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Record W2079780635 · doi:10.3390/toxins2061536

Molecularly Imprinted Polymers for Ochratoxin A Extraction and Analysis

2010· review· en· W2079780635 on OpenAlexaff
Jorn Yu, Edward P. C. Lai

Bibliographic record

VenueToxins · 2010
Typereview
Languageen
FieldChemistry
TopicAnalytical chemistry methods development
Canadian institutionsCarleton University
Fundersnot available
KeywordsMolecularly imprinted polymerOchratoxin AOchratoxinsExtraction (chemistry)OchratoxinSolid phase extractionMolecular imprintingChromatographyComputer scienceBiochemical engineeringNanotechnologyChemistryMaterials scienceOrganic chemistryMycotoxinSelectivity

Abstract

fetched live from OpenAlex

Molecularly imprinted polymers (MIPs) are considered as polymeric materials that mimic the functionality of antibodies. MIPs have been utilized for a wide variety of applications in chromatography, solid phase extraction, immunoassays, and sensor recognition. In this article, recent advances of MIPs for the extraction and analysis of ochratoxins are discussed. Selection of functional monomers to bind ochratoxin A (OTA) with high affinities, optimization of extraction procedures, and limitations of MIPs are compared from different reports. The most relevant examples in the literature are described to clearly show how useful these materials are. Strategies on MIP preparation and schemes of analytical methods are also reviewed in order to suggest the next step that would make better use of MIPs in the field of ochratoxin research. The review ends by outlining the remaining issues and impediments.

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)0.000
metaresearch head score (Gemma)0.000
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Not applicable · Consensus signal: none
GenreCandidate signal: Review · Consensus signal: Review
Teacher disagreement score0.003
Threshold uncertainty score0.010

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0000.000
Meta-epidemiology (narrow)0.0010.000
Meta-epidemiology (broad)0.0010.000
Bibliometrics0.0010.001
Science and technology studies0.0000.000
Scholarly communication0.0010.001
Open science0.0010.000
Research integrity0.0010.001
Insufficient payload (model declined to judge)0.0030.005

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 head0.042
GPT teacher head0.375
Teacher spread0.333 · 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 designNot applicable
Domainnot available
GenreReview

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

Citations55
Published2010
Admission routes1
Has abstractyes

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