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Record W2115373336 · doi:10.1177/1082013207085914

Molecularly Imprinted Solid Phase Extraction in a Syringe Needle Packed with Polypyrrole-encapsulated Carbon Nanotubes for Determination of Ochratoxin A in Red Wine

2007· article· en· W2115373336 on OpenAlexaff
Yun Wei, Longhui Qiu, Jorn Yu, Edward P. C. Lai

Bibliographic record

VenueFood Science and Technology International · 2007
Typearticle
Languageen
FieldChemistry
TopicAnalytical chemistry methods development
Canadian institutionsCarleton University
Fundersnot available
KeywordsChromatographyDetection limitElutionOchratoxin AChemistryWineSolid phase extractionHigh-performance liquid chromatographyExtraction (chemistry)Mycotoxin

Abstract

fetched live from OpenAlex

A novel micro-solid phase preconcentration (μSPP) device is developed by electrochemically depositing molecularly imprinted polypyrrole (MIPPy) over carbon nanotubes (CNTs) packed inside a 22-gauge syringe needle. The ochratoxin A (OTA) template is removed with 1% triethylamine (TEA) in 20:80 v/v acetonitrile—ammonia buffer (20 mM NH 4 Cl/NH 3 , pH 9.2). This syringe needle is used to extract trace OTA in a red wine sample, and the preconcentrated OTA is eluted with 1% (v/v) TEA in 20:80 v/v acetonitrile—ammonia buffer (20 mM NH 4 Cl/NH 3 , pH 9.2). The eluate is analyzed by high performance liquid chromatography (HPLC) with fluorescence detection (FD). The results demonstrated a significantly selective enrichment of OTA at sub-ppb levels in the presence of red wine matrix components. Using a sample volume of 0.5mL red wine for preconcentration, it is possible to determine OTA down to a detection limit of 0.04 ng/mL (at 3σ B ) or a quantification limit of 0.10 ng/mL (at 10σ B ). The total MIPPy/CNTs-μSPP-HPLC-FD analysis took only 40min, including a μSPP time of 30min, elution time of 20 s, and HPLC analysis time of 10 min. This needle can be reused and hence readily adapted in an autosampler for the processing of multiple samples in series.

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 score0.016
Threshold uncertainty score0.436

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0010.001
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0010.001
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 head0.014
GPT teacher head0.332
Teacher spread0.317 · 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

Citations26
Published2007
Admission routes1
Has abstractyes

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