A systematic approach for optimisation of supercritical-fluid extraction of polycyclic aromatic hydrocarbons from earthworms
- 66 Downloads
- 5 Citations
Abstract
A systematic approach using a mathematical model as an alternative to time-consuming empirical optimisation of a supercritical-fluid extraction (SFE) procedure is presented. The model was applied to the extraction of 15 polycyclic aromatic hydrocarbons (PAH). The selected fat-containing matrix is the earthworm used in ecotoxicological absorption studies. Settings for optimal recovery were established for the important parameters (temperature, pressure, amount of trapping sorbent, flow, and dynamic extraction time) using a D-optimal experimental design (including quadratic terms and interactions). The recoveries were modelled using a two sigmoid-model with parameters for each of the individual PAH. The main objective was to optimise the conditions for 15 PAH congeners by maximisation of the lowest recovery. The results show that for some parameters, e.g. the amount of sorbent material, optimal conditions are identical for all PAH. For other parameters, e.g. extraction time, the optimum is PAH dependent. The advantage of this optimisation procedure is that, within three days of analysis (73 experiments), optimised extraction conditions for the extraction of the set of 15 PAH were found but also optimum conditions for specific subsets can be extracted from the collected data for specific subsets.
Keywords
Experimental design PAH SFE Optimisation Microcontaminants Multi-methodReferences
- 1.Stolker AAM, Zoontjes PW, Schwillens PLWJ, Kootstra PR, van Ginkel LA, Stephany RA, Brinkman UATh (2002) Analyst 127:748–754CrossRefPubMedGoogle Scholar
- 2.Stolker AAM, van Tricht EF, Zoontjes PW, van Ginkel LA, Stephany RW (2003) Anal Chim Acta 483:1–9CrossRefGoogle Scholar
- 3.Juhler RK (1998) Analyst 123:1551–1556CrossRefPubMedGoogle Scholar
- 4.Careri M, Furlattine L, Mangia M, Musci M, Anklam E, Theobald A, van Holst C (2001) J Chromatogr A 912:61–71CrossRefPubMedGoogle Scholar
- 5.Van Holst C, Maio G, Wenclawiak BW, Darskus RD (2000) Fresenius J Anal Chem 368:378–383CrossRefPubMedGoogle Scholar
- 6.Stolker AAM, Sipoli Marques MA, Zoontjes PW, van Ginkel LA (1996) Semin Food Anal 1:117–132Google Scholar
- 7.Jager T, Anton Sanches FA, Muijs B, van der Velde EG, Posthuma L (2000) Environ Toxicol Chem 19:953–961Google Scholar
- 8.Moret S, Conte LS (2000) J Chromatogr A 882:245–253CrossRefPubMedGoogle Scholar
- 9.Mooibroek D, Hoogerbrugge R, Stoffelsen BHG, Dijkman E, Berkhoff CJ, Hogendoorn EA (2002) J Chromatogr A 975:165–173PubMedGoogle Scholar
- 10.Matlab, Statistics Toolbox 3.0, The Mathworks, Natick, MA, USAGoogle Scholar
- 11.Van der Velde EG, Ramlal MR, van Beuzekom AC, Hoogerbrugge R (1994) J Chromatogr A 683:125–139CrossRefGoogle Scholar
- 12.Turner C, Eskilsson CS, Björklund E (2002) J Chromatogr A 947:1–22CrossRefPubMedGoogle Scholar
- 13.Massart DL, Vandeginste BGM, Buydens LMC, de Jong S, Lewi PJ, Smeyers-Verbeke J (1997) Handbook of chemometrics and qualimetrics part A. Elsevier, AmsterdamGoogle Scholar