Method development for simultaneous determination of polar and nonpolar pesticides in surface water by low-temperature partitioning extraction (LTPE) followed by HPLC-ESI-MS/MS.

During this research, chemometric approaches were applied for optimization of the low-temperature partitioning extraction (LTPE) for the simultaneous analysis of the pesticides: acephate, difenoconazole, fenamidone, fluazifop, fluazinam, methamidophos, and thiamethoxam from surface water samples and determination by high-performance liquid chromatography-electrospray ionization-tandem mass spectrometry. It was used the 23 full factorial and the Doehlert experimental designs. The extraction technique was optimized by evaluating the effects of the three variables: sample pH, ionic strength (addition of Na2HPO4), and organic solvent volume. Considering the interest to find an optimal condition for all analytes simultaneously, the best extraction parameters found were as follows: pH = 5.33, concentration of Na2HPO4 = 0.0088 mol L-1 and organic phase volume = 4.5 mL. The optimized methodology showed LOD and LOQ levels from 0.33 to 8.13 ng L-1 and from 1.09 to 26.84 ng L-1, respectively. The recovery values ranged from 38.37 and 99.83% and the RSD values varied from 2.33 to 18.92%. The method was applied to surface water analysis sampled in areas with intensive agricultural practices in Ouro Branco City, Minas Gerais, Brazil. The difenoconazole was detected in concentrations between 12.53 and 94.76 ng L-1.

Neuro-genetic multioptimization of the determination of polychlorinated biphenyl congeners in human milk by headspace solid phase microextraction coupled to gas chromatography with electron capture detection.

Polychlorinated biphenyls (PCB) can eventually contaminate breast milk, which is a serious issue to the newborn due to their high vulnerability. Solid phase microextraction (SPME) can be a very convenient technique for their isolation and pre-concentration prior chromatographic analysis. Here, a simultaneous multioptimization strategy based on a neuro-genetic approach was applied to a headspace SPME method for determination of 12 PCB in human milk. Gas chromatography with electron capture detection (ECD) was adopted for the separation and detection of the analytes. Experiments according to a Doehlert design were carried out with varied extraction time and temperature, media ionic strength and concentration of the methanol (co-solvent). To find the best model that simultaneously correlate all PCB peak areas and SPME extraction conditions, a multivariate calibration method based on a Bayesian Neural Network (BNN) was applied. The net output from the neural network was used as input in a genetic algorithm (GA) optimization operation (neuro-genetic approach). The GA pointed out that the best values of the overall SPME operational conditions were the saturation of the media with NaCl, extraction temperature of 95 degrees C, extraction time of 60 min and addition of 5% (v/v) methanol to the media. These optimized parameters resulted in the decrease of the detection limits and increase on the sensitivity for all tested analytes, showing that the use of neuro-genetic approach can be a promising way for optimization of SPME methods.

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.

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, alpha-endosulfan and beta-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.