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Record W4405379226 · doi:10.4314/sajas.v54i1.07

Natural additives as a source of antioxidants improve lipid oxidation, antioxidant activity, and shelf-life of beef

2024· article· en· W4405379226 on OpenAlexaff
Mariana Garcia Ornaghi, Rodrigo Augusto Cortêz Passetti, Melina Aparecida Plastina Cardoso, Ana Carolina Pelaes Vital, Rodolpho Martin do Prado, Ana Guerrero, Diogo Francisco Rossoni, Ivanor Nunes do Prado

Bibliographic record

VenueSouth African Journal of Animal Science · 2024
Typearticle
Languageen
FieldAgricultural and Biological Sciences
TopicMeat and Animal Product Quality
Canadian institutionsUniversité Laval
FundersConselho Nacional de Desenvolvimento Científico e TecnológicoCoordenação de Aperfeiçoamento de Pessoal de Nível Superior
KeywordsLipid oxidationAntioxidantFood scienceEugenolABTSDPPHTBARSShelf lifeChemistryFeedlotThymolBiologyLipid peroxidationEssential oilAnimal scienceBiochemistry

Abstract

fetched live from OpenAlex

Forty young bulls were finished in feedlot with diets that contained different dosages of a combination of several natural additives (NA), i.e., clove essential oil, cashew and castor oil, and rumenprotected eugenol, vanillin, and thymol. The animals were randomized in five diets containing four different inclusion levels of NA and a control diet (n = 8 animals per treatment): basal diet without NA (CON); NA15 = 1500 mg/day; NA30 = 3000 mg/day; NA45 = 4500 mg/day, and NA60 = basal diet with 6000 mg/day of the natural additives blend. Colour, antioxidant activity (DPPH, ABTS and FRAP assays), lipid oxidation, and visual acceptability were evaluated through display (until 14 d in vacuum or film packages). Both factors (diet and display) affected all parameters evaluated. The highest dosage, NA60, was able to improve the antioxidant potential, decreasing beef oxidation to produce a higher visual acceptability. The results of this research provide evidence that NA included in the diet of beef cattle can improve overall meat quality and extend shelf-life, thus, providing higher visual acceptability through the colour perception of consumers.

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)0.002
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: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.647
Threshold uncertainty score0.366

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0020.001
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.001
Science and technology studies0.0000.001
Scholarly communication0.0000.001
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 head0.020
GPT teacher head0.259
Teacher spread0.238 · 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

Citations3
Published2024
Admission routes1
Has abstractyes

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