Monitoring Of Fermentation Process Research Articles

Transponder-based sensor for monitoring electrical properties of biological cell solutions

An inductive passive remote sensor circuit for monitoring fermentation processes is presented. The sensor circuit consists of an interdigital capacitor and a planar coil structured on a glass laminated FR4-printed circuit board. This circuit resonates at frequencies between 2 and 4 MHz. After the resonant sensor circuit is immersed in a fermentation vessel with a cell solution, the resonant frequencies are detected by measuring the impedance of an external loop antenna. A new theory is presented to describe the behavior of the sensor circuit. In combination with a proposed equivalent circuit, the theory enables the calculation of the permittivity and conductivity of the cell solution under test by determining the resonant frequencies of the sensor without the need for any additional fitting functions. The influence of the relaxation behavior of living cells on the sensor signal with respect to the conductivity of the solution is discussed in detail. To prove the new theory, the determined permittivity is compared with the optical density of a cell solution, an indicator of cell concentration. The performed measurements show the expected correlation between the determined permittivity and optical density. The solution under test is a yeast culture in YPG medium.

On-line monitoring of continuous beer fermentation process using automatic membrane inlet mass spectrometric system

A fully automatic membrane inlet mass spectrometric (MIMS) on-line instrumentation for the analysis of aroma compounds in continuous beer fermentation processes was constructed and tested. The instrumentation includes automatic filtration of the sample stream, flushing of all tubing between samples and pH control. The calibration standards can be measured periodically. The instrumentation has also an extra sample line that can be used for off-line sample collection or it can be connected to another on-line method. Detection limits for ethanol, acetic acid and eight organic beer aroma compounds were from μg l −1 to low mg l −1 levels and the standard deviations were less than 3.4%. The method has a good repeatability and linearity in the measurement range. Response times are shorter than or equal to 3 min for all compounds except for ethyl caproate, which has a response time of 8 min. In beer aroma compound analysis a good agreement between MIMS and static headspace gas chromatographic (HSGC) measurements was found. The effects of different matrix compounds commonly present in the fermentation media on the MIMS response to acetaldehyde, ethyl acetate and ethanol were studied. Addition of yeast did not have any effect on the MIMS response of ethanol or ethyl acetate. Sugars, glucose and xylose, increased the MIMS response of all studied analytes only slightly, whereas salts, ammonium chloride, ammonium nitrate and sodium chloride, increased the MIMS response of all three studied compounds prominently. The system was used for on-line monitoring of continuous beer fermentation with immobilised yeast. The results show that with MIMS it is possible to monitor the changes in the continuous process as well as delays in the two-phase process.

Multicomponent analysis of fermentation growth media using the electronic tongue (ET)

A potentiometric electronic tongue (ET) consisting of eight cross-sensitive chemical sensors and a standard pH electrode has been applied for analysis of simulated fermentation solutions typical for fermentation processes with Aspergillus niger. The electronic tongue has been found capable of simultaneous determination of ammonium, citrate and oxalate in complex media with good precision (typical error within 8%). The system preserved high sensitivity to the targeted substances also in the presence of sodium azide, which is commonly used for suppressing microbial activity in real-world fermentation samples. Sensor performance was fast and reproducible which promises well for routine application of the electronic tongue for fermentation process monitoring.

Multivariate monitoring of fermentation processes with non-linear modelling methods

Multiway principal components analysis (MPCA) and parallel factor analysis (PARAFAC) are widely used in exploratory data analysis and multivariate statistical process control (MSPC). These models are linear in nature, thus, limited when non-linear relations are present in the data. Principal component analysis (PCA) can be extended to non-linear principal components analysis using autoassociative neural networks. In this paper, the network’s bottleneck layer outputs (non-linear components) were made orthogonal. A method to estimate confidence limits based on a kernel probability density function was proposed since these limits do not assume that the non-linear scores are normally distributed. A measure for the non-linear scores ( D NL) was presented here to monitor on-line the process replacing the well known Hotelling’s T 2 statistic. One hundred and two industrial fermentation runs were used to evaluate the performance of a non-linear technique for multivariate process statistical monitoring. Three process runs with faults were used to compare the error detection performance using a statistic for the non-linear scores and the residuals statistic (SPE).

The Real-Time Optimisation of an Industrial Fermentation Process

Abstract A system for the on-line performance monitoring and real-time optimisation of a typical industrial fermentation processes is described. This is implemented in a commercial software framework and can be applied to a variety of industrial processes.

Fermentation monitoring using multisensor systems: feasibility study of the electronic tongue.

The electronic tongue based on an array of 30 non-specific potentiometric chemical sensors has been applied to qualitative and quantitative monitoring of a batch fermentation process of starting culture for light cheese production. Process control charts were built by using PLS regression and data from fermentations run under "normal" operating conditions. Control charts allow discrimination of samples from fermentation batches run under "abnormal" operating conditions from "normal" ones at as early as 30-50% of fully evolved fermentations. The capability of the electronic tongue to quantify concentrations of important organic acids (citric, lactic and orotic) in the present type of fermentation media was demonstrated. Average prediction errors were assessed in the range 5-13% based on test set validation. Correlation between peptide profiles determined using HPLC and the electronic tongue output was also established. The electronic tongue was demonstrated to be a promising tool for fermentation process monitoring and quantitative analysis of growth media.

Integrated condition monitoring and control of fed-batch fermentation processes

This paper investigates the benefits that the partial least squares (PLS) modelling approach offers engineers involved in the operation of fed-batch fermentation processes. It is shown that models developed using PLS can be used to provide accurate inference of quality variables that are difficult to measure on-line, such as biomass concentration. It is further shown that this model can be used to provide fault detection and isolation capabilities and that it can be integrated within a standard model predictive control framework to regulate the growth of biomass within the fermenter. This model predictive controller is shown to provide its own monitoring capabilities that can be used to identify faults within the process and also within the controller itself. Finally it is demonstrated that the performance of the controller can be maintained in the presence of fault conditions within the process.

Monitoring of yeast fermentation by ion mobility spectrometry measurement and data visualisation with Self-Organizing Maps

Ion mobility spectrometry (IMS) measurement combined with unsupervised neurocomputing is considered as a new potential method for on-line monitoring of fermentation and other processes producing volatile compounds that involve micro-organisms. This was demonstrated in a model system in which a strain of brewer’s yeast ( Saccharomyces cerevisiae) was cultivated in a bench-top fermenter. Five phases of yeast growth could be detected from measurements of the exhaust gases from the fermenter, as indicated by the changes in ion mobility spectra analysed by computational methods. The data were first processed using the Self-Organizing Map (SOM) algorithm, the results showing that the phases of fermentation can be detected and identified. The cultivations were also shown by Sammon’s mapping to be comparable to a certain level of accuracy. Contaminated cultivation could be detected by its distinctive ion mobility spectrometry profile.

A modular simulation package for fed-batch fermentation: penicillin production

Simulation software based on a detailed unstructured model for penicillin production in a fed-batch fermentor has been developed. The model extends the mechanistic model of Bajpai and Reuss by adding input variables such as pH, temperature, aeration rate, agitation power, and feed flow rate of substrate and introducing the CO 2 evolution term. The simulation package was then used for monitoring and fault diagnosis of a typical penicillin fermentation process. The simulator developed may be used for both research and educational purposes and is available at the web site: http://www.chee.iit.edu/~control/software.html.

Physiologically Motivated Monitoring of Fermentation Processes by Means of an Electronic Nose

An on-line approach of non-invasive monitoring of the physiological changes in fermentation processes is presented. In yeast batch and bacterial fed-batch fermentations it is shown that metabolic state changes can be revealed using an electronic nose. The transient responses of the gas sensors to the changes in the composition of the volatiles emitted from the cell cultures during fermentation are used to retrieve a semi-quantitative representation of the physiological state of the cultures. With the sensor responses of the electronic nose it is shown that physiological variables such as rates of growth, substrate uptake and product formation can be depicted. The non-invasive method thus seems as a pertinent alternative to conventional bioreactor monitoring methods.

Development of an alcohol fermentation control system based on biosensor measurements interpreted by neural networks

The lack of cheap and robust continuous analytical instruments — capable of on line measurement of the substance concentrations present in a bioreactor — has stimulated the development of instruments called biosensors. The use of such sensors brings difficulties, because of their nonlinear response to different concentrations. Additionally, the sampling system frequently utilized, based on flow injection analysis (FIA) technology, provides discrete signals of the measured concentration, related to peaks occurring at different times. The objective of this work is to anticipate and suggest solutions to the difficulties arising from the use of these biosensors for monitoring and control of fermentation processes. For this aim, two studies were accomplished. The first one verified the capability of artificial neural networks to interpret biosensor signals. Experimental glucose and sucrose biosensor data were employed with this purpose. The second one investigated, by simulation, the use of a biosensor — modeled using typical experimental data, interpreted by neural networks — in the control of an alcohol fed-batch fermentation. It was concluded that the nets are adequate tools to correlate biosensor signal to the measured concentrations. Alternatives to the use of conventional feedback controllers in the presence of these sensors are also proposed.

Monitoring of Lactobacillus fermentation process by using ion chromatography with a series piezoelectric quartz crystal detector

A new method monitoring Lactobacillus fermentation process, which combines ion chromatography (IC) with a series piezoelectric quartz crystal (SPQC) technique, is presented in this paper. Monitoring of the fermentation process was realized by examining the rate of production of lactic acid. An automatic membrane dialyser was used for the pretreatment of the sample in on-line monitoring. A mixture of p-hydroxybenzoic acid and N, N-diethylethanolamine was adopted as mobile phase and its flow rate was 0.8 ml/min. The effects of some fermentation conditions were also discussed in detail. Accordingly, the optimal fermentation conditions were obtained. This method is simple and convenient while the results obtained are accurate and reliable.

An advanced micromachined fermentation monitoring device

This paper describes the design and fabrication of a novel sensor for the monitoring of fermentation processes, e.g. the time-resolved amount of living cells in fluids. The device consists of two comb-electrodes patterned on a freestanding membrane. The novelty of this device is the capability to clean it ultrasonically on chip, i.e. by electrostatic excitation of the membrane on which the electrodes are placed.

Quantification of nisin in flow-injection immunoassay systems

A monoclonal-antibody-based, sequential competitive-flow-injection immunoassay system in expanded-bed mode has been developed for the determination of nisin. The system allows the determination of nisin in the presence of suspended particles without any significant interference, illustrating its potential for on-line monitoring of fermentation processes or the analysis of food matrices. The dose response range of the system when operated in expanded-bed mode was 6–90 μM. The detection limit under packed-bed conditions was 3 μM. The results correlated well with the results from conventional ELISA in the analysis of samples of processed cheese. When milk samples, fermentation samples and buffer were spiked with nisin, the mean recoveries were 86% for milk samples, 96% for fermentation samples and 98% for buffer solution.

On-Line Real Time Monitoring of Penicillin Fermentation

On-line real-time monitoring of fermentation processes is a crucial task. Conventional on-line process monitoring techniques and an adaptive hierarchical Principal Component Analysis technique were applied. A hybrid supervisory knowledge-based system with a heuristic rule-base was also developed and integrated with on-line monitoring techniques for providing real-time monitoring and supervision.

On-line monitoring of fermentation processes by a new remote dispersive middle-infrared spectrometer

A middle infrared spectrometer coupled with a new attenuated total reflectance probe by optical fibres was developed to solve the problem of on-line analysis in the bioreactors. The measurement of sugars, ethanol and organic acid concentrations was investigated during alcoholic and lactic fermentations. This apparatus was able to operate between 5.5 and 10.5 μm with a reasonably good repeatability (CV<5%) between 6 and 10 μm. The standard errors of prediction were between 1.4 g l −1 (for galactose) and 4.5 g l −1 (for fructose).

Enhanced production of L-(+)-lactic acid in chemostat by Lactobacillus casei DSM 20011 using ion-exchange resins and cross-flow filtration in a fully automated pilot plant controlled via NIR

Due to the lack of suitable in-process sensors, on-line monitoring of fermentation processes is restricted almost exclusively to the measurement of physical parameters only indirectly related to key process variables, i.e., substrate, product, and biomass concentration. This obstacle can be overcome by near infrared (NIR) spectroscopy, which allows not only real-time process monitoring, but also automated process control, provided that NIR-generated information is fed to a suitable computerized bioreactor control system. Once the relevant calibrations have been obtained, substrate, biomass and product concentration can be evaluated on-line and used by the bioreactor control system to manage the fermentation. In this work, an NIR-based control system allowed the full automation of a small-scale pilot plant for lactic acid production and provided an excellent tool for process optimization. The growth-inhibiting effect of lactic acid present in the culture broth is enhanced when the growth-limiting substrate, glucose, is also present at relatively high concentrations. Both combined factors can result in a severe reduction of the performance of the lactate production process. A dedicated software enabling on-line NIR data acquisition and reduction, and automated process management through feed addition, culture removal and/or product recovery by microfiltration was developed in order to allow the implementation of continuous fermentation processes with recycling of culture medium and cell recycling. Both operation modes were tested at different dilution rates and the respective cultivation parameters observed were compared with those obtained in a conventional continuous fermentation. Steady states were obtained in both modes with high performance on lactate production. The highest lactate volumetric productivity, 138 g L−1 h−1, was obtained in continuous fermentation with cell recycling. © 2000 John Wiley & Sons, Inc. Biotechnol Bioeng 67: 147–156, 2000.

Enhanced production of L-(+)-lactic acid in chemostat byLactobacillus casei DSM 20011 using ion-exchange resins and cross-flow filtration in a fully automated pilot plant controlled via NIR

Due to the lack of suitable in-process sensors, on-line monitoring of fermentation processes is restricted almost exclusively to the measurement of physical parameters only indirectly related to key process variables, i.e., substrate, product, and biomass concentration. This obstacle can be overcome by near infrared (NIR) spectroscopy, which allows not only real-time process monitoring, but also automated process control, provided that NIR-generated information is fed to a suitable computerized bioreactor control system. Once the relevant calibrations have been obtained, substrate, biomass and product concentration can be evaluated on-line and used by the bioreactor control system to manage the fermentation. In this work, an NIR-based control system allowed the full automation of a small-scale pilot plant for lactic acid production and provided an excellent tool for process optimization. The growth-inhibiting effect of lactic acid present in the culture broth is enhanced when the growth-limiting substrate, glucose, is also present at relatively high concentrations. Both combined factors can result in a severe reduction of the performance of the lactate production process. A dedicated software enabling on-line NIR data acquisition and reduction, and automated process management through feed addition, culture removal and/or product recovery by microfiltration was developed in order to allow the implementation of continuous fermentation processes with recycling of culture medium and cell recycling. Both operation modes were tested at different dilution rates and the respective cultivation parameters observed were compared with those obtained in a conventional continuous fermentation. Steady states were obtained in both modes with high performance on lactate production. The highest lactate volumetric productivity, 138 g L(-1) h(-1), was obtained in continuous fermentation with cell recycling.

Non-Invasive Fermentation Analysis Using an Artificial Neural Network Algorithm for Processing near Infrared Spectra

The feasibility of using Artificial Neural Networks (ANN) to improve the performance of a fibre optic, near infrared (NIR) spectroscopic probe for monitoring fermentation processes was investigated. A miniature diode array spectrometer, operating between 1100 and 1450 nm, was used for on-line, in situ fermentation monitoring by placing a bifurcated fibre bundle inside a fermentation vessel. Non-linearities in the spectral response were complicated by the combined effects of spectral variations in the OH vibrational bands due to temperature fluctuations and pH variation. As the fermentation proceeds interferences from other absorbing molecules and cell masses increase at an exponential rate. The feasibility of accurately predicting both glucose and ethanol concentrations simultaneously during a fermentation process were assessed by applying Partial Least Squares (PLS) and Artificial Neural Networks (ANN) to the NIR spectral data. For a 5% glucose fermentation, a PLS model was able to predict “on-line” concentrations with standard errors of prediction ( SEP) of 0.19% for glucose and 0.11% for ethanol. Three separate on-line fermentation experiments were performed in order to determine the possibility of using a model developed from one experiment to predict the concentrations of another experiment. PLS models produced an average SEP of 0.21% when used to predict different fermentation experiments. The ANN algorithm produced an average SEP of 0.13% on the same data.

Monitoring of Solid Fermentation Process by Computer-Analysis of Near Infrared Spectral Data

Near infrared spectroscopy (NIR) was applied to the prediction of the growth rate of mushroom, Ganoderma lucidum, in the solid fermentation process. In order to produce a calibration equation, Spectral data of solid medium were analyzed by multiple linear regression with a computer. Cell mass is conventionally measured by analyzing concentration of glucosamine which is a component of the cell wall. The concentration of glucosamine obtained by NIR was compared with that obtained by the conventional method. The values predicted by NIR were fairly in good agreement with those obtained by the conventional method. This result suggests that the NIR is applicable to prediction of growth rate in the solid culture and gives a useful method for the process management of mushroom production.