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Development of a Polymerase Chain Reaction Lateral Flow Immunoassay for Rapid Authentication of Venison in Food Products

2020· article· en· W3116730394 on OpenAlex

Why this work is in the frame

A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.

affAt least one author lists a Canadian institution in the pinned OpenAlex snapshot.
fundA Canadian funder is recorded on the work.

Bibliographic record

VenueACS Food Science & Technology · 2020
Typearticle
Languageen
FieldBiochemistry, Genetics and Molecular Biology
TopicIdentification and Quantification in Food
Canadian institutionsMcGill UniversityUniversity of British Columbia
FundersNatural Sciences and Engineering Research Council of CanadaTianjin Normal UniversityMinistry of Science and Technology of the People's Republic of China
KeywordsAgarose gel electrophoresisChromatographyAgarosePolymerase chain reactionAmpliconImmunoassayDetection limitChemistryMolecular biologyBiologyBiochemistryDNAAntibodyGeneticsGene

Abstract

fetched live from OpenAlex

A novel polymerase chain reaction lateral flow immunoassay (PCR-LFI) test was developed to detect venison in food products in a rapid, inexpensive, and user-friendly manner. The LFI strips allowed the detection of PCR products within 5 min by reading the color signals with the naked eye. The PCR-LFI test uncovered a high specificity for venison with no cross-reactivity to 19 animal and plant species and enabled the detection of raw, oven-heated, and fried venison in binary mixtures with a limit of detection (LOD) of 0.01% (w/w), which was lower than the LOD of PCR agarose gel electrophoresis [0.1% (w/w)]. In addition, the PCR-LFI test was applied to detect 15 commercial venison products, and the results were validated by PCR agarose gel electrophoresis. Given its superiority in terms of cost, reliability, and simplicity, the PCR-LFI test has great potential to be employed as a meat authentication tool in the food industry.

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.

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: Bench or experimental
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.009
Threshold uncertainty score0.371

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0000.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.001
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 head0.029
GPT teacher head0.267
Teacher spread0.238 · 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