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Record W2612875184 · doi:10.2527/asasann.2017.829

829 Controlling meat quality through product functionality enhancement

2017· article· en· W2612875184 on OpenAlexaff
Heather L. Bruce

Bibliographic record

VenueJournal of Animal Science · 2017
Typearticle
Languageen
FieldAgricultural and Biological Sciences
TopicMeat and Animal Product Quality
Canadian institutionsUniversity of Alberta
Fundersnot available
KeywordsFood scienceRed meatTendernessBreedMeat tendernessWhite meatLactic acidLipid oxidationChemistryBiologyBacteriaBiochemistryAnimal scienceAntioxidant

Abstract

fetched live from OpenAlex

Meat can be considered a functional food because it is an excellent source of protein; minerals such as iron, zinc, and selenium; and B vitamins, but recent associations of red meat with diseases such as cancer and cardiovascular disease provide an impetus to look at increasing the healthfulness of meat. Modification of the eating quality of meat is currently accomplished through management of animal breed and/or growth-enhancing pharmaceuticals, with gene marker selection for meat tenderness commercially available in cattle and under investigation in pigs. Other antemortem meat quality enhancements include alteration of fat content and fatty acid composition through animal diet; supplementation of animals with vitamins D and E to improve meat tenderness and shelf life, respectively; and provision of glucose and electrolytes prior to slaughter to decrease the likelihood of dark cutting and improve meat color. Given that ground, seasoned, cured, and injected/tenderized products are readily accepted in the North American market, the use and development of additional postmortem strategies and technologies to enhance meat quality and healthfulness warrant reconsideration. Improvement of the healthfulness of meat through prevention of the growth of pathogenic bacteria is easily substantiated, and bacteriocins from lactic acid bacteria have been shown to be effective. Recent research has focused on increasing the proportions of muscle protein and omega-3 fatty acids and reducing sodium chloride and sodium nitrite concentrations in processed meat products. Also, incorporation of “functional” plant products to enhance meat healthfulness and quality through the addition of natural antioxidants has been investigated with some success at reducing heterocyclic aromatic amines. Fermented meats are gaining credibility as functional foods because of the potential proteolytic release of bioactive peptides that exhibit either inhibitory activity on the angiotension I-converting enzyme (ACE) or opiod properties or have a prebiotic function. Meat from ruminants may have potential anticancer, antioxidative, and antiageing effects due to its conjugated linoleic acid content, but most potential healthfulness claims for meat have not been substantiated in humans. There is also the opportunity to improve flavor and eating quality through the introduction of reactive 5-carbon sugars such as ribose and xylose to pork products. Future research improving both meat healthfulness and eating quality while maintaining affordability may return health-conscious consumers to processed and unprocessed meat products.

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 score0.017

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0000.000
Meta-epidemiology (narrow)0.0010.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.000
Science and technology studies0.0000.000
Scholarly communication0.0010.001
Open science0.0000.000
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0050.002

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 head0.137
GPT teacher head0.353
Teacher spread0.216 · 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

Citations0
Published2017
Admission routes1
Has abstractyes

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