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Record W2021519757 · doi:10.5539/jfr.v1n3p178

Comparison of Conventional and Rapid Methods for Salmonella Detection in Artisanal Minas Cheese

2012· article· en· W2021519757 on OpenAlexvenueno aff
Gardênia Márcia Silva Campos Mata, Maria Cristina Dantas Vanetti

Bibliographic record

VenueJournal of Food Research · 2012
Typearticle
Languageen
FieldAgricultural and Biological Sciences
TopicSalmonella and Campylobacter epidemiology
Canadian institutionsnot available
FundersFundação de Amparo à Pesquisa do Estado de Minas Gerais
KeywordsSalmonellaFood scienceRaw milkChemistryBiologyBacteria

Abstract

fetched live from OpenAlex

<p>Artisanal cheeses traditionally produced from raw milk have a diverse microbiota and, due the varied changes that occur in this type of food matrix during the maturation, pathogens detection’s may be impaired. In this study, the conventional method established by ISO 6579:2005 to evaluate the presence of<em> Salmonella</em> was compared with two alternative rapid methods, PCR-BAX<sup>®</sup> (DuPont)<sup> </sup>and VIDAS<sup>®</sup><span style="text-decoration: line-through;">-</span>SLM (BioMérieux), to analyze artisanal Minas cheese, a typical Brazilian product. <em>Salmonella</em> was not detected by conventional or PCR-BAX<sup>®</sup> in 63 artisanal Minas cheese samples analyzed. Although highly specific and accurate, the immunoassay (VIDAS<sup>®</sup>-SLM) presented 3.17% of false positives. Good manufactures practices were absent in some producers of Minas artisanal cheese and, the fact of <em>Salmonella</em> was not detected in analyzed samples should be related with presence of high and diverse endogenous microbiota, including approximately, 10<sup>7</sup> CFU.g<sup>-1</sup> of lactic bacteria, and a low pH and water activity, conditions that can minimize pathogens growth, provide cellular injury and hamper the recovery strategies.</p>

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)0.006
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: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.687
Threshold uncertainty score0.213

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0060.001
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 head0.289
GPT teacher head0.503
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

Citations8
Published2012
Admission routes1
Has abstractyes

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