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Record W4408705995 · doi:10.5539/jfr.v14n2p66

Growth Prediction of Aeromonas hydrophila in Fresh Cheese Stored at 4°C

2025· article· en· W4408705995 on OpenAlexvenueno aff
Helen Cristine Leimann Winter, Maria Fernanda Silva Rodrigues, Iandra de Assis Silva, Rozilaine Aparecida Pelegrine Gomes de Faria, Daniel Oster Ritter, Marilú Lanzarin

Bibliographic record

VenueJournal of Food Research · 2025
Typearticle
Languageen
FieldBiochemistry, Genetics and Molecular Biology
TopicIdentification and Quantification in Food
Canadian institutionsnot available
Fundersnot available
KeywordsAeromonas hydrophilaFood scienceAeromonasMicrobiologyBiologyFisheryBacteriaFish <Actinopterygii>Genetics

Abstract

fetched live from OpenAlex

The aim of this study was to determine the behavior of Aeromonas hydrophila in &amp;ldquo;Minas Frescal&amp;rdquo; cheese during storage at 4&amp;deg;C, 8&amp;deg;C and 12&amp;deg;C using a regression model and to predict the development of this microorganism during 24 days of storage. Cheese was vacuum-packed and then stored at the study temperatures. A. hydrophila and aerobic heterotrophic psychrotrophic bacteria (AHPB) were quantified during storage, water activity (wa) and pH were analyzed. Then, the microbiological counts were submitted to regression analysis and the averages were analyzed using Pearson&amp;#39;s correlation with the wa and pH parameters. Results showed that A. hydrophila developed significantly at refrigeration temperatures, with high growth rates at 4&amp;deg;C and 8&amp;deg;C, in contrast to those observed at 12&amp;deg;C. There was a slight variation in the wa and pH results and AHPB presented good performance at all the temperatures analyzed. The models obtained fitted better at a temperature of 4&amp;deg;C and were able to predict the growth of A. hydrophila with a model fit of 90% (p&amp;lt;0.001). In conclusion, the model obtained for predicting the growth of A. hydrophila at 4&amp;deg;C was accurate up to 13 days after the product was manufactured, showing that temperature control was crucial for maintaining product quality; wa and pH were not parameters for quality control.

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)0.001
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 score0.032
Threshold uncertainty score0.231

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0010.001
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 head0.056
GPT teacher head0.359
Teacher spread0.303 · 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

Citations1
Published2025
Admission routes1
Has abstractyes

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