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Record W2907894591 · doi:10.1002/9780470027318.a9605

Analysis of Herbicide and/or Pesticide Residues in Dietary Botanical Supplements

2018· other· en· W2907894591 on OpenAlexafffund
Renata Raina‐Fulton, Ghada Aborkhees, Asal Behdarvandan

Bibliographic record

VenueEncyclopedia of Analytical Chemistry · 2018
Typeother
Languageen
FieldAgricultural and Biological Sciences
TopicPesticide Residue Analysis and Safety
Canadian institutionsUniversity of Regina
FundersNatural Sciences and Engineering Research Council of Canada
KeywordsQuechersChemistrySolid phase extractionPesticideChromatographyGas chromatography–mass spectrometryPesticide residueSample preparationExtraction (chemistry)Mass spectrometryAgronomy

Abstract

fetched live from OpenAlex

Abstract Analysis of pesticides in nutraceuticals, particularly dietary botanical supplements, is challenging owing to the low water content of powder or tablet samples and potentially higher concentration of co‐extracts produced during the drying and manufacturing processes. These co‐extracts can cause signal suppression or enhancement in mass spectrometric (MS) detection. Poor chromatographic stability and peak shapes can also be observed from co‐eluting matrix components. Sample preparation is critical to the detection of pesticides in extracts, and the major sample preparation methods used for dietary botanical supplements included modified QuEChERS (quick, easy, cheap, effective, rugged, and safe) methods, dilute‐and‐shoot, and pressurized liquid extraction (PLE) with in‐cell (on‐line) cleanup. Additional cleanup of extracts was completed by dispersive solid‐phase extraction (dSPE) or solid‐phase extraction (SPE). Recoveries of individual pesticides from a variety of chemical classes of varying polarities were evaluated using the major sample preparation approaches utilized for analysis of pesticides since 2010. Major chemical classes of pesticides included those predominately analyzed by GC‐MS/MS (pyrethroid insecticides) or LC‐MS/MS (carbamates, sulfonyl ureas, phenyl ureas, and neonicotinoid insecticides) and those that have greater flexibility to be analyzed by GC‐MS/MS or LC‐MS/MS (azole and strobilurin fungicides). Other selected herbicides including cyclohexene oxime herbicides, aryloxyphenoxy propionic herbicides, or fungicides that were included in multiresidue analysis methods were also examined. Common dSPE sorbents included primary secondary amine (PSA), octyldecyl silane (C18), graphitized carbon black (GCB), and zirconia and C18 bonded to silica (Z‐Sep + ), while carbon‐based sorbents were used for SPE along with Florisil ® . C18 and PSA were also used for SPE often in combination with a carbon‐based sorbent. Highlighted are the issues with different chemical classes or sample matrix types.

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: Bench or experimental · Consensus signal: Bench or experimental
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.002
Threshold uncertainty score0.004

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0000.000
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)0.0010.000

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 head0.016
GPT teacher head0.266
Teacher spread0.250 · 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

Citations2
Published2018
Admission routes2
Has abstractyes

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