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Record W2765713066 · doi:10.20546/ijcmas.2017.610.006

Effectiveness of Cooking Methods on Presence of Food Borne Pathogens in Chicken based Meat Products

2017· article· en· W2765713066 on OpenAlexfundno aff
Mahantesh F. Meti, V. Appa Rao

Bibliographic record

VenueInternational Journal of Current Microbiology and Applied Sciences · 2017
Typearticle
Languageen
FieldAgricultural and Biological Sciences
TopicFood Safety and Hygiene
Canadian institutionsnot available
FundersH. Lundbeck A/SUniversity of TorontoWorld Health Organization
KeywordsSalmonellaFood scienceCooking methodsCampylobacterCooked meatPoultry meatBiologyBacteria

Abstract

fetched live from OpenAlex

The food borne diseases are often unnoticed and source of infection is always not confirmative. The consumption of street and fast food has been popularising in developing country like India. The present study was to taken up to assess the effect of different cooking procedures followed by street food vendors and fast food outlets in Chennai city, India. The food borne pathogens viz., Escherichia coli, Salmonella spp, Staphylococcus aureus and Campylobacter jejuni studied by artificially inoculating in chicken meat preparation at level of 100 cfu/g and 1000 cfu/g. The different chicken based meat preparation and cooking practice followed were viz. pan frying for chicken 65 and chilly chicken, oven cooking for tandoori chicken and electric grilling in grilled chicken. The core temperature of 82.8ËšC, 83.8ËšC, 74.3ËšC and 75.3ËšC was achieved in chicken 65, chilly chicken, tandoori chicken and grilled chicken, respectively. The different cooking methods resulted in complete elimination (absent in 25 g of product) of all food borne pathogens viz., Escherichia coli, Salmonella spp, Staphylococcus aureus and Campylobacter jejuni at both 100 cfu/g and 1000 cfu/g and reaching satisfactory core temperature of 720C in above. Thus, cooking procedures by street food vendors and fast food outlets in Indian condition often produce safe chicken products.

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: Bench or experimental
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.109
Threshold uncertainty score0.207

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0020.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.000
Science and technology studies0.0000.001
Scholarly communication0.0000.000
Open science0.0010.000
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 head0.050
GPT teacher head0.333
Teacher spread0.283 · 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
Published2017
Admission routes1
Has abstractyes

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