Dispersive Liquid-Liquid Microextraction Based on Solidification of Floating Organic Droplet Technique Combined With Gas Chromatography-Mass Spectrometry for PBDEs Determination in Sediment Samples.

LANA, N.B.; BERTON, P.; CIOCCO, N.F.; ALTAMIRANO, J

Bruselas

Simposio; Dioxin 2011 - 31st International Symposium on Halogenated Persistent Organic Pollutants; 2011

Polybrominated diphenyl ethers (PBDEs) have been used extensively over the past two decades as additiveflame retardants (FRs) in most types of polymers to prevent ignition and to slow the initial phase of combustion.On the other hand, PBDEs are considered persistent organic pollutants because of their ubiquity, persistence andaccumulation in the environment. Its harmful effects on human health and the environment, has led to itsinclusion of the Stockholm Convention in 2009.In the past few years new extraction techniques, especially in the microextraction category, have gained interestfor PBDEs determination in biological and environmental samples. Efforts have been placed on theminiaturization of the liquid-liquid extraction procedure by greatly reducing the required organic solventamount. In this way dispersive L-L microextraction and further solidification of floating organic droplet(DLLME-SFO) technique has been developed and proposed as a new analytical approach for extracting,cleaning up and preconcentrating polybrominated diphenyl ethers (PBDEs) from sediment samples prior gaschromatography-tandem mass spectrometry (GC-MS/MS) analysis. Statistical analysis was used to evaluate thesignificance of the microextraction factors, and determine which combination leads to the optimum results. Thecombination of microextraction and chemometrics tools significantly simplify sample processing, and addressesproblems related to improvement in detectability and method validation6. In the present work, the study andoptimization of the DLLME-SFO procedure for determination of PBDEs in sediment samples by GC-MS/MSwas carried out through a multivariate approach by using 2k-1 factorial and response-surface designs4.Desirability function was used to optimize the multiple response criteria based on analytes’ peak areas4.