کد مقاله کد نشریه سال انتشار مقاله انگلیسی نسخه تمام متن
1180239 1491571 2006 11 صفحه PDF دانلود رایگان
عنوان انگلیسی مقاله ISI
Artificial neural networks based on principal component analysis input selection for quantification in overlapped capillary electrophoresis peaks
موضوعات مرتبط
مهندسی و علوم پایه شیمی شیمی آنالیزی یا شیمی تجزیه
پیش نمایش صفحه اول مقاله
Artificial neural networks based on principal component analysis input selection for quantification in overlapped capillary electrophoresis peaks
چکیده انگلیسی

The application of three different kinds of artificial neural networks (ANN) based on principal component analysis (PCA) input selection for quantification of overlapped peaks in micellar electrokinetic capillary chromatography (MECC) is investigated. In the case of overlapped peaks, ANN based on PCA input selection were proved to be a promising approach for quantification of the corresponding components. Both the spectra and the electrophoretograms of the unseparated analytes were used as the multivariate input data. The two kinds of data were both suitable for quantification of overlapped peaks by ANN based on PCA input selection. In the study, it was also shown that the input selection based on PCA for the three kinds of ANN could improve the precision of quantification of the corresponding components in both completely and partially overlapped peaks to some extent.

ناشر
Database: Elsevier - ScienceDirect (ساینس دایرکت)
Journal: Chemometrics and Intelligent Laboratory Systems - Volume 82, Issues 1–2, 26 May 2006, Pages 165–175
نویسندگان
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