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Record W2130457075 · doi:10.5539/jfr.v1n4p174

Comparison of Chromatographic and Titrimetric Methods for the Determination of the a-amino Nitrogen in Standard Solution and Fish Protein Hydrolysates

2012· article· en· W2130457075 on OpenAlexvenueno aff
Haiyan Wang, Fenglan Zhang, Jin Cao, Qingsheng Zhang, Zhirong Chen

Bibliographic record

VenueJournal of Food Research · 2012
Typearticle
Languageen
FieldBiochemistry, Genetics and Molecular Biology
TopicProtein Hydrolysis and Bioactive Peptides
Canadian institutionsnot available
Fundersnot available
KeywordsTitrationChemistryChromatographyHydrolysateHigh-performance liquid chromatographyCorrelation coefficientCoefficient of variationFish <Actinopterygii>Relative standard deviationNitrogenDetection limitHydrolysisBiochemistryMathematicsFishery

Abstract

fetched live from OpenAlex

&lt;p&gt;Two analytical methods, reverse phase high-performance liquid chromatography (RP-HPLC) and formol titration, were compared for their accuracy and precision for the determination of a-amino nitrogen (a-AN) concentration in fish protein hydrolysates. The result showed that, when tested with pure amino acid solutions, RP-HPLC method had a better standard deviation (S. D.), coefficient of variation (C. V.), and standard error (S. E.) but a poorer recovery rate compared with formol titration technique. The observed a-AN value by RP-HPLC has good coincidence with the actual a-AN value and their correlation coefficient (r) is 0.9981, which was close to the r value (0.9995) between the a-AN by formol titration and the actual a-AN. However, when tested with protein hydrolysates, the a-AN obtained from RP-HPLC technique were significantly (p&amp;lt;0.05) lower than that from formol titration for the same sample. Therefore, the formol titration method can be used as a quick screening method to assess the quality fish hydrolystaes.&lt;/p&gt;&lt;p&gt; &lt;/p&gt;

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 imitation

Not 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.

metaresearch head score (Codex)0.004
metaresearch head score (Gemma)0.001
Version: codex-gemma-dda1882f352aValidation 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: Empirical · Consensus signal: Empirical
Teacher disagreement score0.062
Threshold uncertainty score0.125

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0040.001
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.000
Science and technology studies0.0000.000
Scholarly communication0.0000.000
Open science0.0000.000
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0000.000

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.

Opus teacher head0.075
GPT teacher head0.431
Teacher spread0.356 · 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 teacher head, not a consensus.

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

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

Citations7
Published2012
Admission routes1
Has abstractyes

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