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Record W4230368268 · doi:10.22175/mmb.10835

Comparison of Lipid and Protein Oxidation Products and their Impact on Colour Stability in Bison Longissimus Lumborum and Psoas Major Muscles

2019· article· en· W4230368268 on OpenAlexaff
Mohamed Hasan, Vipasha Sood, Chyngyz Erkinbaev, Jitendra Paliwal, Surendranath P. Suman, Argenis Rodas‐González

Bibliographic record

VenueMeat and Muscle Biology · 2019
Typearticle
Languageen
FieldAgricultural and Biological Sciences
TopicMeat and Animal Product Quality
Canadian institutionsUniversity of Manitoba
Fundersnot available
KeywordsLongissimusAgeingLipid oxidationThiobarbituric acidLongissimus dorsiChemistryAnimal scienceFood scienceMalondialdehydeBiochemistryBiologyLipid peroxidation

Abstract

fetched live from OpenAlex

ObjectivesTo compare lipid (malondialdehyde [MDA], 4-hydroxy-2-nonenal [HNE]) and protein (carbonyl content [CAR]) oxidation products and determine their influence on color stability in two bison muscles (longissimus lumborum [LL; color stable] and psoas major [PM; color labile]).Materials and MethodsA total of 10 longissimus lumborun (LL) and 10 psoas major (PM) from five A1 grade bison carcasses were obtained from a commercial slaughter plant within 48 h post-mortem. From each muscle, a 10-cm thick piece was removed and subsampled for evaluation of pH, MDA (by thiobarbituric acid assay), HNE (by ELISA) and CAR (by 2,4-dinitrophenylhydrazine). These measurements allowed the establishment of a baseline for the different oxidation products. The remainder of the muscles were cut into two equal portions, and each portion was vacuum-packaged and assigned to an ageing period of 7 and 14 d at 2°C. At the end of each ageing period, each muscle portion was removed from their packages, pH measured, and steaks obtained for sensory (muscle and discoloration scores) and instrumental color measurements (L*, a* and b*) over 5 d of retail display, and for estimation of MDA, HNE and CAR. After 5 d in retail display and following color and pH measurements the steaks were removed and collected for MDA, HNE and CAR determination. Data were analyzed as a completely randomized design with a split-split plot arrangement. Additionally, correlation and regression analysis were performed to identify the influence of the measured attributes on color.ResultsRegardless of the ageing time, LL showed greater redness and lower surface discoloration by instrumental (a* value; P = 0.04) and sensory (P < 0.01) color evaluation than PM at the end of the retail display. Furthermore, LL exhibited lower MDA, HNE and CAR content compared to PM (P < 0.05). A three-way interaction (muscle × ageing time × retail day display) was detected on MDA content, where PM presented a higher level of MDA with increasing ageing time and retail display than LL (P = 0.02). The pH was not different between LL and PM (P > 0.05) steaks.In both muscles, Pearson (r) and Spearman (rs) correlation coefficients indicated that MDA was the oxidation compound showing the highest correlation to a* (r = –0.78; P < 0.01) and discoloration (rs = 0.81; P < 0.01) scores, followed by a moderate correlation with HNE and CAR (r or rs < 0.7; P < 0.01). The pH did not exhibit correlation with color traits, except for lightness, in both muscles. For the stepwise regression analysis, the main variable entered into the equation for predicting a*, color and discoloration score in PM muscle was MDA with an R2 of 0.72, 0.75 and 0.78, respectively, while for LL muscle, MDA presented an R2 of 0.62, 0.68 and 0.66;, respectively. The pH, HNE and CAR only explained an additional 2% of the variation in those attributes.ConclusionThe results of color attributes corroborated that bison LL is a color-stable muscle due to the lower level of protein and lipid oxidation products developed during storage and retail display compared to PM muscle, which is considered color-labile muscle. The MDA seemed to have remarkable importance in the color deterioration than HNE and CAR, particularly in bison PM muscle.

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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: Observational · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.542
Threshold uncertainty score0.265

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 head0.044
GPT teacher head0.289
Teacher spread0.245 · 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 designObservational
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".

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Citations0
Published2019
Admission routes1
Has abstractyes

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