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Record W2009913387 · doi:10.1021/jf0713354

Optimizing Angiotensin I-Converting Enzyme Inhibitory Activity of Pacific Hake (Merluccius productus) Fillet Hydrolysate Using Response Surface Methodology and Ultrafiltration

2007· article· en· W2009913387 on OpenAlexafffund
Crystal D. Cinq-Mars, Eunice C.Y. Li‐Chan

Bibliographic record

VenueJournal of Agricultural and Food Chemistry · 2007
Typearticle
Languageen
FieldBiochemistry, Genetics and Molecular Biology
TopicProtein Hydrolysis and Bioactive Peptides
Canadian institutionsUniversity of British Columbia
FundersHospital for Sick Children
KeywordsHydrolysateChemistryHakeUltrafiltration (renal)Response surface methodologyChromatographyHydrolysisFractionationFood scienceEnzymatic hydrolysisProteaseEnzymeBiochemistryFisheryBiologyFish <Actinopterygii>

Abstract

fetched live from OpenAlex

The in vitro angiotensin I-converting enyzme (ACE) inhibitory activity of Pacific hake hydrolysates was investigated as a function of hydrolysis conditions, starting material variability, and ultrafiltration. Hake fillets were hydrolyzed using Protamex protease under various conditions of pH, hydrolysis time, and enzyme-to-substrate ratio (% E/S) according to a response surface methodology (RSM) central composite design. The hydrolysate produced at pH 6.5, 125 min, and 3.0% E/S had an IC 50 of 165 +/- 9 microg of total solids/mL. ACE-inhibitory activity was not significantly different (P < 0.05) for hydrolysates produced using higher time-enzyme combinations within the model or from fish of different catches. Ultrafiltration (10 kDa molecular mass cutoff) resulted in an IC50 value of 44 +/- 7 microg of peptides/mL, 2.5 times more potent than the commercial product PeptACE Peptides (IC50 = 114 +/- 8 microg of peptides/mL). These results suggest that hydrolysates prepared with minimal fractionation from Pacific hake, an undervalued fish, may be a commercially competitive source of ACE-inhibitory peptides.

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)0.001
metaresearch head score (Gemma)0.000
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 score0.009
Threshold uncertainty score0.518

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0010.000
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 head0.022
GPT teacher head0.246
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 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

Citations69
Published2007
Admission routes2
Has abstractyes

Explore more

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