How different amino acid scoring patterns recommended by <scp>FAO</scp> / <scp>WHO</scp> can affect the nutritional quality and protein claims of lentils
Why this work is in the frame
A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.
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.
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.
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 | 0.001 | 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 it