• Journal of Microbiology and Biotechnology
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• v.28 no.11
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• pp.1782-1790
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• 2018

Assessment of microorganism viability is useful in many industrial fields. A large number of methods associated with the use of fluorescent probes have been developed, including fluorimetry, fluorescence microscopy, and cytometry. In this study, a microvolume spectrofluorometer was used to measure the membrane potential variations of Escherichia coli. In order to estimate the sensitivity of the device, the membrane potential of E. coli was artificially disrupted using an ionophore agent: carbonyl cyanide 3-chlorophenylhydrazone. The membrane potential was evaluated using two ratiometric methods: a Rhodamine 123/4',6-diamidino-2-phenylindole combination and a JC-10 ratiometric probe. These methods were used to study the impact of freezing on E. coli, and were compared with the conventional enumeration method. The results showed that it was beneficial to use this compact, easy-to-use, and inexpensive spectrofluorometer to assess the viability of bacterial cells via their membrane potential.

• KSBB Journal
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• v.20 no.4
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• pp.291-298
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• 2005

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.

• Journal of Korea Technical Association of The Pulp and Paper Industry
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• v.43 no.2
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• pp.9-15
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• 2011

Fluorescent whitening agent (FWA) is a widely used chemical in paper industry, but a systematic and scientific method on FWA analysis has not been established. We performed the basic researches on the fluorescence analysis of FWA. The fluorescence of FWA was investigated using a spectrofluorometer and a spectrophotometer. When FWA solution was analyzed using the spectrofluorometer, we found that the peak wavelength of the fluorescence emission was about 440 nm and that of the fluorescence excitation was about 370 nm irrespective of FWA types. Papers dyed with an internal FWA were prepared in a laboratory and the reflectance and the fluorescence index were measured using the spectrophotometer. It was confirmed that the optimum peak wavelength of the reflectance was 440 nm and the fluorescence index calculated from the CIE whiteness with and without UV light under a light source D65 was the best indicator to measure the fluorescence of FWAs exiting in papers.

• KSBB Journal
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• v.21 no.1 s.96
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• pp.59-67
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• 2006

2D spectrofluorometer produces many spectral data during fermentation processes. The fluorescence spectra are analyzed using chemometric methods such as principal component analysis (PCA), principal component regression (PCR) and partial least square regression (PLS). Analysis of the spectral data by PCA results in scores and loadings that are visualized in score-loading plots and used to monitor a few fermentation processes by S. cerevisae and recombinant E. coli. Two chemometric models were established to analyze the correlation between fluorescence spectra and process variables using PCR and PLS, and PLS was found to show slightly better calibration and prediction performance than PCR.

• KSBB Journal
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• v.20 no.4
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• pp.299-304
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• 2005

A two-dimensional (2D) spectrofluorometer was used to monitor various fermentation processes with recombinant E. coli for the production of 5-aminolevulinic acid (ALA). The whole fluorescence spectral data obtained during a process were analyed using artificial neural networks, i.e. self-organizing map (SOM) and feedforward backpropagation neural network (BPNN).Based on the classified fluorescence spectra a supervised BPNN algorithm was used to predict some of the process parameters. It was also shown that the BPNN models could elucidate some sections of the process performance, e.g. forecasting the process performance.