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Record W4362523181 · doi:10.3724/sp.j.1123.2022.05016

Determination of seven monoaromatic hydrocarbon metabolites by ultra performance liquid chromatography-tandem mass spectrometry

2023· article· en· W4362523181 on OpenAlexaboutno aff
Tian Qiu, Xu Zhang, Yang Yan-wei, Xiaojian Hu, Song Luo, Ying Zhu

Bibliographic record

VenueChinese Journal of Chromatography · 2023
Typearticle
Languageen
FieldChemistry
TopicAnalytical Chemistry and Chromatography
Canadian institutionsnot available
Fundersnot available
KeywordsChemistryChromatographyFormic acidMass spectrometrySolid phase extractionElutionTandem mass spectrometryTriple quadrupole mass spectrometerSelected reaction monitoringDetection limitMethanolElectrospray ionizationExtraction (chemistry)TolueneHigh-performance liquid chromatography

Abstract

fetched live from OpenAlex

Monoaromatic hydrocarbons (MAHs) such as benzene, toluene, and xylene are important anthropogenic pollutants in the urban atmosphere. The detection of urinary MAH metabolites are included in human biomonitoring programs in several countries, including Canada, the United States, Italy, and Germany, because their evaluation is vital to monitor the exposure of humans to MAHs. To this end, herein, a method was developed for the determination of seven MAH metabolites through ultra performance liquid chromatography-tandem mass spectrometry (UPLC-MS/MS). An aliquot of 0.5 mL urine was fortified with an isotopic labeled internal standard solution before being hydrolyzed by 40 μL of 6 mol/L HCl solution, followed by extraction using a 96-well EVOLUTE®EXPRESS ABN solid-phase extraction plate. The samples were washed with 1.0 mL of methanol-water (10∶90, v/v) and eluted with 1.0 mL methanol. The eluate was diluted four times with water prior to use in instrumental analysis. Chromatographic separation was achieved using an ACQUITY UPLC HSS T3 column (100 mm×2.1 mm, 1.8 μm), with gradient elution using 0.1% formic acid as mobile phase A and methanol as mobile phase B. The detection of seven analytes was performed using a triple-quadrupole mass spectrometer equipped with a negative electrospray ionization source in the multiple reaction monitoring mode. The linear ranges of the seven analytes varied from 0.1-20 μg/L to 2.5-500 mg/L, with correlation coefficients greater than 0.995. The method detection limits were 1.5, 0.02, 0.1, 900, 0.6, and 4 μg/L for trans,trans-muconic acid (MU), S-phenylmercapturic acid (PMA), S-benzylmercapturic acid (BMA), hippuric acid (HA), 2-methyl hippuric acid (2MHA), and 3-methyl hippuric acid (3MHA)+4-methyl hippuric acid (4MHA), respectively. The limits of quantification were 5, 0.05, 0.4, 3000, 2, and 12 μg/L for MU, PMA, BMA, HA, 2MHA, and 3MHA+4MHA, respectively. The method was verified by spiking urine samples at three different concentration levels, with recovery rates ranging from 84% to 123%. The intra- and inter-day precisions were 1.8%-8.6% and 1.9%-21.4%, respectively. The extraction efficiencies were 68%-99%, and the matrix effects ranged from -11% to -87%. The urine samples obtained from the German external quality assessment scheme (round 65) were used to assess the accuracy of this method. Both high and low concentrations of MU, PMA, HA, and methyl hippuric acid were within the tolerance range. All analytes in the urine samples were found to be stable for up to seven days at room temperature (20 ℃, absence of light), with less than 15% change in concentration. Analytes in urine samples were found to be stable for at least 42 d at 4 ℃ and -20 ℃, or for six freeze-thaw cycles and up to 72 h in an autosampler (8 ℃). The method was applied to the analysis of 16 non-smokers’ and 16 smokers’ urine samples. The detection rates of MU, BMA, HA, and 2MHA were 100% in both non-smokers’ and smokers’ urine samples. PMA was detected in 75% non-smokers’ and 100% smokers’ urine samples. 3MHA+4MHA was detected in 81% non-smokers’ urine and in all smokers’ urine samples. Statistical differences were found for MU, PMA, 2MHA, and 3MHA+4MHA between the two groups (p<0.001). The established method has good robustness and can provide reliable results. The experiments were carried out in a high-throughput manner with large sample sizes, owing to the small sample volume, and allowed the successful detection of the seven MAH metabolites in human urine.

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)0.001
metaresearch head score (Gemma)0.001
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: Methods · Consensus signal: none
Teacher disagreement score0.001
Threshold uncertainty score0.004

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0010.001
Meta-epidemiology (narrow)0.0010.000
Meta-epidemiology (broad)0.0010.001
Bibliometrics0.0010.001
Science and technology studies0.0010.000
Scholarly communication0.0010.001
Open science0.0010.001
Research integrity0.0010.001
Insufficient payload (model declined to judge)0.0010.001

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 head0.006
GPT teacher head0.230
Teacher spread0.224 · 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
GenreMethods

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".

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Citations0
Published2023
Admission routes1
Has abstractyes

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