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- W2040692988 abstract "A method for real-time processing of snapshot data obtained from a capillary electrophoresis instrument using laser-induced fluorescence detection is described. The detector is based on a charge coupled device imaging 25 mm of the capillary length. Output data from the camera consists of a series of snapshot images of the analyte bands moving across the capillary window. The snapshot images are first pre-processed in order to obtain spatial profiles of fluorescence intensity. Each profile from successive snapshot images is processed on-line using a neural network, allowing the real-time measurement of separation parameters such as the velocity and width for each peak. This approach enables dynamic information to be extracted during the separation and the reduction of storage requirements, since the profiles where there are no peaks need not be recorded. The signal processing is based on profile approximation using a radial basis neural network which provides accurate and sensitive peak determination for each camera snapshot. The high spatial resolution of the system and accurate fluorescence profile approximation leads to good peak shape description and therefore to improved deconvolution of overlapping peaks. Examples of real-time analysis of complex peak patterns in separations of sulfonated aluminium phthalocyanines are given." @default.
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- W2040692988 title "Dynamic analysis of capillary electrophoresis data using real-time neural networks" @default.
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