首页|Supramolecular solvent-based sample treatment workflow for determination of multi-class drugs of abuse in hair by liquid chromatography-tandem mass spectrometry.
Supramolecular solvent-based sample treatment workflow for determination of multi-class drugs of abuse in hair by liquid chromatography-tandem mass spectrometry.
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NSTL
Elsevier
? 2022Hair is becoming a main matrix for forensic drug analyses due to its large detection window compared to traditional matrices (i.e. urine & blood) and the possibility of establishing the temporal pattern of drug consumption. However, the extremely time- and solvent-consuming nature of conventional sample treatments render it difficult for routine use of hair analysis in forensics. In this paper, this drawback was intended to be addressed by the use of hexanol-based supramolecular solvents (SUPRAS) with restricted-access properties. The aim was to develop a fast and interference-free sample treatment workflow for the determination of opioids, cocaine, amphetamines and their metabolites in human hair. The main variables affecting the extraction were optimized and the method was validated following the European Medical Agency guideline. Major advantages of the proposed method were the straightforward sample preparation, which combines a high extraction yield (93–107%) and matrix effect removal (93–102%SSE) in a single step, the high sample throughput, and the reduced volume of organic solvent required (100 μL of SUPRAS per sample), which makes sample treatment cost-effective and eco-friendly. Method quantification limits were lower enough for all the target drugs (0.5–1.1 pg mg?1) to allow their quantitation in human hair routine analyses. The method was successfully applied to the determination of drugs of abuse in a human hair control sample.
Drugs of abuseHair analysisLiquid chromatography/tandem mass spectrometryMicroextractionSupramolecular solvent
Caballero-Casero N.、Beza G.N.、Rubio S.
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Department of Analytical Chemistry Institute of Fine Chemistry and Nanochemistry Universidad de Córdoba
NSTL
Elsevier
