4.7 Article

Monolithic capsule phase microextraction prior to gas chromatography-mass spectrometry for the determination of organochlorine pesticides in environmental water samples

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MICROCHEMICAL JOURNAL
卷 186, 期 -, 页码 -

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ELSEVIER
DOI: 10.1016/j.microc.2022.108355

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Organochlorine pesticides; Capsule phase microextraction; Water; Sample preparation; GC-MS; Monolithic sorbents

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A CPME protocol followed by GC-MS was proposed for accurate and sensitive monitoring of OCPs in environmental water samples. Monolithic sorbents were compared, and a sol-gel PEG-based sorbent in porous extraction capsules showed the highest extraction efficiency. LODs for OCPs ranged from 0.01 ng mL-1 to 0.03 ng mL-1 under optimized CPME conditions. The developed method demonstrated good trueness with relative recoveries between 90.5% and 105.2% (intra-day) and 93.0% and 105.0% (inter-day).
In this study, a capsule phase microextraction (CPME) protocol followed by gas chromatography-mass spec-trometry is proposed for the accurate and sensitive monitoring of organochlorine pesticides (OCPs) in envi-ronmental water samples. Different monolithic sol-gel encapsulated sorbents were compared and monolithic sol-gel poly(ethylene glycol)-based sorbent incorporated into porous microextraction capsules resulted in the highest extraction efficiency. Following the selection of the microextraction device, the CPME conditions were optimized, while linearity, limits detection (LODs), limits of quantification (LOQs), accuracy, and precision were the figures-of-merit measured. Under optimum conditions the LODs for the OCPs ranged between 0.01 ng mL-1 and 0.03 ng mL-1. The relative standard deviations were better than 5.3 % and 9.2 % for intra-day and inter-day study, respectively. The relative recoveries ranged between 90.5 and 105.2 % (intra-day) and 93.0-105.0 % (inter-day), demonstrating good method trueness. Finally, the proposed protocol was implemented for the monitoring of OCPs in tap, river, pond, and lake water. The developed method presents better analytical features than existing methods.

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