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Record W2401363767 · doi:10.5539/jfr.v5n3p92

Meat Colour Stability and Fatty Acid Profile in Commercial Bison and Beef

2016· article· en· W2401363767 on OpenAlexaffvenue
J. K. Galbraith, J. L. Aalhus, M. Juárez, M.E.R. Dugan, I. L. Larsen, Noelia Aldai, L. A. Goonewardene, E. K. Okine

Bibliographic record

VenueJournal of Food Research · 2016
Typearticle
Languageen
FieldAgricultural and Biological Sciences
TopicMeat and Animal Product Quality
Canadian institutionsUniversity of AlbertaAgriculture and Agri-Food CanadaAgriculture Food and Rural Development
Fundersnot available
KeywordsTBARSMetmyoglobinFood sciencePolyunsaturated fatty acidIntramuscular fatComposition (language)ChemistryBeef cattleFatty acidAnimal scienceVitaminBiologyAntioxidantLipid peroxidationBiochemistry

Abstract

fetched live from OpenAlex

Commercial bison meat has been found to discolour more rapidly than beef in retail display. The influence of fat content, fat composition and vitamin E on the colour stability of commercially produced bison and beef were examined. Longissimus samples from grain-fed beef (n = 20) and grass-fed bison (n = 14) were analyzed for fat content, fatty acid composition, vitamin E levels, pigments, TBARS and retail stability. Intramuscular fat content was lower and richer in PUFA in bison (P < 0.01) compared to beef. Pigment and TBARS levels in bison were significantly higher (P < 0.01), leading to higher (P < 0.01) metmyoglobin levels. Regression analysis results showed that differences in total fat content and fatty acid composition were the most responsible factors for early discolouration of commercial bison meat compared to commercial beef. In conclusion, total fat and fatty acid composition can be manipulated to improve the colour stability of bison meat.

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 score0.005
Threshold uncertainty score0.011

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0000.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0010.000
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 head0.211
GPT teacher head0.365
Teacher spread0.154 · 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

Citations8
Published2016
Admission routes2
Has abstractyes

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