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Record W2795869648 · doi:10.5740/jaoacint.17-0426

Evaluation of the iQ-Check®Salmonella II Assay in Select Foods: Collaborative Study, First Action 2017.06

2018· article· en· W2795869648 on OpenAlexaboutno aff
Patrick Bird, M Joseph Benzinger, Benjamin Bastin, Erin Crowley, James Agin, David Goins, Mike Clark, Jean-Philippe Tourniaire, Sophie Pierre, Wendy F Lauer, David W. Aho, K Bierhals, L Buker, Álvaro Marín Camacho, Raúl Crespo, Ninalynn Daquigan, P Dolland, V Gohil, Virginia Beryl Green, G Gutierrez, Zennia Hancock, A Jumbo, Nicole Klass, M McDonough, Santosh Kumar Mishra, J Picket, Niranjan Shrestha, Kawe Simon, B Solorza, Sarah A. Sorensen, J Smith, Lisa M. Thompson, S Weber, Helen A. Wright

Bibliographic record

VenueJournal of AOAC International · 2018
Typearticle
Languageen
FieldMedicine
TopicConsumer Attitudes and Food Labeling
Canadian institutionsnot available
Fundersnot available
KeywordsSalmonellaAction (physics)Food scienceChemistryBiologyGeneticsBacteriaPhysics

Abstract

fetched live from OpenAlex

The iQ-Check Salmonella II Real-Time PCR test kit utilizes Salmonella-specific oligonucleotide probes and primers for the rapid and specific detection of Salmonella species in select food types. The alternative method was evaluated by using 375 g test portions in an unpaired study design for two matrices, milk chocolate and dry dog food. Each matrix was compared with the U.S. Food and Drug Administration Chapter 5 Salmonella reference method. Fourteen technicians from 12 laboratories, including academia and industry, located within the United States and Canada participated in the collaborative study. Three levels of contamination were evaluated for each matrix: an uninoculated control level (0 CFU/test portion), a low inoculum level (0.2-2 CFU/test portion), and a high inoculum level (2-5 CFU/test portion). The statistical analysis was conducted according to the Probability of Detection (POD) statistical model. The results obtained for the low inoculum level test portions produced a difference in the candidate presumptive and confirmatory results (dLPOD) value with a 95% confidence interval of -0.05, (-0.15, 0.06) for the milk chocolate and 0.10, (-0.01, 0.21) for the dry dog food. The dLPOD results indicate an equivalence between the candidate method and reference method for the matrices evaluated, and the method demonstrated acceptable interlaboratory reproducibility as determined in the collaborative evaluation. False positive and false negative rates were determined for each matrix and produce values of <2%. Based on the data generated, the method demonstrated acceptable interlaboratory reproducibility data and statistical analysis.

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 machine prediction

Teacher imitation

Not calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.

metaresearch head score (Codex)0.062
metaresearch head score (Gemma)0.033
Version: metacan-v3-hybrid-931329e0061cValidation 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 score0.062
Threshold uncertainty score0.328

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0620.033
Meta-epidemiology (narrow)0.0020.001
Meta-epidemiology (broad)0.0010.001
Bibliometrics0.0010.001
Science and technology studies0.0010.001
Scholarly communication0.0020.001
Open science0.0020.002
Research integrity0.0020.001
Insufficient payload (model declined to judge)0.0010.001

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 head0.073
GPT teacher head0.392
Teacher spread0.319 · 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 source (direct Gemma or distilled Codex), 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
Published2018
Admission routes1
Has abstractyes

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