Bernd Hitzmann

Hohenheim University, Stuttgart, Baden-Württemberg, Germany

Are you Bernd Hitzmann?

Claim your profile

Publications (149)302.76 Total impact

  • [Show abstract] [Hide abstract]
    ABSTRACT: Generic Emmental cheese from Germany, produced in five different dairies, was evaluated in terms of thermo-physical properties (melting, flowing, oiling-off and browning) with the aim of characterising German Emmental cheese and identifying similarities and/or differences between Emmental samples from different producers. The data set was subjected to principal component analysis and three principal components were calculated accounting for 68.9% of total variance, indicating that thermo-physical properties are very suitable for Emmental cheese characterisation. Furthermore, linear discriminant analysis was conducted to classify the Emmental cheeses according to the producing dairy based on similarities and/or differences of the thermo-physical properties. Linear discriminant analysis revealed a correct classification of 62.2%. The results offer cheese producers the possibility of varying the manufacturing process and producing similar products with the aim of promoting geographical identification or products with greater differentiating attributes, to enhance distinctiveness of each producing dairy.
  • [Show abstract] [Hide abstract]
    ABSTRACT: This report with recommendations is the result of an expert panel meeting on PAT applications in food industry that was organized by the M3C Section of the European Society of Biochemical Engineering Science (ESBES) at the 10th ESBES Symposium. The aim of the panel was to provide an update on the present status of the subject and to identify critical needs and issues for wider applications of PAT in food industry. A brief description of the current stateof-the-art and industrial uptake of the methodology is provided in this report. It concludes with a number of recommendations to facilitate further developments and a wider application of PAT in food industry.
    Biotechnology Journal 08/2015; 10(8):1095-100. DOI:10.1002/biot.201400773 · 3.49 Impact Factor
  • O. Paquet-Durand · V. Zettel · R. Kohlus · B. Hitzmann ·
    [Show abstract] [Hide abstract]
    ABSTRACT: Wheat grains consist of three major components, the bran layer, the endosperm and the germ, with very different water sorption kinetic. The original two parameter Peleg model cannot describe a water sorption process well for such heterogeneous compounds. Therefore, the model was modified to account for the two biggest fractions, the endosperm and the bran layer. This modified model has four parameters and can be used to accurately describe the hydration process of wheat grains. Two experiments were carried out, an initial experiment to get rough parameter values and a second experiment, which was designed optimally by using the Cramer-Rao lower bond method. The percentage parameter errors for the four parameters of the modified model were reduced from 669%, 24%, 12%, and 2.4% to 38%, 5.4%, 4.5% and 1.9% respectively. The presented results demonstrate the advantage of optimal design of experiments.
  • [Show abstract] [Hide abstract]
    ABSTRACT: In the work reported here, baker’s yeast (Saccharomyces cerevisiae), used yeast, and apple pomace were used as feed for the production of liquid biofuels in a continuous one-step process under hydrothermal conditions in the presence of excess hydrogen and K2CO3. The biomass conversion experiments were performed in an up-flow reactor under near-critical water conditions (T 330–450 °C, p 20–32 MPa). The products consisted of three phases: an oil-like organic phase, a gaseous phase, and an aqueous phase. Higher concentrations of organic carbon in the process resulted in a higher product yield. The heating value of the organic phase was up to 37.6 MJ kg−1. Liquefaction of yeast without any addition of K2CO3 also resulted in liquid oil, but the quality and the yield of the oil product were lower. The use of K2CO3 catalyst provides significant effect on the apple pomace conversion process. The carbon mass yield ranged from 270 to 400 g kg−1. Our older statement that the reaction of temperature of 400 °C is optimal for the oil yield and quality has been confirmed with the present new experimental results.
  • Source
    Saskia M Faassen · Bernd Hitzmann ·
    [Show abstract] [Hide abstract]
    ABSTRACT: On-line sensors for the detection of crucial process parameters are desirable for the monitoring, control and automation of processes in the biotechnology, food and pharma industry. Fluorescence spectroscopy as a highly developed and non-invasive technique that enables the on-line measurements of substrate and product concentrations or the identification of characteristic process states. During a cultivation process significant changes occur in the fluorescence spectra. By means of chemometric modeling, prediction models can be calculated and applied for process supervision and control to provide increased quality and the productivity of bioprocesses. A range of applications for different microorganisms and analytes has been proposed during the last years. This contribution provides an overview of different analysis methods for the measured fluorescence spectra and the model-building chemometric methods used for various microbial cultivations. Most of these processes are observed using the BioView® Sensor, thanks to its robustness and insensitivity to adverse process conditions. Beyond that, the PLS-method is the most frequently used chemometric method for the calculation of process models and prediction of process variables.
    Sensors 05/2015; 15(5):10271-10291. DOI:10.3390/s150510271 · 2.25 Impact Factor
  • Marius Nache · Rico Scheier · Heinar Schmidt · Bernd Hitzmann ·
    [Show abstract] [Hide abstract]
    ABSTRACT: The feasibility of using chemometrics and Raman spectroscopy as a fast and non-invasive method to monitor the early postmortem lactate accumulation and pH decline in pork meat has been investigated. For this application, an on-line monitoring methodology has not yet been established. Based on raw Raman spectra of porcine semimembranosus muscles, a range of spectral pre-processing and multivariate calibration techniques were investigated to develop and test on-line prediction models for the meat quality parameters. The influence of the pre-processing methods on the prediction speed, robustness and accuracy performance of the employed linear and non-linear algorithms was compared. Identification of the most effective chemometric evaluation procedure was performed using least square linear regression together with locally weighted regression and metaheuristic data optimization methods such as the genetic algorithm and the ant colony optimization. The herein presented analysis suggests that the locally weighted regression applied to the standard normal variate (SNV) normalized Raman spectra provides the most accurate and robust models with a cross-validated coefficient of determination (r2cv) of 0.97 for pH and lactate, a cross-validated root mean square error (RMSECV) of 4.5 mmol/kg for the lactate prediction and 0.06 pH-units for the pH prediction. These results demonstrate the great potential of combining chemometrics and Raman spectroscopy for on-line meat quality control applications.
    Chemometrics and Intelligent Laboratory Systems 02/2015; 142. DOI:10.1016/j.chemolab.2015.02.002 · 2.32 Impact Factor
  • [Show abstract] [Hide abstract]
    ABSTRACT: The aim of process optimization is obtaining higher productivity and profit in chemical or bio-chemical process. For that, one must apply control techniques that closely correlate with our ability to characterize a process. Optical sensors associated with chemometric modeling are considered a natural choice for non-intrusive and high sensitivity measurements. This study focus on wheat flour characterization (usual and mandatory action, widely present on the food industry) using Near-Infrared, comparing two approaches for spectral region selection: modified CSMWPLS and PSCM/ACO. Spectroscopic data is assayed using a combination of CSMWPLS and variable selection algorithm based on Ant Colony Optimization. Protein prediction results are compared with standards PLS, CSMWPLS and PSCM/ACO models. Prediction capability improved 46% using modified CSMWPLS and PSCM/ACO modeling, confirming the efficiency of the proposed characterization methods and chemometric modeling strategy.
    Chemometrics and Intelligent Laboratory Systems 01/2015; 142. DOI:10.1016/j.chemolab.2015.01.007 · 2.32 Impact Factor
  • O. Roeva · T. Pencheva · S. Tzonkov · B. Hitzmann ·
    [Show abstract] [Hide abstract]
    ABSTRACT: A new functional state, namely dissolved oxygen limitation state for both bacteria Escherichia coli and yeast Saccharomyces cerevisiae fed-batch cultivation processes is presented in this study. Functional state modelling approach is applied to cultivation processes in order to overcome the main disadvantages of using global process model, namely complex model structure and a big number of model parameters. Alongwith the newly introduced dissolved oxygen limitation state, second acetate production state and first acetate production state are recognized during the fed-batch cultivation of E. coli, while mixed oxidative state and first ethanol production state are recognized during the fed-batch cultivation of S. cerevisiae. For all mentioned above functional states both structural and parameter identification is here performed based on experimental data of E. coli and S. cerevisiae fed-batch cultivations.
  • O. Paquet-Durand · V. Zettel · B. Hitzmann ·
    [Show abstract] [Hide abstract]
    ABSTRACT: In this work an optimal experimental design is applied to determine the parameters of the Peleg model for water absorption kinetics as precisely as possible. The parameter estimation errorsmere calculated using equidistant measurements in time as well as equidistant measurements on a logarithmic time scale. They were then compared to the optimal design results calculated, using multiple optimality criteria, constant measurement errors and the error of the initial rough parameter estimates. It is-demonstrated that the optimal experimental-design is beneficial for the parameter error estimation for at least one of the two parameters of the Peleg model equation. The parameter estimation errors could be reduced by up to 62% compared to an equidistant experimental design. Furthermore an approximation function for the optimal design process is developed. Depending on the used optimality criterion, with this function optimal measuring points for the Peleg model can be calculated directly and therefore much faster than the optimization procedure for the optimal experimental design. In case of the very commonly used D-optimality criterion, this function is even an exact solution. The deviation of the parameter estimation errors from the approximation function are mostly around 0.01% and therefore negligible.
    Chemometrics and Intelligent Laboratory Systems 11/2014; 140. DOI:10.1016/j.chemolab.2014.10.006 · 2.32 Impact Factor
  • Stefan Nöbel · Christian Hahn · Bernd Hitzmann · Jörg Hinrichs ·
    [Show abstract] [Hide abstract]
    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 11/2014; 39(1). DOI:10.1016/j.idairyj.2014.06.001 · 2.01 Impact Factor
  • Florian T. Hecker · Marc Stanke · Thomas Becker · Bernd Hitzmann ·
    [Show abstract] [Hide abstract]
    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. DOI:10.1016/j.eswa.2014.03.047 · 2.24 Impact Factor
  • M. Stanke · V. Zettel · S. Schütze · B. Hitzmann ·
    [Show abstract] [Hide abstract]
    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. DOI:10.1016/j.jfoodeng.2014.01.012 · 2.77 Impact Factor
  • I. Hristozov · T. Pencheva · E. Staerk · B. Hitzmann · T. Scheper · St. Tzonkov ·
    [Show abstract] [Hide abstract]
    ABSTRACT: An application of functional states modelling approach for aerobic batch baker's yeast fermentation is presented in this paper. The functional states help in monitoring and control of complex processes such as bioprocesses. The main idea is to use a two-level hierarchy where on the top level the process is divided into macrostates, called functional states, according to behavioural equivalence. In a functional state, the process is described by a conventional type of model, called local model, which is valid in this functional state. To illustrate the concept of functional states in fermentation processes, experimental data and simulations of an aerobic batch baker's yeast fermentation are presented.
    Biotechnology & Biotechnological Equipment 04/2014; 15(2):132-135. DOI:10.1080/13102818.2001.10819145 · 0.30 Impact Factor
  • Source
    O. Roeva · T. Pencheva · Y. Georgieva · B. Hitzmann · S. Tzonkov ·
    [Show abstract] [Hide abstract]
    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. DOI:10.1080/13102818.2004.10817146 · 0.30 Impact Factor
  • Bianca Grote · Torsten Zense · Bernd Hitzmann ·
    [Show abstract] [Hide abstract]
    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; DOI:10.1016/j.foodcont.2013.09.039 · 2.81 Impact Factor
  • [Show abstract] [Hide abstract]
    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 03/2014; 801:575-81. DOI:10.1007/978-1-4614-3209-8_73 · 1.96 Impact Factor
  • Source
    Tetyana Beltramo · Susanne Theuerl · Michael Klocke · Bernd Hitzmann ·

    Journal of Cheminformatics 03/2014; 6(Suppl 1):P26. DOI:10.1186/1758-2946-6-S1-P26 · 4.55 Impact Factor
  • Source
    Marius Nache · Rico Scheier · Heiner Schmidt · Bernd Hitzmann ·

    Journal of Cheminformatics 03/2014; 6(Suppl 1):P21. DOI:10.1186/1758-2946-6-S1-P21 · 4.55 Impact Factor
  • [Show abstract] [Hide abstract]
    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; 132. DOI:10.1016/j.chemolab.2014.01.012 · 2.32 Impact Factor
  • V. Zettel · A. Krämer · F. Hecker · B. Hitzmann ·
    [Show abstract] [Hide abstract]
    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 03/2014; 240(3):1-8. DOI:10.1007/s00217-014-2368-8 · 1.56 Impact Factor

Publication Stats

1k Citations
302.76 Total Impact Points


  • 2011-2015
    • Hohenheim University
      • Process Analysis and Cereal Technology Unit
      Stuttgart, Baden-Württemberg, Germany
  • 1999-2012
    • Leibniz Universität Hannover
      • Institute of Technical Chemistry
      Hannover, Lower Saxony, Germany
  • 1990-2008
    • California Institute of Technology
      • Division of Chemistry and Chemical Engineering
      Pasadena, California, United States