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Record W2398289828 · doi:10.5539/jfr.v5n3p85

ACEI and antioxidant peptides release during ripening of Mexican Cotija hard cheese

2016· article· en· W2398289828 on OpenAlexvenueno aff
Leticia Hernández-Galán, Anaberta Cardador‐Martínez, Daniel Picque, Henry-Éric Spinnler, Micloth López del Castillo‐Lozano, Sandra T. Martín del Campo

Bibliographic record

VenueJournal of Food Research · 2016
Typearticle
Languageen
FieldBiochemistry, Genetics and Molecular Biology
TopicProtein Hydrolysis and Bioactive Peptides
Canadian institutionsnot available
Fundersnot available
KeywordsRipeningChemistryKjeldahl methodAntioxidantFood scienceDPPHCheese ripeningHigh-performance liquid chromatographyNitrogenChromatographyBiochemistryOrganic chemistry

Abstract

fetched live from OpenAlex

Cotija cheese is an artisanal Mexican cheese produced with raw cow´s milk. Our objective was to measure the antioxidant and angiotensin converting enzyme (ACE) inhibitory activities of the peptides released during its ripening. For that, Cotija cheeses were ripened 6 months in a chamber at 25 ºC without humidity control. Weekly samples were taken to determine acid soluble nitrogen (ASN), non-protein nitrogen (NPN) and ethanol soluble nitrogen (EtOH-SN) indexes, by Kjeldahl method. Antioxidant and ACE inhibitory activities were measured by spectrophotometry and HPLC methods, respectively. Peptides in each nitrogen fraction were determined by HPLC. Our results showed that during ripening of Cotija cheeses peptides with antioxidant and ACE inhibitory activities were released and increased through ripening time reaching a maximum of 79.8 % of 2,2- diphenyl-1-picrylhydrazyl (DPPH) discoloration and 100 % of ACE inhibition at the end of ripening. Both activities were highly correlated with the types of peptides present in each fraction.

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)0.000
metaresearch head score (Gemma)0.000
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: Empirical · Consensus signal: Empirical
Teacher disagreement score0.001
Threshold uncertainty score0.002

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0000.000
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)0.0010.000

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 head0.037
GPT teacher head0.316
Teacher spread0.279 · 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
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

Citations33
Published2016
Admission routes1
Has abstractyes

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