303
Views
8
CrossRef citations to date
0
Altmetric
Hive science products

Ionic liquid dispersive liquid–liquid microextraction for pesticide residue analysis in honey

, , , , , , , & show all
Pages 458-467 | Received 13 Dec 2016, Accepted 03 Apr 2019, Published online: 20 Sep 2019
 

Abstract

Honey is a kind of directly consumed food and has the image of being natural, healthy, and clean. However, today’s honey may be contaminated with pesticides applied in agriculture and distributed in the environment. Ionic liquid-based liquid–liquid microextraction (IL-DLLME) methods combine the advantages of ILs, such as tunable properties, low vapor pressure at room temperature, and lower toxicity (compared to conventional organic solvents), with the advantages of DLLME, such as simplicity of operation, low sample volume, low cost, high recovery, easy operation, low consumption of reagents, high enhancement factor, high preconcentration factors, and shorter analytical time. They are turning into remarkable tools to develop greener sample preparation methods in analytical chemistry. This review focuses on the IL-DLLME techniques used for different pesticide analysis in honey. The advantages, limitations, and future outlook of IL-DLLME method on pesticide analysis in honey are also discussed.

Disclosure statement

No potential conflict of interest was reported by the authors.

Additional information

Funding

This work was supported by the earmarked fund for China Agriculture Research System [CARS-45-KXJ7].

Log in via your institution

Log in to Taylor & Francis Online

PDF download + Online access

  • 48 hours access to article PDF & online version
  • Article PDF can be downloaded
  • Article PDF can be printed
USD 61.00 Add to cart

Issue Purchase

  • 30 days online access to complete issue
  • Article PDFs can be downloaded
  • Article PDFs can be printed
USD 236.00 Add to cart

* Local tax will be added as applicable

Related Research

People also read lists articles that other readers of this article have read.

Recommended articles lists articles that we recommend and is powered by our AI driven recommendation engine.

Cited by lists all citing articles based on Crossref citations.
Articles with the Crossref icon will open in a new tab.