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Record W2780401934 · doi:10.1111/jfbc.12489

Reactivity of peptides within the food matrix

2017· review· en· W2780401934 on OpenAlexafffund
Jean Paul Kamdem, Apollinaire Tsopmo

Bibliographic record

VenueJournal of Food Biochemistry · 2017
Typereview
Languageen
FieldBiochemistry, Genetics and Molecular Biology
TopicProtein Hydrolysis and Bioactive Peptides
Canadian institutionsCarleton University
FundersConselho Nacional de Desenvolvimento Científico e TecnológicoNatural Sciences and Engineering Research Council of CanadaCoordenação de Aperfeiçoamento de Pessoal de Nível Superior
KeywordsChemistryPeptideBiochemistryAntioxidantBioavailabilityAmino acidAntimicrobial peptidesPharmacologyBiology

Abstract

fetched live from OpenAlex

Numerous biological activities have been reported for peptides or peptide-rich fractions from hydrolyzed food proteins. Some of the properties of peptides include antioxidant, antimicrobial, anti-inflammation, antihypertensive, and immune system modulation. To evaluate the efficacy of peptides in vivo, foods have been used as carrier vehicles. However, there are many molecules in foods that can react or interact with peptides, thereby reducing the bioavailability or bioactivity of these peptides. The Schiff base reactions of peptides with reducing sugars are well established. Peptides can also react with oxidized lipids or reactive oxygen species. Secondary metabolites such as quinones in foods, can react as well with amine or thiol groups of peptides. All these reactions affect the concentration of peptides. This review summarizes and discusses some of the implication of those reactions on the availability of bioactive peptides within the food matrix. PRACTICAL APPLICATIONS: Bioactive peptides possess specific effects on foods like preventing oxidative rancidity. In human, they may lower blood pressure or reduce inflammation. The knowledge on the type of reactions these peptides may encountered when foods are used as transport vehicles is essential. This will prevent variability in intake and ensure proper dosage and reproducible results.

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)0.001
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesMeta-epidemiology (narrow)
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Bench or experimental · Consensus signal: none
GenreCandidate signal: Review · Consensus signal: Review
Teacher disagreement score0.500
Threshold uncertainty score1.000

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0010.001
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0010.002
Bibliometrics0.0000.000
Science and technology studies0.0000.000
Scholarly communication0.0000.000
Open science0.0010.000
Research integrity0.0010.001
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 head0.052
GPT teacher head0.344
Teacher spread0.293 · 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.

Study designBench or experimental
Domainnot available
GenreReview

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

Citations57
Published2017
Admission routes2
Has abstractyes

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