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Record W2609288760 · doi:10.5539/jfr.v6n3p74

The Relative Efficacy of Thermal and Acidification Stresses on the Survival of E. coli O157:H7, Salmonella, and Listeria Monocytogenes in Ground Beef

2016· article· en· W2609288760 on OpenAlexvenueno aff
Sagor Biswas, Pramod Pandey

Bibliographic record

VenueJournal of Food Research · 2016
Typearticle
Languageen
FieldBiochemistry, Genetics and Molecular Biology
TopicListeria monocytogenes in Food Safety
Canadian institutionsnot available
FundersUniversity of California, DavisSchool of Veterinary Medicine, University of California, Davis
KeywordsListeria monocytogenesSalmonellaListeriaPasteurizationPathogenFood scienceMicrobiologyEscherichia coliChemistryBacteriaBiologyBiochemistry

Abstract

fetched live from OpenAlex

To provide additional insights on pathogen survival, we evaluated the relative efficacy of acidification (pH 2.7), thermophilic treatment (55 °C), and low temperature pasteurization (68 °C) on the inactivation of E. coli O157:H7, Salmonella, and Listeria monocytogenes in ground beef. A series of experiment was conducted under biosafety level 3 environments for assessing the impacts of heat and low pH on pathogen survival. Results showed that 5-log reductions of E. coli O157:H7 could take more than 2640 min at 55 °C, 134 min at 68 °C and 120 min under pH 2.7. Compared to E. coli O157:H7, the 5-log reduction of Salmonella was obtained in 4836, 126, 86 min at 55 °C, 68 °C, and pH 2.7, respectively. The 5-log reduction of Listeria was achieved in 4704, 200, and 115 min under 55 °C, 68 °C, and pH 2.7, respectively. The results of this study will provide additional insights for developing improved methods for controlling pathogens in ground beef.

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 score0.002
Threshold uncertainty score0.005

Distilled classifier scores by category (both heads)

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)0.0010.000

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 head0.180
GPT teacher head0.390
Teacher spread0.210 · 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

Citations3
Published2016
Admission routes1
Has abstractyes

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