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Analysis of Two-Dimensional Fluorescence Spectra in Biotechnological Processes by Artificial Neural Networks I - Classification of Fluorescence Spectra using Self-Organizing Maps -  

Lee Kum-Il (Department of Industrial Engineering, Chonnam National University, Research Center for Biophotonics)
Yim Yong-Sik (Department of Industrial Engineering, Chonnam National University, BioProcess Technology Lab.)
Kim Chun-Kwang (Department of Industrial Engineering, Chonnam National University, BioProcess Technology Lab.)
Lee Seung-Hyun (Department of Industrial Engineering, Chonnam National University, BioProcess Technology Lab.)
Chung Sang-Wook (Department of Industrial Engineering, Chonnam National University, Research Center for Biophotonics)
Rhee Jong Il (Faculty of Applied Chemical Engineering, Chonnam National University, BioProcess Technology Lab.)
Publication Information
KSBB Journal / v.20, no.4, 2005 , pp. 291-298 More about this Journal
Abstract
Two-dimensional (2D) spectrofluorometer is often used to monitor various fermentation processes. The change in fluorescence intensities resulting from various combinations of excitation and emission wavelengths is investigated by using a spectra subtraction technique. But it has a limited capacity to classify the entire fluorescence spectra gathered during fermentations and to extract some useful information from the data. This study shows that the self-organizing map (SOM) is a useful and interpretative method for classification of the entire gamut of fluorescence spectral data and selection of some combinations of excitation and emission wavelengths, which have useful fluorometric information. Some results such as normalized weights and variances indicate that the SOM network is capable of interpreting the fermentation processes of S. cerevisiae and recombinant E. coli monitored by a 2D spectrofluorometer.
Keywords
Bioprocess monitoring fermentation; self-organizing map sensors; 2D spectrofluorometer;
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