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Record W4238947851 · doi:10.21065/advfoodnutrisci.4.1

REDUCTION OF CAMPYLOBACTER ON POULTRY THIGHS USING SEQUENTIAL TREATMENTS OF ANTIMICROBIALS

2020· article· en· W4238947851 on OpenAlexvenueno aff
Landrum MA, N.A. Cox, JL Wilson, M.E. Berrang, GR Gamble, MA Harrison, Brian D. Fairchild, WK Kim, Arthur Hinton

Bibliographic record

VenueAdvanced Food and Nutritional Sciences · 2020
Typearticle
Languageen
FieldAgricultural and Biological Sciences
TopicSalmonella and Campylobacter epidemiology
Canadian institutionsnot available
Fundersnot available
KeywordsPeracetic acidCampylobacterGentamicinInoculationChemistryAgarFood scienceMicrobiologyAntibioticsBiologyBacteriaHorticultureHydrogen peroxideBiochemistry

Abstract

fetched live from OpenAlex

Campylobacter is a major concern for poultry processors, as USDA performance standards have become stricter. This study evaluated CMS PoultrypHresh™, a low pH processing aid, and peracetic acid using consecutive and sequential dip treatments to reduce Campylobacter in thighs. Thighs (n=3/treatment group) were inoculated with a C. coli marker strain (108) and each dipped into bags containing 1 L of treatment 1 for 6 s. Thighs were allowed 5 s to drip, placed onto foil for 60 s, and dipped into treatment 2 for 6 s. After 5 s drip time, each was placed in a bag with 150 mL buffered peptone water and hand shaken for 60 s; controls same procedure, no treatment. Rinsates were serially diluted, plated onto Campy Cefex agar with 200 ppm gentamicin and incubated microaerobically for 48 h at 42°C. Procedures were replicated 5 times. Significant reductions compared to untreated using consecutive dips of PoultrypHresh™ and PAA were 98.2% and 99.3%, respectively. Treatments of PoultrypHresh™ then peracetic acid reduced Campylobacter 99.2% from untreated thighs. Peracetic acid then PoultrypHresh™ showed significant reductions compared to all other treatments (99.9% from untreated). Treating with this sequence may allow processors to meet the strict performance standards on Campylobacter in broiler parts.

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: Bench or experimental · Consensus signal: Bench or experimental
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.154
Threshold uncertainty score0.159

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 head0.078
GPT teacher head0.284
Teacher spread0.206 · 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

Citations0
Published2020
Admission routes1
Has abstractyes

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