Simultaneous optimization by neuro-genetic approach of a multiresidue method for determination of pesticides in Passiflora alata infuses using headspace solid phase microextraction and gas chromatography

• Published in: Journal of Chromatography A 2007-01, Vol.1138 (1), p.251-261
• Main Authors: da Silva, Gilmare Antônia, Augusto, Fabio, Poppi, Ronei Jesus
• Format: Article
• Language: English
• Subjects: Algorithm genetic; Algorithms; Analytical chemistry; Bayesian neural network; Biological and medical sciences; Chemistry; Chromatographic methods and physical methods associated with chromatography; Chromatography, Gas - methods; Doehlert matrix; Exact sciences and technology; Gas chromatographic methods; General pharmacology; HS-SPME-GC-ECD; Medical sciences; Models, Theoretical; Passiflora - chemistry; Passiflora alata Dryander; Pesticides - analysis; Pharmacognosy. Homeopathy. Health food; Pharmacology. Drug treatments; Reproducibility of Results; Simultaneous optimization; Solid Phase Microextraction - methods
• ISSN: 0021-9673
• DOI: 10.1016/j.chroma.2006.10.075

A simultaneous optimization strategy based on neuro-genetic approach has been applied to a HS-SPME-GC-ECD (Headspace Solid Phase Microextraction coupled to Gas Chromatography with Electron Capture Detection) method for simultaneous determination of the pesticides chlorotalonil, methyl parathion, malathion, α-endosulfan and β-endosulfan in herbal infusions of Passiflora alata (Dryander). Two types of extractive fibers were used: a home-made device coated by sol–gel process with polydimethylsiloxane–poly(vinyl alcohol) (PDMS/PVA) and a commercial PDMS. The effects of extraction parameters such as dilution of the infusion, extraction temperature and time, as well as sample ionic strength were evaluated through the Doehlert design. To find a model that could relate these extraction parameters with the extraction efficiency of all pesticide simultaneously, a Bayesian Regularized Artificial Neural Network (BRANN) approach was employed. Subsequently, Genetic Algorithm (GA) was applied to attain the optimum values from the model developed by the neural network. The use of the proposed approach allowed the determination of a single extraction condition that maximized the peak areas of all pesticides simultaneously, showing a promising and a suitable new procedure to the optimization process of complex analytical problems.