by FAO/WHO can estimate protein quality for dietary assessment, but different guidelines for protein content in food labeling exist in North America. This study determined the in vitro protein digestibility (IVPD) and amino acid score (AAS) for the protein quality assessment of lentils. The impact of different recommended amino acid scoring patterns by FAO/WHO (1991, 2013) on AAS and AAS corrected for in vitro protein digestibility (AAS‐IVPDC) were evaluated. The impact of AAS‐IVPDC for determining protein content claims for lentils using USA standards was also evaluated. Sulfur AA and tryptophan were the most limiting amino acids. From this work, estimates of lentil protein quality vary with different recommended amino acid scoring patterns. IVPD in lentils was 82.6%, while mean AAS‐IVPDC values ranged from 37.5% to 64.0%. Regarding the protein content claims, if considering a similar interpretation to the protein digestibility–corrected amino acid score (PDCAAS) system (i.e., corrected content is ≥ 5.0 g per RACC), all lentil samples were considered a “good source of protein.” However, if considering a similar interpretation to digestible indispensable amino acid score (DIAAS) system (i.e., corrected content is ≥ 5.0 g per RACC and a claim threshold of 75%), no samples met these protein claims due to the arbitrary cut‐off. The criteria set for making protein content claims should be revised.
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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 | 0.005 | 0.006 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 0.001 |
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".