MétaCan
Menu
Back to cohort
Record W4248887203 · doi:10.22175/mmb.10839

Correlation Among Ground Beef Lipid Content, Color, and Lipid Oxidation Over a 7-Day Simulated Retail Display Period

2019· article· en· W4248887203 on OpenAlexaffabout
Liping Wang, Shiqi Huang, S. Chalupa-Krebzdak, S. M. Vasquez Mejia, B. M. Bohrer

Bibliographic record

VenueMeat and Muscle Biology · 2019
Typearticle
Languageen
FieldAgricultural and Biological Sciences
TopicMeat and Animal Product Quality
Canadian institutionsUniversity of Guelph
Fundersnot available
KeywordsTrayFood scienceLipid oxidationGrindingSubcutaneous fatAnimal scienceChemistryMaterials scienceBiologyComposite materialAdipose tissueBotanyBiochemistry

Abstract

fetched live from OpenAlex

ObjectivesGround beef is more susceptible to lipid oxidation compared to whole muscle beef cuts. This is due to its smaller particle size and greater surface area, which makes the meat products more prone to be exposed to various environmental factors during production. The objective of this study was to determine the relationships among ground beef lipid content, instrumental color, visual discoloration, and lipid oxidation over a 7d simulated retail display period.Materials and MethodsBeef inside rounds (IMPS #168) from the right sides of steer carcasses (n = 63) were collected from a commercial processing facility and delivered to the U of Guelph Meat Science Laboratory for further analyses. Each inside round was trimmed of all subcutaneous fat and connective tissue, and then fabricated into ground beef patties (113 g/patty) at two targeted fat addition levels (no added fat and 25%). Lean ground beef (no added fat) was made by grinding cubes of inside round muscle through a Sirman Master 90 Y12 meat grinder (Sirman USA, Franklin Park, IL). Regular ground beef (25% added fat) was made by grinding cubed round muscle with an additional 25% subcutaneous fat that originated from the rib primal of the same carcass. For the simulated retail shelf life study, 2 crust frozen patties (crust frozen for 1 h to improve packaging ability) were placed on a Styrofoam meat tray with a soaker pad and overwrapped with PVC film. In total, there were 4 trays of patties per ID (2 trays/targeted fat level). Trays were placed under two LED lights (52 W, 1850 lumens, color temperature of 4000K, 1612.5 to 2152 lux) at 4°C and the locations on shelves were changed every 24 h. Minolta L*, a*, b* color and subjective surface discoloration were evaluated every 24 h for 7 d. Lipid peroxidation of patties before and after the retail display was estimated using thiobarbituric reactive substances (TBARS; mg MDA/g fat). Lipid content of patties was quantified using Soxhlet extraction with petroleum ether. Summary statistics and Pearson correlation coefficients were determined using the PROC CORR procedure of SAS. Correlations were regarded as weak at r < |0.35|, moderate at |0.36| ≤ r ≤ |0.67|, and strong at r ≥ |0.68|.ResultsLipid content in the 126 ground beef samples evaluated in this study ranged from 3.47% to 30.43% (16.30% ± 6.18%). A moderate and significant correlation was observed between lipid content and change in TBARS values (r = –0.59, P < 0.0001). Similarly, an increase in lipid content is moderately correlated with a decrease in a* values (r = –0.58, P < 0.0001) and an increase in surface discoloration after a 7d display period (r = 0.53, P < 0.0001). Δ TBARS values was weakly correlated with Δ L* (r = 0.24, P < 0.01), Δ a* (r = 0.11, P = 0.25), and visual discoloration (r = –0.16, P = 0.09). Finally, Δ a* was strongly correlated with surface discoloration values at d7 (r = –0.76, P < 0.0001).ConclusionAn increase of lipid content in ground beef had a moderate association with decreased redness, greater surface discoloration, and less change in lipid oxidation over a 7d simulated retail display period. Color values were not great predictors of lipid oxidation values and trained technicians often equated visual discoloration in beef to a deviation from the desired cherry red color.

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)0.000
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 score0.621
Threshold uncertainty score0.547

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0000.000
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 head0.035
GPT teacher head0.237
Teacher spread0.202 · 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".

Quick stats

Citations0
Published2019
Admission routes2
Has abstractyes

Explore more

Same venueMeat and Muscle BiologySame topicMeat and Animal Product QualityFrench-language works237,207