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Record W4402540988 · doi:10.1093/jas/skae234.773

PSVII-24 Effectiveness of hot water knife sanitation practices during beef dressing processes

2024· article· en· W4402540988 on OpenAlexaff
Xianqin Yang, Frances Tran, Prakriti Chhabra, Zane Platter, Travis C Tennant, T. M. Brown, T.E. Lawrence

Bibliographic record

VenueJournal of Animal Science · 2024
Typearticle
Languageen
FieldAgricultural and Biological Sciences
TopicMeat and Animal Product Quality
Canadian institutionsAgriculture and Agri-Food Canada
Fundersnot available
KeywordsSanitationBusinessEnvironmental scienceAnimal scienceBiologyEnvironmental engineering

Abstract

fetched live from OpenAlex

Abstract Our objective was to assess whether the harvest processing equipment sanitation practices in beef processing plants are sufficient to mitigate pathogen transfer. Multiple regulatory agencies across the globe require that harvest equipment be sanitized using hot water at a minimum of 82°C, with a momentary dip when a time is specified. Consequently, hot water sanitizers have been widely implemented at meat processing facilities, in particular, for knife sanitation throughout the dressing process. However, there is no standard time requirement for hot water immersion and, as such, the effectiveness of hot water sanitization is uncertain. The practice for knife sanitation at a large beef processing facility was audited and the microbial reduction that simulated the audited practice was assessed along with a momentary dip at 82°C. A five-strain cocktail of Escherichia coli and 11 knives of differing degree of usage obtained from the beef plant were used in the study. An area of 50 cm2 of the upper surface of each knife was spread-inoculated with the five-strain E. coli cocktail and the knife was subsequently submerged in a water bath coupled to a water circulator for immersion at 82°C. Exposures times tested were those derived from the in-plant audit for each knife type. A knife that was similarly inoculated, but was not treated with hot water, served as a positive control. E. coli surviving the treatment were enumerated by plating. In addition, two knives that had most and least reduction of E. coli were artificially contaminated with meat prior to inoculation, by passing the knife through meat multiple times. For each condition, three independent trials were conducted. Least-squares means for log counts were separated by a post-hoc Tukey test. Mean immersion times for different knives audited ranged from 1 to 9.8 s. Immersing knives in water at 82°C significantly reduced the number of E. coli (P < 0.05; Table 1), ranging from 2.3 (air knife)-5.5 (skinning knife 2) log units. Variation among the same knife type was observed. Momentary dip at 82°C reduced E. coli on de-boning, hook, air knife and wizard knife by 1-1.2 log (P < 0.05) and < 1 log for the other six knives (P ≥ 0.05). Treatment at 82°C for the duration of average exposure time observed during the in-plant audit of knives that had been contaminated with meat debris showed lower reductions of E. coli, compared with clean knives. Those reductions (>1.5 log units) were significant (P < 0.05). In conclusion, the duration for hot water sanitation of knives at beef processing plants achieves significant reduction of E. coli if temperature is maintained at 82°C and a momentary dip at the same temperature would be less likely to be effective.

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)0.001
metaresearch head score (Gemma)0.002
Version: metacan-v3-hybrid-931329e0061cValidation 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 score0.004
Threshold uncertainty score0.008

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0010.002
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0010.000
Bibliometrics0.0000.000
Science and technology studies0.0000.000
Scholarly communication0.0010.000
Open science0.0000.001
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0020.000

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 head0.059
GPT teacher head0.325
Teacher spread0.266 · 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 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
Published2024
Admission routes1
Has abstractyes

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