Artificial neural networks capillary electrophoresis

Several additional instrumental techniques have also been developed for bacterial characterization. Capillary electrophoresis of bacteria, which requires little sample preparation,42 is possible because most bacteria act as colloidal particles in suspension and can be separated by their electrical charge. Capillary electrophoresis provides information that may be useful for identification. Flow cytometry also can be used to identify and separate individual cells in a mixture.11,42 Infrared spectroscopy has been used to characterize bacteria caught on transparent filters.113 Fourier-transform infrared (FTIR) spectroscopy, with linear discriminant analysis and artificial neural networks, has been adapted for identifying foodbome bacteria25,113 and pathogenic bacteria in the blood.5... 

Artificial Neural Networks Based on Principal Component Analysis Input Selection for Quantification in Overlapped Capillary Electrophoresis Peaks. 

Jalali-Heravi, M., Shen, Y., Hassanisadi, M., and Khaledi, M. G. (2005). Prediction of electrophoretic mobilities of peptides in capillary zone electrophoresis by quantitative structure-mobility relationships using the Offord model and artificial neural networks. Electrophoresis 26,1874—1885. 

Jalali-Heravi, M. and Garkani-Nejad, Z. (2002a) Prediction of electrophoretic mobilities of alkyl-and alkenylpyridines in capillary electrophoresis using artificial neural networks. /. Chromat., 971, 207-215. 

OPTIMIZATION OF THE SEPARATION OF AMINO ACIDS BY CAPILLARY ELECTROPHORESIS USING ARTIFICIAL NEURAL NETWORKS... 

Neural Networits for Optimization of High-Performance Capillary Zone Electrophoresis Methods. A New Meth Using a Combination of Experimental Design and Artificial Neural Networks. 

Casamento, S., et al. "Optimization of the Separation of Organic Erqrlosives by Capillary Electrophoresis with Artificial Neural Networks." Journal of Forensic Sciences 48 (2003), 1075-1083. 

Zhao, R., Xu, G., Yue, B., Lieblich, H.M., Zhang, Y., Artificial neural network classification based on capillary electrophoresis of urinary nucleosides for the clinical diagnosis of tumors. J. Chromatogr. A, 828, 489-496 (1998). 

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