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Record W4220765760 · doi:10.3390/md20030204

Evaluation of the Preservation and Digestion of Seal Meat Processed with Heating and Antioxidant Seal Meat Hydrolysates

2022· article· en· W4220765760 on OpenAlexafffund
Yi Zhang, Léa Spitzer, Xin Rui, Susana C. M. Fernandes, Romy Vaugeois, Benjamin K. Simpson

Bibliographic record

VenueMarine Drugs · 2022
Typearticle
Languageen
FieldBiochemistry, Genetics and Molecular Biology
TopicProtein Hydrolysis and Bioactive Peptides
Canadian institutionsMcGill University
FundersNatural Sciences and Engineering Research Council of Canada
KeywordsFood scienceHydrolysatePreservativeAntioxidantChemistrySeal (emblem)NutrientDigestion (alchemy)BioavailabilityBiologyHydrolysisChromatographyBiochemistry

Abstract

fetched live from OpenAlex

Seal meat is of high nutritive value but is not highly exploited for human food due to ethical issues, undesirable flavors, and loss of nutrients during the processing/cooking step. In this work, commercially available processed seal meat was treated with its hydrolysates as preservatives with the aim of improving nutrient bioavailability. The contents of the nutrients were analyzed after digestion using a simulated dynamic digestion model, and the effects of different processing conditions, i.e., low-temperature processing and storage (25 °C) and high-temperature cooking (100 °C), of seal meat were investigated. Hydrolysates with antioxidant activity decreased the amounts of the less desirable Fe3+ ions in the seal meat digests. After treatment with hydrolysates at room temperature, a much higher total Fe content of 781.99 mg/kg was observed compared to other treatment conditions. The release of amino acids increased with temperature and was 520.54 mg/g for the hydrolysate-treated sample versus 413.12 mg/g for the control seal meat sample treated in buffer. Overall, this study provides useful data on the potential use of seal meat as a food product with high nutritive value and seal meat hydrolysates with antioxidant activity as preservatives to control oxidation in food.

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 score0.025
Threshold uncertainty score0.218

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 head0.010
GPT teacher head0.232
Teacher spread0.221 · 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

Citations1
Published2022
Admission routes2
Has abstractyes

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