Bernd Hitzmann

Hohenheim University, Stuttgart, Baden-Württemberg, Germany

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Publications (129)255.93 Total impact

  • O. Paquet-Durand, V. Zettel, B. Hitzmann
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    ABSTRACT: Optimal experimental design for a water absorption process has been carried out and compared to equidistant measurement points in ordinary and logarithmic time scale.•Depending on the rough estimation error of the parameter the best experimental design method is identified.•The optimal experimental design was carried out different optimization criteria.•Depending on the optimization criterion an exact or approximation function has been determined to simplify the optimal experimental design.
    Chemometrics and Intelligent Laboratory Systems 11/2014; · 2.38 Impact Factor
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    ABSTRACT: Based on the constraints and frame conditions given by the real processes the production in bakeries can be modelled as a no-wait permutation flow-shop, following the definitions in scheduling theory. A modified genetic algorithm, ant colony optimization and a random search procedure were used to analyse and optimize the production planning of a bakery production line that processes 40 products on 26 production stages. This setup leads to 8.2 × 1047 different possible schedules in a permutation flow-shop model and is thus not solvable in reasonable time with exact methods. Two objective functions of economical interest were analysed, the makespan and the total idle time of machines. In combination with the created model, the applied algorithms proved capable to provide optimized results for the scheduling operation within a predefined runtime of 15 min, reducing the makespan by up to 8.6% and the total idle time of machines by up to 23%.
    Expert Systems with Applications 10/2014; 41(13):5882–5891. · 1.97 Impact Factor
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    ABSTRACT: Dough is a complex system where yeast cells produce carbon dioxide during the leavening process. Mechanistic models were fitted to measurements of the relative volume of wheat dough during proofing obtained from a Rheofermentometer. The measurements are carried out using 2% and 4% of fresh yeast and proofing temperatures of 28, 32 and 35 °C. The free parameters were the viscosity, a specific CO2 production rate and the number of bubbles. The following assumptions were made: spherical bubbles in the dough liquid, considered to behave as a Newtonian liquid, the applicability of the Bernoulli and ideal gas equations as well as the diffusion theory. The relative volume during proofing was simulated with an average percentage error less than 0.5% and the dependency between volume expansion and calculated CO2 production rate was obtained with an R2 of 0.88.
    Journal of Food Engineering 06/2014; 131:58-64. · 2.58 Impact Factor
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    ABSTRACT: This paper presents the implementation of functional state approach to modelling of Es-cherichia coli fed-batch cultivation. Due to the complex metabolic pathways of microor-ganisms, the accurate modelling of bioprocesses is rather difficult. The functional state approach of a process is an alternative concept which helps in modelling and control of complex processes. The approach main idea is developing of models based on multiple submodels for each functional states (operating regime). In each functional state the pro-cess is described by a conventional type of model, called the local model, which is valid in this state. For parameter identification of the model the genetic algorithms are used. Ge-netic algorithms are directed random search techniques, applying the mechanics of natu-ral selection and natural genetics, which can find the global optimal solution in complex multidimensional search spaces. Based on the available experimental data and simula-tions of E. coli fed-batch cultivation it is shown how this process can be divided into func-tional states and how the model parameters can be obtained on the basis of genetic algo-rithms. By simulation and comparison between the results and experimental data, can be seen how the concept of functional state approach works and how effective is the proposed identification scheme.
    Biotechnology & Biotechnological Equipment 04/2014; 18(3):207-214. · 0.38 Impact Factor
  • Bianca Grote, Torsten Zense, Bernd Hitzmann
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    ABSTRACT: Sourdough is used for the manufacture of bakery products, especially rye bread and vital for the development of its typical flavor. Although the process of sourdough fermentation is known thousands of years, still it is not understood in detail. Despite, in modern bread fabrication quality requirements are very high and demand a consequent control not only for the final baking process, but also for the production of intermediates. Characteristic process variables like pH-value and the degree of acidity are typically measured off-line to receive information about the state of the fermentation process. A new approach for monitoring the actual process state is the employment of 2D-fluorescence spectroscopy. As a non-invasive, optical method it is widely used for monitoring various types of bioprocesses, e.g. yeast or bacterial cultivations. In this contribution the application of partial least squares (PLS) regression and principal component regression (PCR) models for prediction of process variables of rye sourdough fermentations are compared to an evaluation where principal component analysis (PCA) is combined with artificial neural networks (ANN) for prediction of pH-value and acidity. For the pH-value PLS regression proved as good as PCR models and the combination of PCA and ANN. The average percentage root mean square error of prediction (pRMSEP) was between 2.5 and 5.1%. For the prediction of the acidity level, the best results were obtained using PLS regression models (pRMSEP: 6.0–8.1%). Smoothing the noisy 2D-fluorescence spectra slightly decreased the errors about 0.6%. Predictions with data sets of varying dough yield and constant temperature led to about 2% better results than data sets with different temperatures and constant dough yield. These results indicate a higher sensitivity of the prediction quality with respect to varying temperature compared to varying dough yield.
    Food Control 04/2014; · 2.82 Impact Factor
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    ABSTRACT: The key objective for this process optimization is to obtain higher productivity and profit in the chemical or bio-chemical process. To achieve this, we must apply control techniques that closely correlate with our ability to characterize this process. Within this context, optical sensors associated with chemometrical modeling are considered a natural choice due to their low response time as well as their non-intrusive and high sensibility characteristics. Usually, chemometrical modeling is based on PCR (Principal Component Regression) and PLS (Partial Least Squares). However, since optical techniques are highly sensible and bio-chemical mediums are highly complex, these methodologies can be replaced by using chemometrical modeling based on Pure Spectra Components (PSCM). Our study applies PCR, PLS and PSCM for protein prediction in flour samples measured with Near Infrared Reflectance (NIR), comparing the three methodologies for on-line sensor project. We also outline the development of a spectral filter based on PSCM associated with Ant Colony Optimization. The results lead to our conclusion that the use of optical techniques works best when PSCM analysis is applied, as it allows the development of a spectral sensor for protein quantification in flour samples with less than twenty NIR wavelengths evaluated, selected from a total of 1150. The filtering tool showed favorable results in condensing relevant information from NIR spectral data, increasing R2 from sample prediction by almost 60% for PCR models and 40% for PLS models, using 10% and 20% of full spectral data, confirming the viability of filtering methods.
    Chemometrics and Intelligent Laboratory Systems 03/2014; · 2.38 Impact Factor
  • V. Zettel, A. Krämer, F. Hecker, B. Hitzmann
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    ABSTRACT: The effects of chia incorporated as gel in wheat bread dough as hydrocolloid were characterized. To avoid competition of starch and ground chia, chia was incorporated as gel. The gel was prepared of ground chia with 5 and 10 g/g water, respectively. The doughs were prepared with 1–3 % chia related to the amount of wheat flour. To characterize the dough, measurements with a Farinograph, a Rheofermentometer and a Kieffer dough rig were performed. The pasting curves of all variations were recorded. The fundamental rheological characteristics were determined with tests using a rotational rheometer. Baking experiments were performed to evaluate the effect of chia gel addition on the bread quality. The staling and crumb firmness were analysed by differential scanning calorimetry and texture analysis. Dough analyses show that the doughs with added chia gel have a softer consistency. Dough stability during fermentation and volume yield of the bread loafs increased with added chia gel to a certain extent. These changes are visible with already 1 % chia. The bread quality was improved with respect to storage as the crumb firmness was reduced compared to the breads without added chia gel.
    European Food Research and Technology 01/2014; · 1.39 Impact Factor
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    ABSTRACT: The duration of cell death may allow deducing the underlying degenerative mechanism. To find out how long a photoreceptor takes to die, we used the rd1 mouse model for retinal neurodegeneration, which is characterized by phosphodiesterase-6 (PDE6) dysfunction and photoreceptor death triggered by high cGMP levels. Based on cellular data on the progression of cGMP accumulation, cell death, and survival, we created a mathematical model to simulate the temporal development of the degeneration and the clearance of dead cells. Both cellular data and modelling suggested that at the level of the individual cell, the degenerative process was rather slow, taking around 80 h to complete. Organotypic retinal explant cultures derived from wild-type animals and exposed to the selective PDE6 inhibitor zaprinast, confirmed the surprisingly long duration of an individual photoreceptor cell's death. We briefly discuss the possibility to link different cell death stages and their temporal progression to specific enzymatic activities known to be causally connected to cell death. This in turn opens up new perspectives for the treatment of inherited retinal degeneration, both in terms of therapeutic targets and temporal windows-of-opportunity.
    Advances in Experimental Medicine and Biology 01/2014; 801:575-81. · 2.01 Impact Factor
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    ABSTRACT: Many applications of sonic velocity measurements for material testing are known and widely employed. Yet the technique is rarely employed in biotechnology especially for process monitoring. The ultrasonic resonator technology allows a highly precise measurement of the sonic velocity in small volumes, which makes this technology interesting for process analytics. New techniques give information about various process parameters on-line and in real time. Nowadays special interest is the on-line product analytic. Not only is the product concentration is in focus, but the purity and activity, which are currently rarely accessible on-line. The ultrasonic resonator is an opportunity to close this gap. An evaluation method will be introduced to ensure a maximum in precision and accuracy during the measurement. The best obtained precision of less than 0.3 mm s−1 sonic velocity and an accuracy of 0.027 m s−1, for water at different temperatures, give the possibility to detect slightest changes and recover worthwhile information during the measurements.
    Sensors and Actuators A Physical 08/2013; 198:69–74. · 1.94 Impact Factor
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    ABSTRACT: A mathematical model is presented for the kinetic resolution of racemates. It takes all intermediate binding steps into account and assumes that such steps are reversible. The model describing dynamics of the chiral reaction products consists of two nonlinear differential equations. With this model, the enantioselectivity of enzyme has been studied. Mathematical and numerical simulation of the model show that there are several ways to control the enantiomeric ratio (E) but the affinity and the binding rates of the intermediate enzyme complex to the racemic substrates are the key steps for the enzyme enantioselectivity.
    Journal of Mathematical Chemistry 06/2013; 51(6). · 1.27 Impact Factor
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    ABSTRACT: For most neurodegenerative diseases the precise duration of an individual cell's death is unknown, which is an obstacle when counteractive measures are being considered. To address this, we used the rd1 mouse model for retinal neurodegeneration, characterized by phosphodiesterase-6 (PDE6) dysfunction and photoreceptor death triggered by high cyclic guanosine-mono-phosphate (cGMP) levels. Using cellular data on cGMP accumulation, cell death, and survival, we created mathematical models to simulate the temporal development of the degeneration. We validated model predictions using organotypic retinal explant cultures derived from wild-type animals and exposed to the selective PDE6 inhibitor zaprinast. Together, photoreceptor data and modeling for the first time delineated three major cell death phases in a complex neuronal tissue: (1) initiation, taking up to 36 h, (2) execution, lasting another 40 h, and finally (3) clearance, lasting about 7 h. Surprisingly, photoreceptor neurodegeneration was noticeably slower than necrosis or apoptosis, suggesting a different mechanism of death for these neurons.
    Cell Death & Disease 01/2013; · 5.18 Impact Factor
  • Marc Stanke, Bernd Hitzmann
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    ABSTRACT:  : In this chapter, different approaches for open-loop and closed-loop control applied in bioprocess automation are discussed. Although in recent years many contributions dealing with closed-loop control have been published, only a minority were actually applied in real bioprocesses, the majority being simulations. As a result of the diversity of bioprocess requirements, a single control algorithm cannot be applied in all cases; rather, different approaches are necessary. Most publications combine different closed-loop control techniques to construct hybrid systems. These systems are supposed to combine the advantages of each approach into a well-performing control strategy. The majority of applications are soft sensors in combination with a proportional-integral-derivative (PID) controller. The fact that soft sensors have become this importance for control purposes demonstrates the lack of direct measurements or their large additional expense for robust and reliable online measurement systems. The importance of model predictive control is increasing; however, reliable and robust process models are required, as well as very powerful computers to address the computational needs. The lack of theoretical bioprocess models is compensated by hybrid systems combining theoretical models, fuzzy logic, and/or artificial neural network methodology. Although many authors suggest a possible transfer of their presented control application to other bioprocesses, the algorithms are mostly specialized to certain organisms or certain cultivation conditions as well as to a specific measurement system.
    Advances in biochemical engineering/biotechnology 12/2012; · 1.64 Impact Factor
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    ABSTRACT: Alcoholic fermentation fluids are containing various sugars, ethanol and carbon dioxide in aqueous solution. Precondition for the concentration determination with ultrasound based methods is a calibration model describing the dependency of typical ultrasound parameters like the sound velocity from the concentration of the dissolved components and the temperature. For this reason, the sound velocity c in aqueous solutions with CO2, CO2 + saccharose, CO2 + ethanol and CO2 + saccharose + ethanol in dependence of the temperature in a range of 2 °C up to 30 °C and constant CO2-pressures of 2.01 ∙ 105 Pa and 3.01 ∙ 105 Pa was measured and compared with the data in equivalent solutions without a carbon dioxide fraction. Carbon dioxide induces, like the components saccharose and ethanol, an increase Δc of the sound velocity. The density ρ of the investigated fluids was calculated by the interpolation of literature data combined with several approximation approaches. The adiabatic compressibility κ, which can be determined from the relationship κ = 1/(c2 ∙ ρ), is decreased by all investigated solutes, showing a linear dependency from their mole fractions χ. Different values ∂κ/∂χ can be explained by the molecular structure of the investigated molecules. It could be shown, that the overall decrease ΔκSa + Et + CO2 of the compressibility induced by saccharose, ethanol and CO2 can be depicted as a sum ΔκSa + ΔκEt + ΔκCO2 of contributions generated in solutions containing only one of the three components. Regression functions ΔcSa + Et + CO2(χSa, χEt, χCO2, T), ΔκSa + Et + CO2(χSa, χEt, χCO2, T) have been calculated for the change of the sound velocity and compressibility respectively.
    Journal of Molecular Liquids 11/2012; 175:111–120. · 2.08 Impact Factor
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    A. Hitzemann, B. Grote, B. Hitzmann
    Chemie Ingenieur Technik 08/2012; 84(8). · 0.70 Impact Factor
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    M. Stanke, V. Heine, B. Hitzmann
    Chemie Ingenieur Technik 08/2012; 84(8). · 0.66 Impact Factor
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    ABSTRACT: Industrial baking is a temperature and time controlled process, which considers neither the actual quality of the raw materials nor the process parameters like humidity, pastry temperature and actual pastry status. Furthermore the baking process is irreversible. Therefore, without a process monitoring considering the actual process state, suboptimal results may be achieved. To obtain optimal results, an automated monitoring system is required, but not yet available. Such a system must be able to identify the baking goods and the current state of the baking process represented by color and size of the baking goods.To develop such a system, digital image processing was used. An optical system was implemented, which was able to make digital images of the baking goods from inside the oven in a continuous form. The goal was the development of algorithms for distinction of baking goods and characterization of color saturation and shape, altogether resulting in an optical online process monitoring system. By using a modified Viola–Jones algorithm the kind of baking good in the oven is identified with an error of 5.6%. The error of automated determination of the width and height change of bread rolls with respect to manual evaluation is less than 4%. Based on a neural network, the baking good is identified pixel by pixel. The training error of the neuronal net was 7.0%. This allows the calculation of the evolution of lightness and color saturation. Using this information, the state of the baking process is identified reliably. Therefore, the basics for the automatic control is provided.
    Journal of Food Engineering 07/2012; 111(2):425–431. · 2.58 Impact Factor
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    ABSTRACT: The goal of this study was to show that the metabolism of Klebsiella pneumoniae under different aeration strategies could be monitored and predicted by the application of chemometric models and fluorescence spectroscopy. Multi-wavelength fluorescence was applied to the on-line monitoring of process parameters for K. pneumoniae cultivations. Differences observed in spectra collected under aerobiosis and anaerobiosis can be explained by the different metabolic states of the cells. To predict process variables such as biomass, glycerol, and 1,3-propanediol (1,3-PD), chemometric models were developed on the basis of the acquired fluorescence spectra, which were measured continuously. Although glycerol and 1,3-PD are not fluorescent compounds, the results showed that this technique could be successfully applied to the on-line monitoring of variables in order to understand the process and thus improve 1,3-PD production. The root mean square errors of predictions were 0.78 units, 10 g/L, and 2.6 g/L for optical density, glycerol, and 1,3-PD, respectively.
    Journal of Industrial Microbiology 01/2012; 39(5):701-8. · 1.80 Impact Factor
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    ABSTRACT: Multi-wavelength fluorescence spectroscopy was evaluated as a tool for on-line monitoring of recombinant Escherichia coli cultivations expressing human basic fibroblast growth factor (hFGF-2). The data sets for the various combinations of the excitation and emission spectra from batch cultivations were analyzed using principal component analysis. Chemometric models (the partial least squares method) were developed for correlating the fluorescence data and the experimentally measured variables such as the biomass and glucose concentrations as well as the carbon dioxide production rate. Excellent correlations were obtained for these variables for the calibration cultivations. The predictability of these models was further tested in batch and fed-batch cultivations. The batch cultivations were well predicted by the PLS models for biomass, glucose concentrations and carbon dioxide production rate (RMSEPs were respectively 5%, 7%, 9%). However, when tested for biomass concentrations in fed-batch cultivations (with final biomass three times higher than the highest calibration data) the models had good predictability at high growth rates (RMSEPs were 3% and 4%, respectively for uninduced and induced fed-batch cultivations), which was as good as for the batch cultivations used for developing the models (RMSEPs were 3% and 5%, respectively for uninduced and induced batch cultivations). The fed-batch cultivations performed at low growth rates exhibited much higher fluorescence for fluorophores such as flavin and NAD(P)H as compared to fed-batch cultivations at high growth rate. Therefore, the PLS models tended to over-predict the biomass concentrations at low growth rates. Obviously the cells changed their concentration of biogenic fluorophores depending on the growth rate. Although multi-wavelength fluorescence spectroscopy is a valuable tool for on-line monitoring of bioprocess, care must be taken to re-calibrate the PLS models at different growth rates to improve the accuracy of predictions.
    Biochemical Engineering Journal 12/2011; 58:133-139. · 2.37 Impact Factor

Publication Stats

665 Citations
255.93 Total Impact Points


  • 2007–2014
    • Hohenheim University
      • • Process Analysis and Cereal Technology Unit
      • • Institute of Food Science and Biotechnology
      Stuttgart, Baden-Württemberg, Germany
  • 2013
    • University of Tuebingen
      • Institute for Ophthalmic Research
      Tübingen, Baden-Wuerttemberg, Germany
  • 2011
    • Helmholtz Centre for Infection Research
      Brunswyck, Lower Saxony, Germany
  • 1993–2011
    • Leibniz Universität Hannover
      • Institute of Technical Chemistry
      Hannover, Lower Saxony, Germany
  • 2005
    • Bielefeld University
      • Faculty of Technology
      Bielefeld, North Rhine-Westphalia, Germany
  • 1994
    • University of Münster
      • Institute of Biochemistry
      Münster, North Rhine-Westphalia, Germany