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Record W2166160338 · doi:10.1111/1750-3841.12140

Effect of Low‐Dose Electron Beam Irradiation on Quality of Ground Beef Patties and Raw, Intact Carcass Muscle Pieces

2013· article· en· W2166160338 on OpenAlexafffund
Devapriya Kundu, Richard A. Holley

Bibliographic record

VenueJournal of Food Science · 2013
Typearticle
Languageen
FieldAgricultural and Biological Sciences
TopicRadiation Effects and Dosimetry
Canadian institutionsUniversity of Manitoba
FundersBeef Cattle Research Council
KeywordsFood scienceTendernessAromaChemistryFlavorIrradiationRaw meatOdorPhysics

Abstract

fetched live from OpenAlex

The objectives of this study were to determine the effects of a low-dose (≤1 kGy), low-penetration electron beam on the sensory qualities of (1) raw muscle pieces of beef and (2) cooked ground beef patties. Outside flat, inside round, brisket and sirloin muscle pieces were used as models to demonstrate the effect of irradiation on raw beef odor and color, as evaluated by a trained panel. Ground beef patties were also evaluated by a trained panel for tenderness, juiciness, beef flavor, and aroma at 10%, 20%, and 30% levels of fat, containing 0% (control), 10%, 20%, 50%, and 100% irradiated meat. With whole muscle pieces, the color of controls appeared more red (P < 0.05) than irradiated muscles, however, both control and treatments showed a gradual deterioration in color over 14 d aerobic storage at 4 °C. Off-aroma intensity of both control and treatments increased with storage time, but by day 14, the treated muscles showed significantly (P < 0.05) less off-aroma than the controls, presumably as a result of a lower microbial load. It was found that a 1 kGy absorbed dose had minimal effects on the sensory properties of intact beef muscle pieces. Irradiation did not have a significant effect (P > 0.05) on any of the sensory attributes of the patties. Low-dose irradiation of beef trim to formulate ground beef appears to be a viable alternative processing approach that does not affect product quality.

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)0.000
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 score0.809
Threshold uncertainty score0.134

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0020.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.001
Science and technology studies0.0000.000
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 head0.013
GPT teacher head0.265
Teacher spread0.252 · 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

Citations12
Published2013
Admission routes2
Has abstractyes

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