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Record W1585774853 · doi:10.1093/jaoac/88.3.877

Chromatographic Determination of Amino Acids in Foods

2005· article· en· W1585774853 on OpenAlexaff
Robert W. Peace, G. Sarwar Gilani

Bibliographic record

VenueJournal of AOAC International · 2005
Typearticle
Languageen
FieldAgricultural and Biological Sciences
TopicProbiotics and Fermented Foods
Canadian institutionsHealth Canada
Fundersnot available
KeywordsAmino acidChemistryHydrolysisDerivatizationChromatographyHydrolyzed proteinBiochemistryHigh-performance liquid chromatography

Abstract

fetched live from OpenAlex

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.

How this classification was reachedexpand

Full frame machine prediction

Teacher imitation

Not 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.

metaresearch head score (Codex)0.001
metaresearch head score (Gemma)0.003
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Bench or experimental · Consensus signal: Bench or experimental
GenreCandidate signal: Methods · Consensus signal: Methods
Teacher disagreement score0.003
Threshold uncertainty score0.010

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0010.003
Meta-epidemiology (narrow)0.0010.000
Meta-epidemiology (broad)0.0010.001
Bibliometrics0.0030.002
Science and technology studies0.0010.001
Scholarly communication0.0010.001
Open science0.0010.001
Research integrity0.0010.001
Insufficient payload (model declined to judge)0.0030.005

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.

Opus teacher head0.012
GPT teacher head0.236
Teacher spread0.224 · how far apart the two teachers sit on this one work
Validation statusscore_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from it

Classification

machine, unvalidated

Machine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.

The models applied no category: nothing in the taxonomy fit this work.
Study designBench or experimental
Domainnot available
GenreMethods

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".

Quick stats

Citations102
Published2005
Admission routes1
Has abstractyes

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