Organic chemistry
Shahryar Abbasi; Mahmoud Roushani; Hadi Noorizadeh
Abstract
In this work, we developed a method based on ultrasound-assisted emulsification microextraction (USAEME) for the determination of zinc and copper by flame atomic absorption spectrometry (FAAS). The method is based on the use of the organic solvent carbon tetrachloride (CCl4) as an extraction solvent. ...
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In this work, we developed a method based on ultrasound-assisted emulsification microextraction (USAEME) for the determination of zinc and copper by flame atomic absorption spectrometry (FAAS). The method is based on the use of the organic solvent carbon tetrachloride (CCl4) as an extraction solvent. In order to obtain a high enrichment factor, the effect of different parameters affecting the complex formation and extraction conditions (such as the type and volume of the extraction solvent, pH, the chelating agent amount, extraction time, extraction temperature and ionic strength) were tested. Under optimum conditions, the eight replicates mixture of the 100 ngmL-1 and 50 ngmL-1 for Zn(II) and Cu(II) ions, gave a mean absorbance of 0.055 and 0.061, with a relative standard deviation (RSD) of ±%3.2 and 2.9, respectively. The equations for the lines were A = 0.4921C + 0.0027 (R = 0.9998) and A = 1.0701C + 0.0032 (R = 0.9997), respectively. The limit of detection for Zn (II) and Cu(II) ions were 1.06 and 1.4 ngL−1, respectively. The calibration graph was linear in the range of 3.0–2000.0 ngmL−1 and 2.0-850.0 ngmL−1 for Zn and Cu respectively. In the proposed procedure, enhancement factors were 9.51 and 6.25 for Zn and Cu, respectively. This proposed method was successfully applied in the analysis of four real environmental water samples and good spiked recoveries over the range of 98.4–103.0% were obtained. This is a first research used USAEME for simultaneous determination Zn and Cu in water.
Analytical chemistry
Hadi Noorizadeh; Sharmin Esmaeilpoor; Zohreh Moghadam; Shahnaz Nosratolahy
Volume 2, Issue 4, pp. 236-325, Serial No. 5 , October 2014, , Pages 283-299
Abstract
The veterinary drugs residues are also important pollutants found in milk, since veterinary drugs are commonly used in cattle management. Considering the role of milk in human nutrition and its wide consumption throughout the world, it is very important to ensure the milk quality. A quantitative structure–retention ...
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The veterinary drugs residues are also important pollutants found in milk, since veterinary drugs are commonly used in cattle management. Considering the role of milk in human nutrition and its wide consumption throughout the world, it is very important to ensure the milk quality. A quantitative structure–retention relationship (QSRR) was developed using the partial least square (PLS), Kernel PLS (KPLS) and Levenberg-Marquardt artificial neural network (L-M ANN) approach for chemometrics study. Genetic algorithm was employed as a factor selection procedure for PLS and KPLS modeling methods. By comparing the results, GA-KPLS descriptors are selected for L-M ANN. Finally a model with a low prediction error and a good correlation coefficient was obtained by L-M ANN. This is the first research on the QSRR of veterinary drugs using the chemometrics models.
Nanochemistry
Sharmin Esmaeilpoor; Zahra Shirzadi; Hadi noorizadeh
Volume 2, Issue 1, pp. 1-81, Serial No. 2 , January 2014, , Pages 56-71
Abstract
The quantitative structure-retention relationship (QSRR) of nanoparticles in roadside atmosphere against the comprehensive two-dimensional gas chromatography which was coupled to high-resolution time-of-flight mass spectrometry was studied. The genetic algorithm (GA) was employed to select the variables ...
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The quantitative structure-retention relationship (QSRR) of nanoparticles in roadside atmosphere against the comprehensive two-dimensional gas chromatography which was coupled to high-resolution time-of-flight mass spectrometry was studied. The genetic algorithm (GA) was employed to select the variables that resulted in the best-fitted models. After the variables were selected, the linear multivariate regressions [e.g. the partial least squares (PLS)] as well as the nonlinear regressions [e.g. the kernel PLS (KPLS) and Levenberg- Marquardt artificial neural network (L-M ANN)] were utilized to construct the linear and nonlinear QSRR models. The correlation coefficient cross validation (Q2) and relative error for test set L-M ANN model are 0.939 and 4.89, respectively. The resulting data indicated that L-M ANN could be used as a powerful modeling tool for the QSPR studies.
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