• Biotechnology and Bioprocess Engineering:BBE
• /
• v.11 no.5
• /
• pp.432-441
• /
• 2006

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 recombinant Escherichia coli and Saccharomyces 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.

• KSBB Journal
• /
• v.13 no.3
• /
• pp.244-250
• /
• 1998

Flow-injection analysis (FIA) for on-line monitoring of glucose and acetate are described and employed in E.coli fermentation process. Glucose oxidase (GOD) for the detection of glucose is immobilized on epoxy polymer support, which is packed in a small cartirdge, and applied to a GOD-FIA system. The detection of acetate is based on the inhibition of acetate to the oxidation of sarcosine by sarcosine oxidase (SOD). SOD is also immobilized on epoxy polymer support and used for a SOD-FIA system. GOD-FIA system is characterized as well as SOD-FIA system by the investigation of the effects of pH, temperature and metabolites in samples on the peak height. GOD-FIA and SOD-FIA systems were also applied for on-line On-line measurements buy FIA measurements by FIA were in god agreement with off-line measurements.

• Biotechnology and Bioprocess Engineering:BBE
• /
• v.2 no.2
• /
• pp.69-72
• /
• 1997

The pH of microbial culture medium was calculated from equations of equilibrium, meterial balances for ionic components and electro-neutrality theory. Ammonium ion consumption and Acetic acid production are found out to be the major contributors for the alteration of the pH as well as the buffer capacity of the medium. By measuring the buffer capacity on-line, levels of acetic acid were estimated by a software sensor using pH signal in a fermentation process of E.coli growing in a minimal medium. The measured values of acetic acid showed good correlation to those of estimated by the software sensor.

• Biotechnology and Bioprocess Engineering:BBE
• /
• v.11 no.4
• /
• pp.351-356
• /
• 2006

This study developed an artificial neural network (ANN) to estimate the growth of microorganisms during a fermentation process. The ANN relies solely on the cumulative consumption of alkali and the buffer capacity, which were measured on-line from the on/off control signal and pH values through automatic pH control. The two input variables were monitored on-line from a series of different batch cultivations and used to train the ANN to estimate biomass. The ANN was refined by optimizing the network structure and by adopting various algorithms for its training. The software estimator successfully generated growth profiles that showed good agreement with the measured biomass of separate batch cultures carried out between at 25 and $35^{\circ}C$.

• KSBB Journal
• /
• v.10 no.2
• /
• pp.126-130
• /
• 1995

A membrane gas sensor was developed for the measurement of ethanol concentration during acetic acid fermentation. The fermentation broth including ethanol was permeated through the silicone membrane by synthetic air as a carrier gas and was detected by a semiconductor gas sensor. The optimum conditions of membrane gas sensor were 20m1/min of flow rate and 0.5mm of membrane thickness. In acetic acid fermentation, an on-line measurement of ethanol concentration was conducted by the proposed membrane gas sensor and then the on-line sensor signal, was compared with the result of off-line analysis by gas chromatography. As a result, a correlated response over the range of $0∼70g/\ell$ was shown between membrane gas sensor and gas chromatography and this use of membrane gas sensor was experimentally ascertained for the monitoring and control of bioprocess like acetic acid fermentation.

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

• Biotechnology and Bioprocess Engineering:BBE
• /
• v.5 no.5
• /
• pp.355-361
• /
• 2000

A recycling reactor system operated under sequential anoxic and oxic conditions for the treatment of swine wastewater has been developed, in which piggery slurry is fermentatively and aerobically treated and then part of the effluent is recycled to the pigsty. This system significantly removes offensive smells (at both the pigsty and the treatment plant), BOD and others, and may be cost effective for small-scale farms. The most dominant heterotrophic were, in order, Alcaligenes faecalis, Brevundimonas diminuta and Streptococcus sp., while lactic acid bacteria were dominantly observed in the anoxic tank. We propose a novel monitoring system for a recycling piggery slurry treatment system through the use of neural networks. In this study, we tried to model the treatment process for each tank in the system (influent, fermentation, aeration, first sedimentation and fourth sedimentation tanks) based upon the population densities of the heterotrophic and lactic acid bacteria. Principal component analysis(PCA) was first applied to identify a relationship between input and output. The input would be microbial densities and the treatment parameters, such as population densities of heterotrophic and lactic acid bacteria, suspended solids(SS), COD, NH$_4$(sup)+-N, ortho-phosphorus (o-P), and total-phosphorus (T-P). then multi-layer neural networks were employed to model the treatment process for each tank. PCA filtration of the input data as microbial densities was found to facilitate the modeling procedure for the system monitoring even with a relatively lower number of imput. Neural network independently trained for each treatment tank and their subsequent combined data analysis allowed a successful prediction of the treatment system for at least two days.