کد مقاله کد نشریه سال انتشار مقاله انگلیسی ترجمه فارسی نسخه تمام متن
582945 877863 2008 9 صفحه PDF سفارش دهید دانلود رایگان
عنوان انگلیسی مقاله ISI
Application of principal component-artificial neural network models for simultaneous determination of phenolic compounds by a kinetic spectrophotometric method
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موضوعات مرتبط
مهندسی و علوم پایه مهندسی شیمی بهداشت و امنیت شیمی
پیش نمایش صفحه اول مقاله
Application of principal component-artificial neural network models for simultaneous determination of phenolic compounds by a kinetic spectrophotometric method
چکیده انگلیسی
A multicomponent analysis method based on principal component analysis-artificial neural network models (PC-ANN) is proposed for the determination of phenolic compounds. The method relies on the oxidative coupling of phenols (phenol, 2 chlorophenol, 3-chlorophenol and 4-chlorophenol) to N,N-diethyl-p-phenylenediamine in the presence of hexacyanoferrate(III). The reaction monitored at analytical wavelength 680 nm of the dye formed. Phenols can be determined individually over the concentration range 0.1-7.0 μg ml−1. Differences in the kinetic behavior of the four species were exploited by using PC-ANN, to resolve mixtures of phenol. After reducing the number of kinetic data using principal component analysis, an artificial neural network consisting of three layers of nodes was trained by applying a back-propagation learning rule. The optimized ANN allows the simultaneous quantitation of four analytes in mixtures with relative standard errors of prediction in the region of 5% for four species. The results show that PC-ANN is an efficient method for prediction of the four analytes.
ناشر
Database: Elsevier - ScienceDirect (ساینس دایرکت)
Journal: Journal of Hazardous Materials - Volume 157, Issue 1, 30 August 2008, Pages 161-169
نویسندگان
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