Chromatographic Determination of Amino Acids in Foods
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
Amino acids in foods exist in a free form or bound in peptides, proteins, or nonpeptide bonded polymers. Naturally occurring L-amino acids are required for protein synthesis and are precursors for essential molecules, such as co-enzymes and nucleic acids. Nonprotein amino acids may also occur in animal tissues as metabolic intermediates or have other important functions. The development of bacterially derived food proteins, genetically modified foods, and new methods of food processing; the production of amino acids for food fortification; and the introduction of new plant food sources have meant that protein amino acids and amino acid enantiomers in foods can have both nutritional and safety implications for humans. There is, therefore, a need for the rapid and accurate determination of amino acids in foods. Determination of the total amino acid content of foods requires protein hydrolysis by various means that must take into account variations in stability of individual amino acids and resistance of different peptide bonds to the hydrolysis procedures. Modern methods for separation and quantitation of free amino acids either before or after protein hydrolysis include ion exchange chromatography, high performance liquid chromatography (LC), gas chromatography, and capillary electrophoresis. Chemical derivatization of amino acids may be required to change them into forms amenable to separation by the various chromatographic methods or to create derivatives with properties, such as fluorescence, that improve their detection. Official methods for hydrolysis and analysis of amino acids in foods for nutritional purposes have been established. LC is currently the most widely used analytical technique, although there is a need for collaborative testing of methods available. Newer developments in chromatographic methodology and detector technology have reduced sample and reagent requirements and improved identification, resolution, and sensitivity of amino acid analyses of food samples.
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 | 0.000 | 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 it