Need for Accurate and Standardized Determination of Amino Acids and Bioactive Peptides for Evaluating Protein Quality and Potential Health Effects of Foods and Dietary Supplements
Bibliographic record
Abstract
Accurate standardized methods for the determination of amino acid in foods are required to assess the nutritional safety and compositional adequacy of sole source foods such as infant formulas and enteral nutritionals, and protein and amino acid supplements and their hydrolysates, and to assess protein claims of foods. Protein digestibility-corrected amino acid score (PDCAAS), which requires information on amino acid composition, is the official method for assessing protein claims of foods and supplements sold in the United States. PDCAAS has also been adopted internationally as the most suitable method for routine evaluation of protein quality of foods by the Food and Agriculture Organization/World Health Organization. Standardized methods for analysis of amino acids by ion-exchange chromatography have been developed. However, there is a need to develop validated methods of amino acid analysis in foods using liquid chromatographic techniques, which have replaced ion-exchange methods for quantifying amino acids in most laboratories. Bioactive peptides from animal and plant proteins have been found to potentially impact human health. A wide range of physiological effects, including blood pressure-lowering effects, cholesterol-lowering ability, antithrombotic effects, enhancement of mineral absorption, and immunomodulatory effects have been described for bioactive peptides. There is considerable commercial interest in developing functional foods containing bioactive peptides. There is also a need to develop accurate standardized methods for the characterization (amino acid sequencing) and quantification of bioactive peptides and to carry out dose-response studies in animal models and clinical trials to assess safety, potential allergenicity, potential intolerance, and efficacy of bioactive peptides. Information from these studies is needed for determining the upper safe levels of bioactive peptides and as the basis for developing potential health claims for bioactive peptides. This information is, in turn, needed by regulatory agencies for developing appropriate policy and regulations on adding these substances to foods and for determining if health claims are scientifically substantiated.
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 | 0.000 |
| 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".