A scoping review of the application of the indicator amino acid oxidation (IAAO) method for assessing the metabolic availability of amino acids in legumes
Bibliographic record
Abstract
Legumes are good source of plant-based protein, but understanding of their amino acid metabolic availability (MA) remains limited. The indicator amino acid oxidation (IAAO) method is a relatively recent approach for determining the protein quality of foods that is not yet as widely adopted as traditional methods such as fecal and ileal digestibility. This scoping review examined current literature on assessment of metabolic availability (MA) of indispensable amino acids in legumes using IAAO method. Relevant studies published in English, Malay, and Chinese were identified through three databases. Of the ten studies included, three were conducted on animals, while the remaining seven involved human participants, specifically school-aged children and adult men. Legumes examined included peas, faba beans, Amarillo peas, soy protein, chickpeas, lentils, black beans, and cereal-legume-based vegetarian meals. The MA of lysine in legumes was generally high (>80%), whereas methionine showed relatively lower availability (<80%). Although the application of IAAO is still limited in MA determination, IAAO is a valuable and reliable tool for understanding protein quality in plant-based diets and can guide strategies to enhance nutritional outcomes, particularly in populations relying heavily on legumes as a primary protein source.
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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 | 0.011 | 0.050 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.004 | 0.005 |
| Bibliometrics | 0.022 | 0.023 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.004 | 0.003 |
| Open science | 0.002 | 0.002 |
| Research integrity | 0.002 | 0.001 |
| Insufficient payload (model declined to judge) | 0.005 | 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".