Institut für Lebensmittelwissenschaft und Biotechnologie

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Application of nature-inspired optimization algorithms to improve the production efficiency of small and medium-sized bakeries
(2023) Babor, Md Majharul Islam; Hitzmann, Bernd
Increasing production efficiency through schedule optimization is one of the most influential topics in operations research that contributes to decision-making process. It is the concept of allocating tasks among available resources within the constraints of any manufacturing facility in order to minimize costs. It is carried out by a model that resembles real-world task distribution with variables and relevant constraints in order to complete a planned production. In addition to a model, an optimizer is required to assist in evaluating and improving the task allocation procedure in order to maximize overall production efficiency. The entire procedure is usually carried out on a computer, where these two distinct segments combine to form a solution framework for production planning and support decision-making in various manufacturing industries. Small and medium-sized bakeries lack access to cutting-edge tools, and most of their production schedules are based on personal experience. This makes a significant difference in production costs when compared to the large bakeries, as evidenced by their market dominance. In this study, a hybrid no-wait flow shop model is proposed to produce a production schedule based on actual data, featuring the constraints of the production environment in small and medium-sized bakeries. Several single-objective and multi-objective nature-inspired optimization algorithms were implemented to find efficient production schedules. While makespan is the most widely used quality criterion of production efficiency because it dominates production costs, high oven idle time in bakeries also wastes energy. Combining these quality criteria allows for additional cost reduction due to energy savings as well as shorter production time. Therefore, to obtain the efficient production plan, makespan and oven idle time were included in the objectives of optimization. To find the optimal production planning for an existing production line, particle swarm optimization, simulated annealing, and the Nawaz-Enscore-Ham algorithms were used. The weighting factor method was used to combine two objectives into a single objective. The classical optimization algorithms were found to be good enough at finding optimal schedules in a reasonable amount of time, reducing makespan by 29 % and oven idle time by 8 % of one of the analyzed production datasets. Nonetheless, the algorithms convergence was found to be poor, with a lower probability of obtaining the best or nearly the best result. In contrast, a modified particle swarm optimization (MPSO) proposed in this study demonstrated significant improvement in convergence with a higher probability of obtaining better results. To obtain trade-offs between two objectives, state-of-the-art multi-objective optimization algorithms, non-dominated sorting genetic algorithm (NSGA-II), strength Pareto evolutionary algorithm, generalized differential evolution, improved multi-objective particle swarm optimization (OMOPSO) and speed-constrained multi-objective particle swarm optimization (SMPSO) were implemented. Optimization algorithms provided efficient production planning with up to a 12 % reduction in makespan and a 26 % reduction in oven idle time based on data from different production days. The performance comparison revealed a significant difference between these multi-objective optimization algorithms, with NSGA-II performing best and OMOPSO and SMPSO performing worst. Proofing is a key processing stage that contributes to the quality of the final product by developing flavor and fluffiness texture in bread. However, the duration of proofing is uncertain due to the complex interaction of multiple parameters: yeast condition, temperature in the proofing chamber, and chemical composition of flour. Due to the uncertainty of proofing time, a production plan optimized with the shortest makespan can be significantly inefficient. The computational results show that the schedules with the shortest and nearly shortest makespan have a significant (up to 18 %) increase in makespan due to proofing time deviation from expected duration. In this thesis, a method for developing resilient production planning that takes into account uncertain proofing time is proposed, so that even if the deviation in proofing time is extreme, the fluctuation in makespan is minimal. The experimental results with a production dataset revealed a proactive production plan, with only 5 minutes longer than the shortest makespan, but only 21 min fluctuating in makespan due to varying the proofing time from -10 % to +10 % of actual proofing time. This study proposed a common framework for small and medium-sized bakeries to improve their production efficiency in three steps: collecting production data, simulating production planning with the hybrid no-wait flow shop model, and running the optimization algorithm. The study suggests to use MPSO for solving single objective optimization problem and NSGA-II for multi-objective optimization problem. Based on real bakery production data, the results revealed that existing plans were significantly inefficient and could be optimized in a reasonable computational time using a robust optimization algorithm. Implementing such a framework in small and medium-sized bakery manufacturing operations could help to achieve an efficient and resilient production system.
Beschreibung und Optimierung der Vorgänge der dynamischen Gefriertrocknung
(2018) Pliske, Roland; Kohlus, Reinhard
Freeze-drying is a gentle but also time-consuming drying method. One reason for the longer drying times is the formation of a dry layer during drying, which is a heat and mass transfer resistance. One approach for reducing the drying time is removing these resistances. The detail of an approach to remove the dry layer within a special powder mixer has been investigated. The process of freeze-drying while agitating has been termed ‘dynamic freeze-drying’. The used mixer was a plow-share type, in which the dry layer is actively rubbed-off permanently during the drying process. In this process the drying always takes place on the moister particle surface. This corresponds to the characteristics of a constant drying rate period, which can be considered confirmed by independent dynamic freeze-drying experiments. Freeze-drying process typically do not show a constant drying rate period. The drying front retreats immediately at the start of drying into inside of the particle. Therefore, drying rate of dynamic-freeze drying could be increased. The drying rate can be furthermore increased applying higher heating temperature in the case of dynamic freeze-drying compared to static freeze-drying. The danger of a collapse is prevented by abrasion of the dry layer during dynamic freeze drying. It has also been shown that under identical drying conditions, dynamic freeze-drying has an up to tenfold faster drying rate compared to conventional, static freeze-drying. One reason for this is a higher conductive heat flux into the bed. Another reason is the conversion of the kinetic energy into heat energy during the mixing of the bed, which is additionally used for the sublimation. Since the dry layer is removed during dynamic freeze-drying, the advantage should lie by larger initial diameters, because there are greater heat and mass transfer resistances compared to smaller initial particle diameters. This effect is overcompensated by the number of particles that are present if the same initial mass will be used for creation smaller particles than bigger particles. The contact number of particles to mixer wall determines the heat transfer by conduction and particle to particle determines the heat transfer by friction. For this reason, the drying time of the dynamic freeze-drying of smaller diameter beds is always lower. All results indicate that the number of contact points of particles to the mixer wall and other particles is relevant for the energy transfer to the bed during dynamic freeze-drying. As the particles become smaller during the drying process, however their number remains constant, and so is the effective heat transfer coefficient. A positive effect on drying rate was determined for the dried powder, which is within the mixer during the drying process. While drying with low rotational frequency less dried powder was discharged from the mixer and the experimental drying times always were lower than the modeled ones. The powder is heated at the mixer wall and is then afterwards reintroduced into the bed. At high rotational frequencies the powder is fluidized up more intensively and discharged with the water vapor from the mixer. During the drying process the water vapor leaves the mixer and partially the dried final product, too, and the load decreases and the energy input as well. Freeze-drying covers a large part of microorganism conservation so called starter culture conservation. First trials in using dynamic freeze-drying for this application have been conducted. Dynamic freeze-drying has been used in the drying of microorganisms in order to compare the viable count and the activity of the dried microorganisms with those from static freeze-drying. The presented results show that the viable count of the dynamic freeze-dried microorganisms is reduced. The activity however is partly higher than that of static freeze-dried microorganisms, which indicates a stress activation. These results were found using starter cultures that were frozen without adding "protective medium". Whether trials using protective medium will show similar results is currently unclear. The phenomenon of stress activation has to be confirmed using a large variety of lactic acid bacteria.
Bioprospecting for novel lipopeptide-producing strains for potential application in food and agriculture
(2024) Akintayo, Stephen Olusanmi; Hausmann, Rudolf
The need for sustainable alternatives to chemical products has been a huge topic in recent years and has put a demand on researchers and biotechnological companies to come up with bio-based alternatives to several chemical products. In line with this, interest in biosurfactants as alternatives to chemical surfactants is on the rise. Biosurfactants produced by microorganisms have great potential for application in detergents, personal care products, and pharmaceuticals, as well as in environmental, food processing, and agricultural applications. There are a few types of biosurfactants, including lipopeptides, which are primarily produced by Bacillus species and exhibit antimicrobial properties in addition to the well-known surface activity, surface tension reduction, and emulsifying ability of biosurfactants. Like other biosurfactants, lipopeptides have found more use in environmental applications such as bioremediation and microbial enhanced oil recovery (MEOR), while their use in agriculture and food industries remains limited due to concerns that may be related to acceptability, compatibility, and low yield by wild-type strains. To overcome these challenges, this thesis sought to find novel wild-type lipopeptide-producing strains from food-related sources that could be presumably safe for use in agriculture and food applications. To achieve this goal, a screening approach that combined several methods was adopted to identify potential high-yield wild-type, and possibly novel lipopeptide-producing strains. The ability of selected strains as promising biocontrol agents in agriculture was also evaluated. In Publication 1, potential lipopeptide-producing strains were isolated from food-related sources and screened for lipopeptide production. The screening approach combined microbiological and molecular identification of strains, with screening methods based on biosurfactant properties, as well as chemical analysis of surfactin production. Strains with promising lipopeptide-production potential belonging to three genera of Bacillus, Lysinibacillus and Priestia were identified. These strains included several exotic species that were either previously unknown or minimally studied with respect to LP production. Multiple strains that produced more than 150 mg L-1 surfactin, including a B. subtilis strain with a yield of about 1.5 g L−1 were discovered. In Publication 2, two promising LP-producing B. velezensis strains ES1-02 and EFSO2-04 were evaluated for their biocontrol potential and compared with commercial biocontrol strains B. velezensis QST713 and FZB42. The isolated strains demonstrated biocontrol ability comparable to QST713 against Diaporthe spp., which are notorious fungal pathogens of soybeans and other economically important crops. Co-incubation of strain ES1-02 with the phytopathogen D. longicolla induced a 10-fold increase in surfactin production. The broader molecular response of B. velezensis to plant pathogens investigated through an associated global proteome analysis showed the adaptation and response mechanisms of B. velezensis to plant pathogens. In general, B. velezensis seemed to adopt LP- modulation, physiological adaptation, and increased abundance of antimicrobial compounds as antagonistic and adaptation strategies for interaction with the phytopathogen D. longicolla. In Publication 3, genomic techniques were used in the discovery and description of a novel lipopeptides-producing species of the genus Lysinibacillus for which the name Lysinibacillus irui sp. nov. was proposed. This Gram-positive, motile, aerobic, rod-shaped, endospore-forming strain designated IRB4-01T was isolated from fermented African locust beans (Iru) and as such was named after Iru. A comprehensive chemotaxonomic analysis of the strain showed that the cell wall peptidoglycan type is A4α (Lys–Asp), and MK-7 is the major respiratory quinone. Detailed information about the polar lipids and major cellular fatty acids was also obtained. The G+C content of the genomic DNA was 37.4 mol%. Surfactin production by this novel strain was described in Publication 1 of this work.
Characterization of the aroma properties in fragrant rapeseed oil and aroma variation during critical roasting phase
(2023) Zhang, Youfeng; Zhang, Yanyan
Rapeseed oil is one of the third most-produced vegetable oil in the world, which is appreciated for its characteristic flavor and high nutritional value. Fragrant rapeseed oil (FRO) produced by a typical roasting process is popular for its characteristic aroma, which has an annual consumption exceeding 1.5 million tons. However, the changes in aroma blueprint of FRO during the typical roasting processing are still unclear, which challenges rapeseed oil quality and consumer acceptance. Accordingly, the aim of this work was to investigate the aroma characteristics and their precursors pyrolysis behavior of FRO to provide a basis and guidance for the control of FRO aroma quality during production processing. First, a systematic review on summarizing, comparing, and critiquing the literature regarding the flavor of rapeseed oil, especially about employed analysis techniques (i.e., extraction, qualitative, quantitative, sensorial, and chemometric methods), identified representative/off-flavor compounds, and effects of different treatments during the processes (dehulling, roasting, microwave, flavoring with herbs, refining, oil heating, and storage) was performed. One hundred and thirty-seven odorants found in rapeseed oil from literature are listed, including aldehydes, ketones, acids, esters, alcohols, phenols, pyrazines, furans, pyrrolines, indoles, pyridines, thiazoles, thiophenes, further S-containing compounds, nitriles, and alkenes, and possible formation pathways of some key aroma-active compounds are also proposed. Nevertheless, some of these compounds require further validation (e.g., nitriles) due to lack of recombination experiments in the previous work. To wrap up, advanced flavor analysis techniques should be evolved toward time-saving, portability, real-time monitoring, and visualization, which aims to obtain a “complete” flavor profile of rapeseed oil. Aparting from that, studies to elucidate the influence of key roasting processing on the formation of aroma-active compounds are needed to deepen understanding of factors resulting in flavor variations of rapeseed oil. Following, a systematic comparison among five flavor trapping techniques including solid-phase microextraction (SPME), SPME-Arrow, headspace stir bar sorptive extraction (HSSE), direct thermal desorption (DTD), and solvent-assisted flavor evaporation (SAFE) for hot-pressed rapeseed oil was conducted. Besides, methodological validation of these five approaches for 31 aroma standards found in rapeseed oil was conducted to compare their stability, reliability, and robustness. For the qualification of the odorants in hot-pressed rapeseed oil, SAFE gave the best performance, mainly due to the high sample volumes, but it performed worse than other methods regarding linearity, recovery, and repeatability. SPME-Arrow gave good performances in not only odorant extraction but also quantification, which is considered most suitable for quantifying odorants in hot-pressed rapeseed oil. Taking cost/performance ratio into account, SPME is still an efficient flavor extraction method. Multi-method combination of flavor capturing techniques might also be an option of aroma analysis for oil matrix. Afterwards, by application of the Sensomics approach the key odorants in representative commercial FRO samples were decoded. On the basis of the aroma blueprint, changes of overall aroma profiles of oils and their key odorants were studied and compared in different roasting conditions. To better simulate industrial conditions, high temperatures (150-200 ºC) were used in our roasting study, which was rarely studied before. Identification and quantitation of the key odorants in FRO were well performed by means of the Sensomics concept. Glucosinolate degradation products were a special kind of key odorants existing in rapeseed oil. Most of the odorants showed first rising and then decline trends as the roasting process progressed. Aroma profile results showed that high-temperature-short time and low-temperature-long time conditions could have similar effects on the aroma profiles of roasted rapeseed oils, which could provide a reference for the time cost savings in industrial production. To gain the fundamental knowledge of the aroma formation in FRO, the thermal degradation behavior of progoitrin (the main glucosinolate of rapeseed) and the corresponding generated volatile products were investigated in liquid (phosphate buffer at a pH value of 5.0, 7.0, or 9.0) and solid phase systems (sea sand and rapeseed powder). The highest thermal degradation rate of progoitrin at high temperatures (150-200 ºC) was observed at a pH value of 9.0, followed by sea sand and then rapeseed powder. It could be inferred that bimolecular nucleophilic substitution reaction (SN2) was mainly taken place under basic conditions. The highest degradation rate under basic conditions might result from the high nucleophilicity of present hydroxide ions. Under the applied conditions in this study, 2,4-pentadienenitrile was the major nitrile formed from progoitrin during thermal degradation at high temperature compared to l-cyano-2-hydroxy-3-butene, which might be less stable. The possible formation pathways of major S-containing (thiophenes) and N-containing (nitriles) volatile (flavor) compounds were proposed. Hydrogen sulfide, as a degradation product of glucosinolates, could act as a sulfur source to react further with glucose to generate thiophenes. Overall, the present work comprehensively documented the effects of thermal conditions and matrices on the aroma characteristics, aroma profiles, and key odorants of hot-pressed rapeseed oil, which could provide data and theoretical basis for the flavor control of FRO under thermal treatment at actual production temperatures (150-200 °C).
Characterization of the rehydration behavior of food powders
(2019) Wangler, Julia; Kohlus, Reinhard
The rehydration behavior of food powders is of high importance in terms of powder processing and product quality. Rehydration of powders mainly depends on the physical powder characteristics particle size, porosity and wettability, the latter being expressed by the contact angle between solid and rehydrating liquid. With focus on food powders, it could be shown that the rehydration behavior is strongly influenced by dynamic changes of these physical characteristics. This includes the initiation of dissolution and swelling directly after powder-liquid contact. Especially in case of biopolymers, which were investigated in detail by the example of xanthan gum, guar gum and alginate, these processes are important to describe their rehydration behavior. Due to the special characteristics of these biopolymers dissolution and swelling result in an increase of viscosity as well as in a decrease of bulk porosity. The kinetics and interactions of these processes significantly affect the individual steps of rehydration and have to be considered in describing the process of food powder rehydration. For inert powder-liquid systems capillary liquid uptake into a powder bulk can be described by the Washburn equation which equates the capillary pressure and the hydrodynamic flow resistance. This approach was used as basic equation to describe capillary liquid uptake of food powders. The validity of the original approach is restricted to the case of constant powder and liquid properties. With regard to food powders, changes within the powder-liquid system were considered by a stepwise adaption of the variables of the Washburn equation. Thus, the first part of this thesis focused on establishing and defining methods to characterize the dynamics of the physical properties particle size, bulk porosity, viscosity and contact angle. This enabled a more detailed characterization of the interactions between food powder and liquid during rehydration. Wettability of food powders in contact with dist. water was assessed by contact angle measurements. Contact angles were 52° for alginate, 58.1° for xanthan gum and 70° for guar gum which confirmed their hydrophilic character. To describe the change of the bulk porosity a rheological measurement set-up was constructed to quantify the swelling behavior. Influence of viscosity on rehydration was determined by measuring the concentration dependent viscosity increase and the rate of viscosity increase over time. The change of viscosity as a consequence of dissolution allowed conclusions about the dissolution rate of biopolymers in highly concentrated situations. These results indicated that rehydration of guar gum is mainly influenced by viscosity effects whereas swelling has the highest impact on the rehydration behavior of xanthan gum and alginate. Further methods such as Nuclear Magnetic Resonance analysis enabled a more detailed characterization concerning the dynamics of powder-liquid interactions and the strength of water binding within these biopolymer gels. The strength of water binding was found to correlate with the stability of highly concentrated biopolymer aggregates. The aggregate stability was determined by rheological analyses and is of importance, particularly with regard to powder dispersability. To predict food powder rehydration, a model was established using a VoF approach. To simulate capillary liquid rise based on physical characteristics, dynamic changes were resolved both spatial and temporally. To describe particle and liquid properties more precisely, a model system consisting of biopolymer coated glass beads was developed by fluid bed technology. By the variation of the coating layer thickness and the coating material, dynamic changes within the system could be controlled which enabled a more differentiated description. A parameter variation study was conducted to simulate the influence and interaction of dynamic processes on capillary liquid uptake into such powder systems. Capillary liquid uptake into the coated glass beads was investigated experimentally. It could be shown that even with coating layers of 0.5 µm dynamic effects are sufficiently strong to cause a stop of capillary liquid uptake. It has been shown that viscosity development dominates guar gum rehydration whereas swelling is the prevalent mechanism in xanthan gum and alginate rehydration. Simulation of capillary liquid rise demonstrated that the influence of the coating layer thickness is not significant. This result could be explained by the slow dissolution rates of the biopolymer samples. Calculations indicated that even a coating layer of 0.5 µm could only be dissolved partially after a dissolution time of 250 s. This explains the little impact of coating layer thickness on viscosity development and thus on capillary liquid uptake. Further explanations focus on biopolymer swelling. Simulation showed that coating layers of 0.5 µm are sufficient to cause swelling-induced pore-blocking conditions.
Development of rapid analytical methods for coffee quality assessment: Spectroscopy and chemometrics approach
(2024) Munyendo, Leah Masakhwe; Hitzmann, Bernd; Zhang, Yanyan
The assessment of coffee quality is based on the physical characteristics (bean quality), chemical constituents, and cup quality. Different factors, including altitude, genetics, management conditions, presence of adulterants, roasting, geographical origin, processing methods, and storage, affect the coffee quality. To meet the consumers' expectations regarding quality, the development of fast, new, and advanced analytical techniques for assessing the factors affecting coffee quality is a central aspect. Therefore, this research aimed to develop spectroscopic techniques complemented with chemometrics for evaluating the factors affecting coffee quality. The first specific objective was to investigate the ability of a deep autoencoder neural network to detect adulterants in roasted Arabica coffee and to determine a coffee’s geographical origin using near‐infrared (NIR) spectroscopy. Arabica coffee was adulterated with Robusta coffee or chicory at adulteration levels ranging from 2.5 % to 30 % in increments of 2.5 % at light, medium, and dark roast levels. Based on the results, all the samples adulterated with chicory were detectable by the autoencoder at all roast levels. For Robusta-adulterated samples, the detection was possible at adulteration levels above 7.5 % at medium and dark roasts. One can attribute the observations to potential differences in the chemical composition among the samples. Additionally, it was possible to differentiate coffee samples from different geographical origins. As a continuation of the first objective, the potential of NIR spectroscopy to quantify Robusta coffee or chicory in roasted Arabica coffee using different regression models constructed from the linear discriminant analysis (LDA) or principal component analysis (PCA) features was investigated. In addition, two classification methods (k-nearest neighbor regression (KNR) and LDA) were used. The regression models derived from LDA-extracted features exhibited better accuracies than those derived from PCA-extracted features. The two feature extraction methods exhibit differences in their working principle. PCA focuses on identifying the direction of maximum variance regardless of the adulteration levels. In contrast, LDA identifies the feature subspace that optimizes the separability of the classes (adulteration levels) and minimizes the variance within the class. Therefore, LDA extracted the features better than PCA, explaining the better performance of the regression models constructed from its features. The models provided satisfactory results with the coefficient of determination (R2) values above 0.92 for both the adulterants, indicating their efficiency in quantifying Robusta coffee or chicory in roasted Arabica coffee. For the classification methods, the LDA model performed better than KNR. Another focus of this doctoral research was to develop analytical tools based on Raman and NIR spectroscopy for real-time monitoring of the coffee roasting process by predicting chemical changes in coffee beans during roasting. Green coffee beans of Robusta and Arabica species were roasted at 240 °C for 5, 7, 9, 11, 13, 15, 17, 19, 21, 23, 25, 27, and 29 minutes. Four process runs were performed for each coffee species. The spectra of the ground samples were taken using the two spectrometers and modeled by the KNR, partial least squares regression (PLSR), and multiple linear regression (MLR). All the models based on the NIR spectra provided satisfactory results for the prediction of chlorogenic acid, trigonelline, and DPPH radical scavenging activity with low relative root mean square error of prediction (pRMSEP < 9.469 %) and high R2 (> 0.916) values. Similarly, all the models based on the Raman spectra provided acceptable prediction accuracies for monitoring the dynamics of chlorogenic acid, trigonelline, and DPPH radical scavenging activity (pRMSEP < 7.849 % and R2> 0.944). In conclusion, this research proposes different approaches that would allow valuable decisions regarding coffee quality to be made quickly and efficiently. The study suggests using NIR spectroscopy to determine a coffee’s geographical origin and detect and quantify adulterants in roasted coffee. The findings reveal that the method could be a promising tool for routine coffee quality control applications in the coffee industry and other legal sectors. The study also proposes using different spectroscopic methods (NIR and Raman) to monitor a coffee roasting process. One can consider the presented approaches as essential steps toward optimizing the roasting process at an industrial scale as they permit instantaneously taking significant process decisions.
Entwicklung von datengetriebenen Auswerteverfahren zur Analyse und Schätzungder Reaktorleistung von Biogasanlagen
(2020) Beltramo, Tanja; Hitzmann, Bernd
The production of biogas is very complex process, which runs in some stages involving different microorganisms. Microbiological diversity of the process depends mainly on the composition of substrate and ambient conditions, such as process temperature. The fact is, the development and composition of the microbiological communities of the process are difficult to predict. Thus, the control and evaluation of such complex biological processes are very time consuming and expensive. In Germany the evaluation of the biogas plants can be performed according to the VDI-Norm 4630, which describes the methods for the evaluation of fermentation of organic materials including characterization of the substrate, sampling, collection of material data and fermentation tests. For that specially equipment and skilled personnel are required. Moreover, the evaluation procedure is very time consuming. That is why a new state-of-the-art alternative for the evaluation purposes is necessary to simplify and to speed up the assessment of the biogas production processes. The aim of this doctoral thesis is the development of a fast and reliable method for the evaluation of the biogas production processes. Therefore the mathematical modelling should identify significant process variables able to evaluate the whole process. For the optimization of mathematical models metaheuristic tools were used. In this doctoral thesis two different data sets were used – experimental data and simulated data. The experimental data were collected in projects “Biogas-Biocoenosis” (FKZ 22010711, Dr. Michael Klocke, Leibnitz-Institute für Agrartechnik und Bioökonomie e.V., Potsdam) and “Biogas-Enzyme” (FKZ 22027707, Dr. Monika Heiermann, Leibnitz-Institute für Agrartechnik und Bioökonomie e.V., Potsdam). The simulated data set was generated using the Anaerobic Digestion Model No.1 (ADM1). The chemical process variables were used as the independent process variable set, while the biogas production output represented the dependent process variable. Prediction of the biogas production was done using linear and nonlinear mathematic models. Here, Partial-Least-Square-Regression (PLSR), Locally-Weighted-Regression (LWR) and Artificial Neural Networks (ANN) were implemented. In order to identify the most significant undependable process variables optimization algorithms were used, Ant Colony Optimization (ACO) and Genetic Algorithm (GA). Prediction capacity was evaluated using two model evaluation variables, Root Mean Square Error (RMSE) and Coefficient of Determination (R2). Figure 1 in Supplementary represents the flow chart of the developed methodology applied for ADM1 generated data set. In Figure 2 (Supplementary) there is a flow chart of the developed methodology applied for the experimentally collected data. The developed approaches could be successfully used for the prediction of the desired process variable, biogas production rate. The variable selection done with the help of metaheuristic optimization algorithms improved the prediction results and reduced number of the independent process variables. Hydraulic retention time, dry matter, neutral detergent fibre, acid detergent fibre and n-butyric acid were identified as the most significant ones. The best prediction was obtained using ANN models. Here, the error of prediction was low and the coefficient of determination high. The successful implementation of the developed methodology proved mathematical models to be an effective alternative method capable to evaluate and to optimize complicated biological processes. Furthermore, it would be mandatory further experimental evaluation of the developed strategy, using the model-based process information.
Impact of process parameters on the sourdough microbiota, selection of suitable starter strains, and description of the novel yeast Cryptococcus thermophilus sp. nov.
(2013) Vogelmann, Stephanie Anke; Hertel, Christian
The microbiota of a ripe sourdough consists of lactic acid bacteria (LAB), especially of the genus Lactobacillus, and yeasts. Their composition is influenced by the interplay of species or strains, the kind of substrate as well as the process parameters temperature, dough yield, redox potential, refreshment time, and number of propagation steps (Hammes and Gänzle, 1997). As taste and quality of sourdough breads are mainly influenced by the fermentation microbiota, intense research has been focused on determination of sourdough associated species and search for new starter cultures. In recent years, economic competition pressure and new consumer demands have led to steady research for new cereal products, especially with health benefit or for people suffering from celiac disease. For these reasons, alternative cereals like oat and barley (both toxic for celiac disease patients) as well as the celiac disease compatible cereals rice and maize, sorghum and millets, the pseudocereals amaranth, quinoa and buckwheat as well as cassava got into the focus of interest. However, information about the microbiota of sourdoughs fermented with buckwheat, amaranth, quinoa, oat or barley is not available except for the following recent studies: a study about the microbiota of amaranth sourdoughs by Sterr et al. (2009), a study about barley sourdough by Zannini et al. (2009), a study about oat sourdoughs by Huettner et al. (2010) and a study about buckwheat and teff sourdoughs by Moroni et al. (2011). The microbiota of sourdoughs from the other mentioned cereals as well as cassava was multiply characterised but not systematically. Fermentation conditions were partly not clearly defined, and identification of species was often based on physiological criteria only, known to be insufficient for the exact classification of LAB. Thus, in this thesis, the influence of the process parameters substrate, temperature, refreshment time, amount of backslopping dough as well as the interplay between the different species or strains were examined and potential starter strains were selected. In Chapter III, the effect of the substrate on the sourdough microbiota was examined and suitable starter cultures for fermentation of non-bread cereals and pseudocereals were selected. Eleven different flours from wheat, rye, oat, barley, millet, rice, maize, amaranth, quinoa, buckwheat and cassava were inoculated with a starter mixture containing numerous LAB and yeasts. Sourdoughs were fermented at 30 °C and refreshed every 24 hours until the microbiota was stable. Species were identified by PCR-DGGE as well as bacteriological culture and RAPD-PCR, followed by 16S/26S rRNA sequence analysis. In these fermentations, the dominant yeast was Saccharomyces cerevisiae; Issatchenkia (I.) orientalis was only competitive in the quinoa and the maize sourdough. No yeasts were found in the buckwheat and the oat sourdough. The dominant LAB species were Lactobacillus (L.) paralimentarius in the pseudocereal sourdoughs, L. fermentum, L. helveticus and L. pontis in the cereal sourdoughs, and L. fermentum, L. plantarum and L. spicheri in the cassava sourdough. Competitive LAB and yeasts were inserted as starters for a further fermentation using new flours from rice, maize, millet and the pseudocereals. After ten days of fermentation, most of the starter strains were still dominant, but L. pontis and L. helveticus could not compete with the other species. It is remarkable that from the numerous starter strains which all were adapted to or isolated from sourdoughs, only a few were competitive in these fermentations; but if, then in most cases in a lot of different flours. In Chapter IV, the effects of the exogenous process parameters substrate, refreshment time, temperature, amount of backslopping dough as well as competing species on the two microbial associations L. sanfranciscensis ? Candida (C.) humilis and L. reuteri ? L. johnsonii ? I. orientalis were examined. Both associations had previously been found to be competitive in sourdough (Kline and Sugihara, 1971a; Nout and Creemers-Molenaar, 1987; Gobbetti et al., 1994a; Garofalo et al., 2008; Böcker et al., 1990; Meroth et al., 2003a). 28 sourdough batches were fermented under defined conditions until the microbiota was stable. Dominant LAB and yeasts were characterized by bacteriological culture, RAPD-PCR and 16S/26S rRNA gene sequence analysis. The process parameters for the association L. sanfranciscensis ? C. humilis could be defined as follows: rye bran, rye flour or wheat flour as substrate, temperatures between 20 and 30 °C, refreshment times of 12 to 24 hours and amounts of backslopping dough from 5 to 20 %. In addition, the association was predominating against all competing lactic acid bacteria and yeasts. The association L. reuteri ? L. johnsonii ? I. orientalis was competitive at temperatures of 35 to 40 °C, refreshment times of 12 to 24 hours and the substrates rye bran, wheat flour and rye flour, but only with sufficient oxygen supply. Cell counts of I. orientalis fell rapidly under the detection limit when using high amounts of doughs (small ratio of surface to volume) and refreshment times of 12 hours. The fermentations depicted in Chapter III and IV give new information about the influence of process parameters on the sourdough microbiota. The studies show that the sourdough microbiota is markedly influenced by the process parameters and kind and quality of substrate. The competitiveness of a single LAB or yeast is strain specific. Interactions between microorganisms also play an important role. However, for the search for suitable starter strains, it would be beneficial to know the reasons, why a single LAB or yeast strain is better adapted to specific process parameters or substrates than others. One of the starter sourdoughs used for fermentation I described in Chapter III was a sourdough made from cassava flour, inoculated with several LAB. No yeast had been inserted, but several yeasts were isolated from the ripe sourdough, which are supposed to originate from the cassava flour. An unknown yeast species constituted 10 % of the isolated yeasts which is described as novel species Cryptococcus thermophilus sp. nov. in Chapter V. This yeast is characterized by budding on small neck-like structures, no fermentative ability, growth at 42 °C and without vitamins, a major ubiquinone of Q-10, as well as the production of green or blue fluorescent substances in the growth medium. It is distinct from related species by the ability to assimilate raffinose and cadaverine, the inability to assimilate soluble starch, xylitol, galactitol, butane-2,3-diol, sodium nitrite and lysine, and the inability to produce starch-like substances. The closest relatives are the yeasts belonging to the Cryptococcus humicola complex.
Isolierung universell einsetzbarer und mikrobiologisch stabiler Sauerteigstarterkulturen durch spontane Fermentationen mit Amaranth.
(2009) Sterr, Yasemin Arzu; Schmidt, Herbert
Spontaneous fermented sourdoughs prepared from five amaranth flours were screened for the presence of lactic acid bacteria (LAB) that predominate the autochthonous microbiota and thus may be suitable as starter cultures. The doughs were fermented with daily backslopping on the laboratory scale for 10 days with a dough yield of 200 at 30°C. Every 24 hours, the pH-values and total titratable acidity degrees were determined and samples were analyzed for the presence of LAB and yeasts by cultural methods. The identity of the isolates was traced during the fermentation with RAPD-PCR and two different primers, and the strains were identified by sequence analysis of the 16S rDNA genes. The strains Lactobacillus plantarum RTa12, L. sakei RTa14, and Pediococcus pentosaceus RTa11 were selected and applied as starters in further laboratory fermentations. All strains were predominant in repeated experiments, both, as single strains and in combination. During the first 24 h, L. plantarum RTa12 and P. pentosaceus RTa11 grew quite similar. The pH-value dropped earlier with P. pentosaceus RTa11, while both strains gave the same pH-values after 10 h of fermentation. In the challenge test with the autochthonous mikrobiota both strains overgrew the other LAB of the spontaneous fermented dough within the first eight hours, and were therefore considered dominant over the resident microbiota. Influences of the incubation temperature on the fermentation characteristics were mainly assessed in the viable cell counts, the pH-values and the titratable acidity degrees at 25°C. The pH-values for both strains were at high incubation temperatures (30 and 35°C) during the fermentation lower than at 25°C, respectively. However, after 24 h of fermentation both strains reached a pH-value of approximately 4.0 after 24 h. Further sugar, organic acid, mannitol and ethanol profiles of fermented doughs were determined with HPLC. Mainly analyzed metabolites in the doughs were glucose, sucrose, lactate, and acetate. To compare the potential starter cultures with commercial available startercultures, fermentations with two industrial startercultures were performed for 24 h at 30°C and a dough yield of 200. Both strains were able to compete with the commercial available starter cultures concerning viable cell counts, total titratable acidity and pH-values. Because of the dominance of both strains in sourdough fermentations with amaranth, the ability for acidification in a short time, the capacity to compete with the autochthonous mikrobiota, the robustness against lesser effects of the environment, e. g. variation of the temperature, and at least because of the ability to compete with commercial available startercultures, thus, the characterized strains L. plantarum RTa12 and P. pentosaceus RTa11 are interesting candidates as starter cultures for amaranth sourdoughs.
Verbesserung der Energie-, Stoff- und Emissionsbilanzen bei der Bioethanolproduktion aus nachwachsenden Rohstoffen
(2010) Fleischer, Sven; Senn, Thomas
In this thesis, a process was realized that uses starchy raw material (triticale) as well as lignocellulosic biomass (corn silage) in one ethanol production process. In contrast to other so called 2nd generation ethanol processes, which only use lignocellulosic material, the problem of the very low potential ethanol concentration (

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