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.