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Changes in the Composition of the Bacterial Flora on Tray‐Packaged Pork during Chilled Storage Analyzed by PCR‐DGGE and Real‐Time PCR

2010· article· en· W2153874707 on OpenAlexaff
Yun Jiang, Feng Gao, Xinglian Xu, KePing Ye, Guanghong Zhou

Bibliographic record

VenueJournal of Food Science · 2010
Typearticle
Languageen
FieldBiochemistry, Genetics and Molecular Biology
TopicIdentification and Quantification in Food
Canadian institutionsMinistry of Education and Child Care
FundersMinistry of Science and Technology of the People's Republic of China
KeywordsFood scienceBacteriaTemperature gradient gel electrophoresis16S ribosomal RNAFood spoilagePseudomonasMeat spoilageBiologyPopulationMicrobiologyChemistry

Abstract

fetched live from OpenAlex

In this study, a polymerase chain reaction (PCR)-denaturing gradient gel electrophoresis (DGGE) was used to investigate the changes in the composition of the bacterial population of tray-packaged pork during chilled storage. Relative quantitative real-time PCR was further used to evaluate the predominant spoilage bacteria obtained from DGGE analysis for their relative amount to the total bacteria in meat samples. DGGE analysis of the V3 and V6-V8 regions of the 16S rRNA gene showed that Pseudomonas were the predominant bacterial species at the end of the monitoring period. Real-time PCR expressed as the ΔΔC(T) method showed that the average 2(-ΔΔC)(T) values increased continually during the storage period from less than 0.001 at day 0 to 4.438 at the end of the monitoring, which indicated that the proportions of Pseudomonas within the total bacteria in meat samples increased. Both methods confirmed that Pseudomonas was the predominant spoilage bacteria. Practical Application: This study uses new techniques to identify bacteria in fresh retail pork and to follow changes in the bacterial population during 12 d refrigerated storage. Pseudomonads were found to increase with storage time, becoming the dominant flora after 12 d.

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)0.001
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 score0.098
Threshold uncertainty score0.156

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0010.000
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 head0.011
GPT teacher head0.246
Teacher spread0.236 · 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

Citations26
Published2010
Admission routes1
Has abstractyes

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