Bernd Hitzmann

Hohenheim University, Stuttgart, Baden-Württemberg, Germany

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Publications (124)187.34 Total impact

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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. · 2.28 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; · 2.74 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. · 1.83 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 01/2014; · 2.29 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 01/2014; 41(13):5882–5891. · 1.85 Impact Factor
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    ABSTRACT: The mechanical spectra from rheological measurements of fermented dairy products are described by a viscoelastic response function in the frequency domain, namely, the Cole-Cole model. The suitability of this model for application in dairy products was validated by using casein micelle suspensions, mixed casein micelle and glass microspheres suspensions and microgel suspensions (fresh cheese) as examples. In addition, the microgel particles were substituted with similar-sized glass microspheres at different ratios. The accuracy and stability of the fitting algorithm, used for the complex valued and finite-bandwidth data, was discussed. Different superimposed sub-models with independent parameter sets were attributed to each microstructural element in fermented dairy products, namely casein micelles and microgel particles. The Cole-Cole model was established for dairy products and possible correlations with an altered microstructure were demonstrated.
    International Dairy Journal. 01/2014;
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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; · 6.04 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 01/2013; 51(6). · 1.23 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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    A. Hitzemann, B. Grote, B. Hitzmann
    Chemie Ingenieur Technik 08/2012; 84(8). · 0.70 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.
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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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    M. Stanke, V. Heine, B. Hitzmann
    Chemie Ingenieur Technik 01/2012; 84(8). · 0.70 Impact Factor
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    ABSTRACT: Basic fibroblast growth factor (FGF-2) is a multifunctional cytokine that regulates various cellular processes both in vitro and in vivo. FGF-2 is extensively used in embryonic stem cell cultures since it can maintain the cells in an undifferentiated state. However, the high price of FGF-2 has limited its application in stem cell research. Here we present a fast and efficient process for the purification of FGF-2 from recombinant Escherichia coli cultures using reusable membrane adsorbers. A high expression level of FGF-2 (42 mg/g dry cell) was achieved by fed-batch cultivation of E. coli BL21(DE3). A new combination of cation exchange membrane chromatography and heparin-sepharose affinity chromatography was used for the purification of the protein. A novel anion exchange membrane chromatography was used in the polishing step to remove endotoxins and DNA. In this new process, about 200 mg soluble FGF-2 was yielded from 1.9 L culture broth with a purity of 98%. The purified protein was identified to be endotoxin-free and bioactive. It was successfully tested to keep primate embryonic stem cell and human-induced pluripotent stem cell pluripotent. Our approach, in which a controlled cultivation process is combined with an optimized fast and versatile downstreaming process, is suitable for low-cost preparation of bioactive FGF-2 at bench-scale and may be beneficial to the effective production of other cytokines.
    Engineering in Life Sciences 11/2011; 12(1):29 - 38. · 1.63 Impact Factor
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    ABSTRACT: Transcriptome analysis technologies are important systems-biology methods for the investigation and optimization of mammalian cell cultures concerning with regard to growth rates and productivity. For the production of recombinant proteins, knowledge of the expression conditions of the influencing genes is a major issue in the improvement of cell lines by means of genome engineering. This chapter presents two main techniques for transcriptome analysis: microarray technology and next-generation sequencing. Protein-based methods are also briefly outlined. Furthermore, the impact of these technologies on mammalian cell culture improvement is discussed.
    Advances in biochemical engineering/biotechnology 09/2011; 127:1-25. · 1.64 Impact Factor
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    ABSTRACT: The polysialic acid (PSA) production in Escherichia coli (E. coli) K1 was studied using three different cultivation strategies. A batch cultivation, a fed-batch cultivation at a constant specific growth rate of 0.25 h(-1) and a fed-batch cultivation at a constant glucose concentration of 50 mg l(-1) was performed. PSA formation kinetics under different cultivation strategies were analyzed based on the Monod growth model and the Luedeking-Piret equation. The results revealed that PSA formation in E. coli K1 was completely growth associated, the highest specific PSA formation rate (0.0489 g g(-1)h(-1)) was obtained in the batch cultivation. However, comparing biomass and PSA yields on the glucose consumed, both fed-batch cultivations provided higher yields than that of the batch cultivation and acetate formation was prevented. Moreover, PSA yield on glucose was also correlated to the specific growth rate of the cells. The optimal specific growth rate for PSA production was 0.32 h(-1) obtained in the fed-batch cultivation at a constant glucose concentration of 50 mg l(-1), with highest conversion efficiency of 43 mg g(-1).
    Journal of Biotechnology 04/2011; 154(4):222-9. · 3.18 Impact Factor
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    ABSTRACT: The purpose of the work presented here is the production of liquid biofuels from wet organic waste matter in a continuous one-step catalytic process under hydrothermal conditions. The catalytic reaction of wet organic matter at near-critical water conditions (T > 300 °C, p > 22.1 MPa) is used to produce a mixture of combustible organics which can be used as liquid biofuel. In order to achieve a good product quality in a continuous one-step process, two catalysts were applied, a homogeneous potassium carbonate catalyst and a heterogeneous ZrO2 catalyst. In addition, the reaction mixture was recirculated. The continuous flow of concentrated waste biomass feed at low flow rates and recirculation of the hot reaction mixture were the most challenging obstacles to overcome. The scale of the plant (0.1 l reactor volume) allowed for a variation of the feed, reaction temperature, and recirculation rate in order to optimise the process conditions. Still, the product quantity obtained was sufficient to perform a analytical characterisation. The experimental results confirmed the feasibility of the process. Hydrothermal treatment of waste biomass, after dewatering, resulted in a biocrude oil of high calorific value.
    Fuel. 02/2011;
  • 01/2011: pages 67 - 81; , ISBN: 9780470909997

Publication Stats

501 Citations
187.34 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