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Record W4386256350 · doi:10.3390/foods12173247

Antibacterial Effect of Pomegranate Juice on Listeria innocua and E. coli in Different Media

2023· article· en· W4386256350 on OpenAlexafffund
Zhaojun Ban, Lihua Fan, Jun Song, Sherry Fillmore, Junfeng Guan

Bibliographic record

VenueFoods · 2023
Typearticle
Languageen
FieldAgricultural and Biological Sciences
TopicEssential Oils and Antimicrobial Activity
Canadian institutionsAgriculture and Agri-Food Canada
FundersAgriculture and Agri-Food CanadaMinistry of Education, India
KeywordsListeriaIncubationDistilled waterFood scienceBacterial growthEscherichia coliBacteriaChemistryAntibacterial activityPopulationIncubation periodInoculationMicrobiologyListeria monocytogenesBiologyChromatographyBiochemistry

Abstract

fetched live from OpenAlex

The antibacterial effect of pomegranate juice (PJ) at six concentrations (0, 10, 20, 30, 40, and 50%) against Listeria innocua and Escherichia coli was investigated in distilled water (DW) and bacterial culture broth. L. innocua and E. coli at approximately 105 cfu mL−1 were inoculated in PJ samples and incubated at 4, 25, and 37 °C for 0, 6, 24, and 48 h. The bacterial population and pH of culture media were measured at each removal. Results indicated that the antibacterial effect of PJ was dependent upon bacteria species, juice concentration, incubation temperature, and growth medium. Higher juice concentration and incubation temperature resulted in increased antibacterial effects. Bacterial populations were decreased more significantly in DW systems than in the culture broth, while L. innocua was more sensitive to PJ than E. coli in the DW systems. Regardless of PJ concentrations in DW systems, L. innocua, initially inoculated at approximately 105 cfu mL−1, was reduced to undetectable levels at 25 and 37 °C within 24 h. The growth of L. innocua and E. coli was significantly inhibited in bacterial culture broth containing ≥ 20% PJ (p < 0.001). This study provides insight into the potential application of PJ in food and beverage products for food protection.

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: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.740
Threshold uncertainty score0.137

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 head0.013
GPT teacher head0.228
Teacher spread0.214 · 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

Citations7
Published2023
Admission routes2
Has abstractyes

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