Application of principal component analysis and self-organizing map to the analysis of 2D fluorescence spectra and the monitoring of fermentation processes |
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Authors: | Jong Il Rhee Tae-Hyoung Kang Kum-Il Lee Ok-Jae Sohn Sun-Yong Kim Sang-Wook Chung |
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Institution: | (1) Chemistry Department, St Petersburg State University, Universitetskaya nab. 7/9, 199034 St Petersburg, Russia |
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Abstract: | 2D fluorescence sensors produce a great deal of spectral data during fermentation processes, which can be analyzed using a
variety of statistical techniques. Principal component analysis (PCA) and a self-organizing map (SOM) were used to analyze
these 2D fluorescence spectra and to extract useful information from them. PCA resulted in scores and loadings that were visualized
in the score-loading plots and used to monitor various fermentation processes with recombinantEscherichia coli andSaccharomyces cerevisiae. The SOM was found to be a useful and interpretative method of classifying the entire gamut of 2D fluorescence spectra and
of selecting some significant combinations of excitation and emission wavelengths. The results, including the normalized weights
and variances, indicated that the SOM network is capable of being used to interpret the fermentation processes monitored by
a 2D fluorescence sensor. |
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Keywords: | |
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