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Record W4382701591 · doi:10.47866/2615-9252/vjfc.4073

Determination of lipophilic marine biotoxins in aquatic products by liquid chromatography coupled with triple quadrupole mass spectrometry

2023· article· en· W4382701591 on OpenAlexaff
Anh Nguyen Tuan, Trac Nguyen Duc Anh, Thien Nguyen Quang, Nhan Le Cong

Bibliographic record

VenueHeavy metals and arsenic concentrations in water agricultural soil and rice in Ngan Son district Bac Kan province Vietnam · 2023
Typearticle
Languageen
FieldEnvironmental Science
TopicMarine Toxins and Detection Methods
Canadian institutionsCanadian Food Inspection Agency
Fundersnot available
KeywordsChromatographyMass spectrometryDetection limitTriple quadrupole mass spectrometerChemistryMarine toxinHigh-performance liquid chromatographyLiquid chromatography–mass spectrometrySolid phase extractionSolventSelected reaction monitoringTandem mass spectrometryOrganic chemistryBiochemistry

Abstract

fetched live from OpenAlex

Lipophilic marine biotoxins include Azaspiracid-1 (AZA-1), Azaspiracid-2 (AZA-2), Azaspiracid-3 (AZA-3), Pectenotoxin-2 (PTX 2), Okadaic acid (OA), Dinophysistoxin-2 (DTX-2), Dinophysistoxin-1 (DTX-1), Yessotoxin (YTX), and 1-Homoyessotoxin (Homo-YTX) were extracted with methanol, followed by cleaning up with solid phase extraction technique (SPE). Lipophilic toxins were confirmed and quantified by liquid chromatography coupled with triple quadrupole mass spectrometry (LC/MS/MS) using calibration curves on the solvent. The quantification limits of this method satisfied the requirements of the European Maximum Residue Limit (MRLs) with 25 µg/kg for the YTX group and 10 µg/kg for AZA, OA, and PTX groups. To validate the effectiveness of this method, matrices of clams, fish, and mixed seafood were collected and analyzed (recovery ranged from 92.4 - 101.5%, and relative standard deviation was less than 20%). The method was used successfully to participate in a proficiency testing program organized by Quasimeme (z-score in the range of ±2)

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)0.000
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: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.730
Threshold uncertainty score0.600

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0010.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.002
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 head0.007
GPT teacher head0.219
Teacher spread0.212 · 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

Citations0
Published2023
Admission routes1
Has abstractyes

Explore more

Same venueHeavy metals and arsenic concentrations in water agricultural soil and rice in Ngan Son district Bac Kan province VietnamSame topicMarine Toxins and Detection MethodsFrench-language works237,207