University of North Florida
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Stuart Chalk, Ph.D.
Department of Chemistry
University of North Florida
Phone: 1-904-620-1938
Fax: 1-904-620-3535
Email: schalk@unf.edu
Website: @unf

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Bernd Hitzmann

Abbrev:
Hitzmann, B.
Other Names:
Address:
Institut fur Technische Chemie, University Hannover, Callinstr. 3, 30167 Hannover, Germany
Phone:
NA
Fax:
+49-511-762-3004

Citations 16

"Sensors As Components Of Integrated Analytical Systems"
Trends Biotechnol. 1994 Volume 12, Issue 2 Pages 42-46
Thomas Scheper,Frank Plötz, Cord Müller and Bernd Hitzmann

Abstract: This paper focuses on the construction of a simple optical sensor system for the determination of penicillin V in a culture of Penicillium chrysogenum. This example illustrates problems that are representative of those encountered in the installation and optimization of sensors in complex analytical systems used for continuous monitoring of relevant bioprocess variables and, thus, should illustrate the requirements for the development of a successful, integrated sensor system.
Penicillin Pharmaceutical Sensor Review

"Neural Networks As A Modeling Tool For The Evaluation And Analysis Of FIA Signals"
J. Biotechnol. 1998 Volume 65, Issue 1 Pages 15-22
Bernd Hitzmann*, Adnan Ritzka, Roland Ulber, Karsten Schöngarth and Olaf Broxtermann

Abstract: Three different application examples are discussed, where neural networks are used as a modeling tool for the evaluation of FIA measurements. In the first example, the ability of a neural network is presented to relate the characteristic shape of a FIA measurement with the corresponding analyte, i.e. penicillin, concentration. Using classical evaluation techniques, based on peak height, area or width, these signals cannot be evaluated reliably. The second application demonstrates a multiple injection FIA system for glucose detection, whose measurement signals are a superposition caused by a fast triplicate injection. The neural network is used for the deconvolution process, which makes possible a simultaneous measurement and calibration. In the last application, neural networks are presented as a tool to detect distorted FIA measurement signals.
Penicillin Glucose Theory Neural network Deconvolution Detector

"A Knowledge-based System For Real-time Validation Of Calibrations And Measurements"
Chemom. Intell. Lab. Syst. 1999 Volume 46, Issue 1 Pages 57-66
Axel Löhn and Bernd Hitzmann

Abstract: In this contribution, a knowledge-based system is presented which is able to identify faults during measurements and calibrations of a flow injection analysis (FIA) system. The knowledge-based system is a subtask of the automation system CAFCA, which can be used for the automation of flow systems, and runs on MS-DOS-PCs, Tne knowledge-based system consists of a numerical module as well as a knowledge-based module, where the knowledge about the process analyzer. is implemented using rules, Using these rules faults will be identified fast and reliable. If a fault has been detected, the operator is informed automatically. Furthermore, minor faults can even be corrected by the knowledge-based system. The application of the system shows that it is an efficient support for the operator,
Calibration

"FIA System For Fast Glucose Measurement In Bioprocesses"
Chem. Ing. Tech. 1998 Volume 70, Issue 3 Pages 297-299
Karsten Schöngarth, Priv.-Doz. Dr. Bernd Hitzmann, Karl Friehs

Abstract: A flow injection analysis system is presented for the determination of glucose concentration. online in bioprocesses without a sampler. It is based on the injection of a glucose oxidase solution into the fluid and the determination of the O consumption in the glucose turnover. In a culture of Escherichia coli the measuring error was ≤5% compared to the off-line values. The measuring time was 47 s.
Glucose Fermentation broth Bacteria Process monitoring

"Methods Of Evaluation Of Measuring Signals From Flow Injection Analysis"
Chem. Ing. Tech. 1996 Volume 68, Issue 5 Pages 570-573
Adnan Ritzka, Roland Ulber, Bernd Hitzmann

Abstract: Accurate, affordable and above all rapid determination of various compounds plays a central role in chemical engineering. Accordingly, the number has increased to sensors drastically in recent times. Especially the combination of miniaturizable electronic components with high specific biological or biochemical systems leads to sensors that can be specifically applied to a measurement problem. The use of biosensors based on semiconductors such as. For example, pH-sensitive field effect transistor (pH-FET), in a flow injection analysis (FIA) uses besides the advantages of a fully automated operation. With appropriate sampling systems, it is also possible to use the sensor systems for on-line analysis.
Glucose Sensor Field effect transistor Detector

"Neural Networks For Evaluation Of Measuring Signals From Flow Injection Analysis"
Chem. Ing. Tech. 1993 Volume 65, Issue 8 Pages 947-949
Antje Waßmann, Bernd Hitzmann

Abstract: The applications of a neural network in flow injection analysis (FIA) is described. The FIA signal consisted of 180 single data points. The evaluation of the signal with disturbances showed only an increase in standard deviation.
Neural network Signal processing

"A Feedforward-feedback Substrate Controller Based On A Kalman Filter For A Fed-batch Cultivation Of Escherichia Coli Producing Phytase"
Comp. Chem. Engineer. 2005 Volume 29, Issue 5 Pages 1113-1120
M. Arndt, S. Kleist, G. Miksch, K. Friehs, E. Flaschel, J. Trierweiler and B. Hitzmann

Abstract: For the feeding-phase of a batch/fed-batch cultivation of a recombinant Escherichia coli producing extracellular phytase, a controller has been developed. Based on the estimated process variables by a Kalman filter, a feedforward-feedback controller has been implemented in order to maximize the phytase production and to minimize acetate production. The Kalman filter was used to reduce the noise of the glucose measurements and to estimate the biomass concentration, the substrate (glucose) concentration, the maximal growth rate as well as the reaction broth volume, which are used to calculate the feedforward controller contribution. A PI controller was applied to adjust the glucose concentration to the desired set point of 0.2 g/L. The secretion of phytase into the medium is increased at low glucose concentration whereas the acetate production is reduced, due to a low concentration avoiding significant overflow metabolism. The glucose concentration, as the sole measured variable used by the controller, is determined using flow injection analysis (FIA). The operation of the controller as well as its application to the E. coli cultivation is presented. The average on-line measured glucose concentration is 0.208 g/L with a standard deviation of 0.066 g/L. During the cultivation a fault occurred in the measurement system. The response of the controller system with respect to this fault is discussed in detail. Compared to a controller based on oxygen measurements, the yield of phytase is the same with the presented system. © 2004 Elsevier Ltd. All rights reserved.

"Kalman Filter Based Glucose Control At Small Set Points During Fed-Batch Cultivation Of Saccharomyces Cerevisiae"
Biotechnol. Prog. 2004 Volume 20, Issue 1 Pages 377-383
Michael Arndt and Bernd Hitzmann

Abstract: A glucose control system is presented, which is able to control cultivations of Saccharomyces cerevisiae even at low glucose concentrations. Glucose concentrations are determined using a special flow injection analysis (FIA) system, which does not require a sampling module. An extended Kalman filter is employed for smoothing the glucose measurements as well as for the prediction of glucose and biomass concentration, the maximum specific growth rate, and the volume of the culture broth. The predicted values are utilized for feedforward/feedback control of the glucose concentration at set points of 0.08 and 0.05 g/L. The controller established well-defined conditions over several hours up to biomass concentrations of 13.5 and 20.7 g/L, respectively. The specific glucose uptake rates at both set points were 1.04 and 0.68 g/g/h, respectively. It is demonstrated that during fed-batch cultivation an overall pure oxidative metabolism of glucose is maintained at the lower set point and a specific ethanol production rate of 0.18 g/g/h at the higher set point.

"The Control Of Glucose Concentration During Yeast Fed-batch Cultivation Using A Fast Measurement Complemented By An Extended Kalman Filter"
Bioprocess Eng. 2000 Volume 23, Issue 4 Pages 337-341
B. Hitzmann, O. Broxtermann, Y.-L. Cha, O. Sobieh, E. Stärk, T. Scheper

Abstract: In this contribution results are presented from the control of glucose during a yeast fed-batch cultivation. For glucose measurements a special flow injection analysis (FIA) system was employed, which uses a glucose oxidase solution instead of immobilized enzymes. To avoid the large delay time caused by probing systems samples containing cells, i.e., samples containing the ordinary culture broth, are injected into the FIA system. Based on a special evaluation method the glucose concentration can be measured with a delay time of about 60 s. Employing an extended Kalman filter, the biomass, the glucose concentration as well as the µ(max) (Monod model) are estimated. Based on the estimation a feed forward and a PI-control with a set point of 0.5 g/l was carried out. The mean deviation of the set point and the estimated value as well as the set point and the measured value were 0.05 and 0.11 g/l respectively for a control period of 8 h producing a cell dry mass of more than 6 g/l.

"Simultaneous Calibration In Flow Injection Analysis Using Multiple-injection Signals Evaluated By Partial Least Squares"
Anal. Chim. Acta 1998 Volume 363, Issue 2-3 Pages 183-189
Karsten Schöngarth and Bernd Hitzmann*

Abstract: In this contribution, a new calibration technique for flow injection analysis is presented. The technique is based on a multiple-injection system complemented with multivariate evaluation. A standard the sample and another standard solution are injected in a fast sequence. A partial mixing of the solutions occurs due to dispersion. However, the overlapping measurement signals can be deconvoluted reliably employing partial least-squares regression. The simultaneous calibration technique enables a fast adaptation to changes in the reaction system, while the time lost by rapid threefold injection and the signal evaluation is minimal. Applying simultaneous calibration the change of the sensor sensitivity is considered inherently. To test the simultaneous calibration technique, it was applied to measurements of a flow injection system for the determination of glucose while the temperature of the reaction coil was changed.
Glucose Partial least squares Calibration Multiinjection Multivariate calibration Heated reaction

"A New Evaluation Technique For FIA Measurements Projective Reference Evaluation"
Anal. Chim. Acta 1997 Volume 348, Issue 1-3 Pages 161-166
B. Hitzmann*, A. Löhn, M. Arndt, R. Ulber and C. Müller

Abstract: In this contribution we present a new evaluation technique used for flow injection analysis (FIA) measurements called projective reference evaluation. Using different FIA systems employing an enzyme cartridge in combination with an oxygen electrode for glucose measurement, an enzyme field effect transistor for urea measurement as well as an enzyme optode for penicillin measurement it will be demonstrated, that the new evaluation technique is able to calculate reliable analyte concentrations even from heavily faulty signals. The occurrence of a fault can be detected very easily, using the new technique. Furthermore, it will be shown, that the linear measurement range of a FIA system can be extended significantly. Applying this evaluation technique the whole information of the analysis system inherently hidden in the measurement signal is exploited. The simplicity of the procedure implied its use for online application in a process FIA where a reliable evaluation as well as a fast fault detection is imperative. 19 References
Penicillin Urea Glucose Electrode Field effect transistor Column Immobilized enzyme Optosensing

"Computational Neural Networks For The Evaluation Of Biosensor FIA Measurements"
Anal. Chim. Acta 1997 Volume 348, Issue 1-3 Pages 135-141
B. Hitzmann*, A. Ritzka, R. Ulber, T. Scheper and K. Schügerl

Abstract: A computational neural network based evaluation method is presented, which enables a reliable quantification of enzyme field effect transistor (EnFET) flow injection analysis (FIA) signals from samples with changing pH values. Two FIA systems, one for glucose and the other for urea determination, are employed to test the evaluation method. Measurement signals were obtained from samples with different glucose concentrations (3, 4, 5, 6 and 7 g/l) and urea concentrations (1, 1.25, 1.5, 1.75 and 2.0 g/l) at various pH values (5.5, 5.75, 6.0, 6.25 and 6.5). These signals cannot be evaluated based on the peak height, width or integral. Using a large set of measuring signals for training the artificial neural network (12 samples, each measured fivefold (=60) signals) the error of analyte prediction from test signals are 3.2% and 2.5% for glucose and urea respectively. With a reduced training set of five measurement signals the error of prediction of the test set increases to 4.5% and 5.5% for glucose and urea respectively. In this investigation it will be demonstrated that computational neural networks are able to evaluate FIA signals, which cannot be evaluated reliably by FIA standard methods. 10 References
Urea Field effect transistor Sensor Chemometrics Neural network Peak width

"The Automation Of Immun-FIA-systems"
Anal. Chim. Acta 1995 Volume 313, Issue 1-2 Pages 55-62
B. Hitzmanna,*, A. Löhna, M. Reineckeb, B. Schulzeb and T. Scheperb

Abstract: A software package for automation was applied to two different flow injection immunoassays (FIIA). The software package was called CAFCA, computer assisted flow control and analysis. The program was implemented in Turbo Pascal 7.0 and run on a MS-DOS personal computer of a 80386 type or higher. The main features covered by CFACA were the configuration, the programming of the course of events, the calibration, the online procedures (control, measurement and evaluation) and the off-line evaluation. CAFCA was applied to an homogeneous FIIA system with tubidimetric detection and an heterogeneous FIIA system with fluorimetric detection for the determination of proteins in animal cell cultivation processes. The results were based on the average of three measurements. A method was proposed which allowed the evaluation of severely distorted signals in heterogeneous FIIA.
Proteins Immunoassay Automation Computer

"Evaluation Of PH Field Effect Transistor Measurement Signals By Neural Networks"
Anal. Chim. Acta 1994 Volume 294, Issue 3 Pages 243-249
Bernd Hitzmann* and Thomas Kullick

Abstract: A feed-forward neural network was developed which allowed both the analyte concentration and the buffer concentration to be calculated from a FIA signal of a pH-FET detector. The method was applied to the determination of penicillin G (benzylpenicillin) using a detector prepared by immobilizing penicillin amidase on to the gate area of the pH-FET. The detector was mounted in a FIA system which employed phosphate buffer solution, pH 7, as the mobile phase. The detector responded to both the presence of penicillin and buffer ion producing changes in peak height and contour. The neural network was used to evaluate the different contours of the signal. The net was trained with all combinations of 1.5, 5 and 10 g/l benzylpenicillin with 5, 20, 35 and 50 mM buffer ion and with all combinations of 2.5 and 7.5 g/l benzylpenicillin with 15, 25 and 45 mM buffer ion. The total average errors were 4.7 and 4.9% for benzylpenicillin and phosphate buffer ion, respectively.
Benzylpenicillin Pharmaceutical Field effect transistor Ion exchange Neural network

"Knowledge-based Fault Detection And Diagnosis In Flow Injection Analysis"
Anal. Chim. Acta 1994 Volume 291, Issue 1-2 Pages 29-40
Jens Brandt and Bernd Hitzmann*

Abstract: A real-time knowledge-based system is described for the fast detection and diagnosis of faults in flow injection systems. The system was developed on a VAXstation 3100 (Digital Equipment, Maynard, MA, USA) and combined numerical data analysis with symbolic knowledge processing. Empirical and systematic methods of knowledge acquisition were combined to reach an extensive consideration of all possible disturbances. Small knowledge fragments in the form of simple rules provide effective results in order to enhance speed, reliability and selectivity of the fault diagnosis. Results are discussed.

"Computer-aided Detection Of Failures In Flow Injection Analysis Systems"
Am. Biotechnol. Lab. 1993 Volume 11, Issue 5 Pages 78-80
Brandt, J.;Hitzmann, B.

Abstract: The capabilities of an expert system to detect and diagnose a failure in a flow-injection anal. system are illustrated by the enzymatic determination of glucose measured during cultivation of Penicillium chrysogenum.
Glucose Fermentation broth Computer Process monitoring