Browsing by Person "Hitzmann, Bernd"

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Advancing 2D fluorescence online monitoring in microtiter plates by separating scattered light and fluorescence measurement, using a tunable emission monochromator
(2023) Berg, Christoph; Busch, Selma; Alawiyah, Muthia Dewi; Finger, Maurice; Ihling, Nina; Paquet-Durand, Olivier; Hitzmann, Bernd; Büchs, Jochen
Online fluorescence monitoring has become a key technology in modern bioprocess development, as it provides in‐depth process knowledge at comparably low costs. In particular, the technology is widely established for high‐throughput microbioreactor cultivation systems, due to its noninvasive character. For microtiter plates, previously also multi‐wavelength 2D fluorescence monitoring was developed. To overcome an observed limitation of fluorescence sensitivity, this study presents a modified spectroscopic setup, including a tunable emission monochromator. The new optical component enables the separation of the scattered and fluorescent light measurements, which allows for the adjustment of integration times of the charge‐coupled device detector. The resulting increased fluorescence sensitivity positively affected the performance of principal component analysis for spectral data of Escherichia coli batch cultivation experiments with varying sorbitol concentration supplementation. In direct comparison with spectral data recorded at short integration times, more biologically consistent signal dynamics were calculated. Furthermore, during partial least square regression for E. coli cultivation experiments with varying glucose concentrations, improved modeling performance was observed. Especially, for the growth‐uncoupled acetate concentration, a considerable improvement of the root‐mean‐square error from 0.25 to 0.17 g/L was achieved. In conclusion, the modified setup represents another important step in advancing 2D fluorescence monitoring in microtiter plates.
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.
Application of two-dimensional fluorescence spectroscopy for the on-line monitoring of teff-based substrate fermentation inoculated with certain probiotic bacteria
(2022) Alemneh, Sendeku Takele; Emire, Shimelis Admassu; Jekle, Mario; Paquet-Durand, Olivier; von Wrochem, Almut; Hitzmann, Bernd
There is increasing demand for cereal-based probiotic fermented beverages as an alternative to dairy-based products due to their limitations. However, analyzing and monitoring the fermentation process is usually time consuming, costly, and labor intensive. This research therefore aims to apply two-dimensional (2D)-fluorescence spectroscopy coupled with partial least-squares regression (PLSR) and artificial neural networks (ANN) for the on-line quantitative analysis of cell growth and concentrations of lactic acid and glucose during the fermentation of a teff-based substrate. This substrate was inoculated with mixed strains of Lactiplantibacillus plantarum A6 (LPA6) and Lacticaseibacillus rhamnosus GG (LCGG). The fermentation was performed under two different conditions: condition 1 (7 g/100 mL substrate inoculated with 6 log cfu/mL) and condition 2 (4 g/100 mL substrate inoculated with 6 log cfu/mL). For the prediction of LPA6 and LCGG cell growth, the relative root mean square error of prediction (pRMSEP) was measured between 2.5 and 4.5%. The highest pRMSEP (4.5%) was observed for the prediction of LPA6 cell growth under condition 2 using ANN, but the lowest pRMSEP (2.5%) was observed for the prediction of LCGG cell growth under condition 1 with ANN. A slightly more accurate prediction was found with ANN under condition 1. However, under condition 2, a superior prediction was observed with PLSR as compared to ANN. Moreover, for the prediction of lactic acid concentration, the observed values of pRMSEP were 7.6 and 7.7% using PLSR and ANN, respectively. The highest error rates of 13 and 14% were observed for the prediction of glucose concentration using PLSR and ANN, respectively. Most of the predicted values had a coefficient of determination (R2) of more than 0.85. In conclusion, a 2D-fluorescence spectroscopy combined with PLSR and ANN can be used to accurately monitor LPA6 and LCGG cell counts and lactic acid concentration in the fermentation process of a teff-based substrate. The prediction of glucose concentration, however, showed a rather high error rate.
Characterization of the effects of chia gels on wheat doughand bread rheology as well as the optimization of breadroll production with the Nelder-Mead simplex method
(2016) Zettel, Viktoria; Hitzmann, Bernd
Chia (Salvia hispanica L.) is becoming increasingly popular as ingredient for baked goods. The aim of the first part of this thesis was to investigate the influence of gel from ground chia on the rheology of different wheat dough systems and the resulting baked goods. The evaluated products were wheat bread and sweet pan bread. The effects of chia incorporated as gel in wheat bread dough as hydrocolloid were characterized using empirical and fundamental rheological methods and differential scanning calorimetry. To avoid competition of starch and ground chia with respect to the water uptake, chia was incorporated as gel. The gel was prepared of ground chia with 5 g/g and 10 g/g water, respectively. The doughs were prepared with 1-3 % chia related to the amount of wheat flour. The effects of gel from ground chia were studied also as fat replacer in sweet pan breads. The main focus of the work was to study the effects of the fat substitution on the dough rheology. The dough rheology was characterized using a rotational rheometer and a Rheofermentometer. The end products were evaluated with a texture analyser and two samples were additionally evaluated with respect to their fatty acid profile. The substitution was secondly addressed to reduce the total amount of fat in the product and to improve the nutritional value of the products regarding the fatty acid composition. The fat was replaced in four steps, and the ratio among the ingredients was held constant to ensure a better comparability. Within this thesis it was shown that addition of gel from ground chia can affect wheat doughs and the resulting baked products in a positive way. The approach of using ground chia as gel seems to be fruitful to avoid competition between starch and chia with respect to the water uptake while the crumb formation during the baking process takes place. The evaluation of the pasting profiles of wheat flour suspensions with chia gel addition reinforced this assumption. The gel from ground chia affected the pasting properties in a way that the viscosities decreased with increasing amount of chia. The rheological properties of the doughs were affected in negative ways with respect to further processing by the addition of too high amounts of chia gel. The dough stability was reduced and the resulting baked products were less and irregular porous and therefore compact. All doughs showed weakening regarding the rheometer measurements, however the linear viscoelastic region was not affected. The frequency sweep measurements showed for all doughs a decrease with increasing content of gel from ground chia. The creep-recovery tests of the sweet pan bread doughs revealed that the zero viscosity η0 decreased and the creep compliance J0 increased with increasing chia gel content. The weakening of the doughs may not absolutely be caused by the incorporated chia, but by the additional water. There seems to be a kind of interaction between ground chia particles, wheat flour constituents and water, because nearly the same results were achieved for 2 % and 1 % of ground chia with 5 g/g and 10 g/g water, respectively. These experiments lead also to the best results for incorporating gel from ground chia to wheat breads. The best results for sweet pan breads were obtained with 25 % fat replacement through gel from ground chia. This gel was prepared of 2.3 g ground chia with 5 g/g water. Summarizing the incorporation of defined amounts of gel from ground chia has a positive effect on the rheology and the resulting baked products. The retrogradation of the baked products was decreased over storage and the dietary fibre content was increased. Thus chia acts like a hydrocolloid. The nutritional values of the evaluated baked products, wheat bread and sweet pan bread, were increased. For the sweet pan breads an increase of omega-3 fatty acids was determined. The resulting best sweet pan bread exhibited an amount of 5 % linolenic acid. Gel from ground chia can therefore be incorporated into bakery products as hydrocolloid and for improving the nutritional values regarding the dietary fibre and omega-3 fatty acid contents. Another part of the work was the optimization of the production parameters, proofing time and baking temperature, for bread rolls. The optimization was performed with the Nelder-Mead simplex method. The optimization was necessary for a new oven type, where the oven walls were coated with a ceramic, that increased the infrared radiation during the baking process. The quality criterion for the optimization were the specific volume, the baking loss, the colour saturation, crumb firmness as well as the elasticity of the bread rolls. Within 11 experiments the optimal baking result defined by the results of a conventional oven was obtained. The optimal processing parameters for the bread rolls were a proofing time at 117 minutes and a baking temperature of 215 °C for 16 minutes.
Chemometric approach for profiling of metabolites of potential antioxidant activity in Apiaceae species based on LC-PDA-ESI-MS/MS and FT-NIR
(2023) Atta, Noha H.; Handoussa, Heba; Klaiber, Iris; Hitzmann, Bernd; Hanafi, Rasha S.
Chemometrics is a tool for data mining and unlocking the door for solving big data queries. Apiaceae is a family species which is commonly cultivated worldwide. Although members of this species are widely used as antioxidant, antibacterial, antifungal, and anti-inflammatory agents, their metabolites profiling remains ambiguous. Based on WHO support, chemometrics has been used in evaluating the quality and authenticity of the herbal products. The objective of this study is to profile and characterize phenolic metabolites in nine species from Egyptian cultivars and three different species of German cultivars from the Apiaceae family using multivariate analysis after LC-PDA-ESI-MS/MS and near infrared spectroscopy data are generated. Principal component analysis was successfully applied to distinguish between the nine Egyptian cultivars and the three German cultivars, and hierarchical cluster analysis also confirmed this distinctive clustering. Partial least square regression (PLS-R) models showed a relationship between phytochemicals and antioxidant activities. The metabolites responsible for the clustering pattern and variables important for projection (VIP) were identified, being twelve amongst nine Egyptian cultivar samples and thirteen amongst the Egyptian cultivar and the German cultivar comparison. The identified VIPs were also correlated with the antioxidant activity using PLS-R. In conclusion, the study showed novelty in the application of hyphenated analytical techniques and chemometrics that assist in quality control of herbal medicine.
Development of an on-line process monitoring for yeast cultivations via 2D-fluorescence spectroscopy
(2019) Assawajaruwan, Supasuda; Hitzmann, Bernd
An optimum process is required in the field of food, pharmaceutical and biotechnological industry with the ultimate goal of achieving high productivity and high-quality products. In order to achieve this goal, there are many different parameters to be realized and controlled, e.g., physical, chemical and biological aspects of microbial bioprocesses. Microbial cultivations are a very complex process, therefore, reliable and efficient tools are required to receive as much real-time information for an on-line monitoring as possible, so that the processes can be controlled in time. The primary objective of this research was to apply a two-dimensional (2D) fluorescence spectroscopy to monitor glucose, ethanol and biomass concentrations of yeast cultivations. The measurement of one spectrum has 120 fluorescence intensity variables of excitation and emission wavelength combinations (WLCs) without consideration of the scattered light. To investigate which WLCs carry important and relevant information regarding the analyte concentrations, the three wavelength selection methods were implemented: a method based on loadings, variable importance in projection (VIP) and ant colony optimization. The five selected WLCs from each method for a particular analyte were evaluated by multiple linear regression (MLR) models. The selected WLCs, which showed the best predictive performance of the MLR models, were relevant to the analyte concentrations. Regarding the results of the MLR models, the most significant WLCs contained seven different excitation and emission wavelengths. They can be combined to have 38 WLCs for one spectrum based on the principle of fluorescence. They were in the area of NADH, tryptophan, pyridoxine, riboflavin and FAD/FMN. The 38 WLCs were used to predict the glucose, ethanol and biomass concentrations via partial least squares (PLS) regression. The best prediction from the PLS models with 38 WLCs had the percentage of root mean square error of prediction (pRMSEP) in the range of 3.1-6.3 %, which was not significantly different from the PLS models with the 120 variables. Therefore, the specific fluorescence sensor for yeast cultivations could be built with less filters, which would make it a low-cost device. The following plan of the research goal was to investigate the attribute of fluorophores inside cells in real time using a 2D fluorescence spectrometer. The considered intracellular fluorophores, such as NADH, tryptophan, pyridoxine, riboflavin and FAD/FMN were observed during the yeast cultivations under three different conditions: batch, fed-batch with the glucose pulse during a glucose growth phase (GP) and fed-batch with the glucose pulse during an ethanol growth phase (EP) after a diauxic shift. With the help of principal component analysis, the different states of the yeast cultivations, particularly the glucose pulse during EP, can be recognized and identified from the on-line fluorescence spectra. On the other hand, the change of the fluorescence spectra in the fed-batch process with the glucose pulse during GP was not recognizable. Remarkably, the intensities of the fluorophores due to the glucose pulse during EP did not change in the same direction. The fluorescence intensities of NADH and riboflavin increased, but the intensity of tryptophan, pyridoxine and FAD/FMN decreased. The conversion between tryptophan and NADH intensities was quantified as a proportional factor. It was calculated from the ratio of the area of NADH and tryptophan fluorescence intensity after the glucose addition until depletion. The proportional factor was independent on various glucose concentrations with the coefficient of determination, R2 = 0.999. The correlative intensity changes of these fluorophores demonstrate a metabolic switch from ethanol to glucose growth phase. Based on the previous experiments, a closed-loop control has been implemented for yeast cultivations. 2D fluorescence spectroscopy was applied for an on-line monitoring and control of yeast cultivations to attain pure oxidative metabolism. A glucose concentration is an important factor in a fed-batch process of Saccharomyces cerevisiae. Therefore, it has to be controlled under a critical concentration to avoid overflow metabolism and to gain high productivity of biomass. The characteristic of the NADH intensity can effectively identify the metabolic switch between oxidative and oxidoreductive states. Consequently, the feed rates were regulated using the NADH intensity as a metabolic signal. With this closed-loop control of the glucose concentration, a biomass yield was obtained at 0.5 gbiomass/gglucose. Additionally, ethanol production could be avoided during the controlled feeding phase. The fluorescence sensor with the signal of the NADH intensity has potential to control a glucose concentration under the critical value in real time. The experiments carried out show that 2D fluorescence spectroscopy has great potential in on-line monitoring and process control of the yeast cultivations. Consequently, it is promising to build up a compact and economical fluorescence sensor with the specific wavelengths using light-emitting diodes and photodiodes. The sensor would be a cost-effective and miniaturized device for routine analysis, which could be advantageous to real-time bioprocess monitoring.
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.
Development of software sensors for on-line monitoring of baker’s yeast fermentation process
(2021) Yousefi-Darani, Abdolrahim; Hitzmann, Bernd
Software sensors and bioprocess are well-established research areas which have much to offer each other. Under the perspective of the software sensors area, bioprocess can be considered as a broad application area with a growing number of complex and challenging tasks to be dealt with, whose solutions can contribute to achieving high productivity and high-quality products. Although throughout the past years in the field of software sensors and bioprocess, progress has been quick and with a high degree of success, there is still a lack of inexpensive and reliable sensors for on-line state and parameter estimation. Therefore, the primary objective of this research was to design an inexpensive measurement system for on-line monitoring of ethanol production during the backer’s yeast cultivation process. The measurement system is based on commercially available metal oxide semiconductor gas sensors. From the bioreactor headspace, samples are pumped past the gas sensors array for 10 s every five minutes and the voltage changes of the sensors are measured. The signals from the gas sensor array showed a high correlation with ethanol concentration during cultivation process. In order to predict ethanol concentrations from the data of the gas sensor array, a principal component regression (PCR) model was developed. For the calibration procedure no off-line sampling was used. Instead, a theoretical model of the process is applied to simulate the ethanol production at any given time. The simulated ethanol concentrations were used as reference data for calibrating the response of the gas sensor array. The obtained results indicate that the model-based calibrated gas sensor array is able to predict ethanol concentrations during the cultivation process with a high accuracy (root mean square error of calibration as well as the percentage error for the validation sets were below 0.2 gL-1 and 7 %, respectively). However the predicted values are only available every five minutes. Therefore, the following plan of the research goal was to implement an estimation method for continues prediction of ethanol as well as glucose, biomass and the growth rates. For this reason, two nonlinear extensions of the Kalman filter namely the extended Kalman filter (EKF) and the unscented Kalman filter (UKF) were implemented separately for state and parameter estimation. Both prediction methods were validated on three different cultivation with variability of the substrate concentrations. The obtained results showed that both estimation algorithms show satisfactory results with respect to estimation of concentrations of substrates 6 and biomass as well as the growth rate parameters during the cultivation. However, despite the easier implementation producer of the UKF, this method shows more accurate prediction results compared to the EKF prediction method. Another focus of this study was to design and implement an on-line monitoring and control system for the volume evaluation of dough pieces during the proofing process of bread making. For this reason, a software sensor based on image processing was designed and implemented for measuring the dough volume. The control system consists of a fuzzy logic controller which takes into account the estimated volume. The controller is designed to maintain the volume of the dough pieces similar to the volume expansion of a dough piece in standard conditions during the proofing process by manipulating the temperature of the proofing chamber. Dough pieces with different amounts of backer’s yeast added in the ingredients and in different temperature starting states were prepared and proofed with the supervision of the software sensor and the fuzzy controller. The controller was evaluated by means of performance criteria and the final volume of the dough samples. The obtained results indicate that the performance of the system is very satisfactory with respect to volume control and set point deviation of the dough pieces.
Drought stress during anthesis alters grain protein composition and improves bread quality in field-grown Iranian and German wheat genotypes
(2021) Rekowski, Azin; Wimmer, Monika A.; Tahmasebi, Sirous; Dier, Markus; Kalmbach, Sarah; Hitzmann, Bernd; Zörb, Christian
Drought stress is playing an increasingly important role in crop production due to climate change. To investigate the effects of drought stress on protein quantity and quality of wheat, two Iranian (Alvand, Mihan) and four German (Impression, Discus, Rumor, Hybery) winter wheat genotypes, representing different quality classes and grain protein levels, were grown under field conditions in Eqlid (Iran) during the 2018–2019 growing season. Drought stress was initiated by interrupting field irrigation during the anthesis phase at two different stress levels. Drought stress at anthesis did not significantly change total grain protein concentration in any of the wheat genotypes. Similarly, concentrations of grain storage protein sub-fractions of albumin/globulin, gliadin and glutenin were unaltered in five of the six genotypes. However, analysis of protein sub-fractions by SDS polyacrylamide gel electrophoresis revealed a consistent significant increase in ω-gliadins with increasing drought stress. Higher levels of HMW glutenins and a reduction in LMW-C glutenins were observed exclusively under severe drought stress in German genotypes. The drought-induced compositional change correlated positively with the specific bread volume, and was mainly associated with an increase in ω-gliadins and with a slight increase in HMW glutenins. Despite the generally lower HMW glutenin concentrations of the Iranian genotypes and no effect of drought on the concentration of HMW sub-fraction, there was still high specific bread volume under drought. It is suggested that for the development of new wheat cultivars adapted to these challenging climatic conditions, the protein composition should be considered in addition to the yield and grain protein concentration.
Effect of refrigerated storage on some physicochemical characteristics of a teff‐based fermented beverage and the viability of the fermenting Lactiplantibacillus plantarum and Lacticaseibacillus rhamnosus used
(2022) Alemneh, Sendeku Takele; Emire, Shimelis Admassu; Jekle, Mario; Hitzmann, Bernd
Probiotic beverages made from cereals become interesting in the recent food industries. In this contribution, a fermented teff-based probiotic beverage was produced using the whole grain teff flour and co-culture strains of Lactiplantibacillus plantarum (LPA6) and Lacticaseibacillus rhamnosus GG (LCGG). Then, the effect of 25 days of refrigerated storage on cell viability (LPA6 and LCGG), and contents of sugars, organic acids, and titratable acidity (TA), as well as pH values were examined. Furthermore, pathogenic microorganisms, hygiene indicators, and sensory tests of the beverage were analyzed. Presumptive cell counts of LPA6 and LCGG were observed to decrease throughout refrigerated storage. Glucose, lactic acid, maltose, and acetic acid contents were significantly (p < 0.05) increased over storage time. Also, pH reduction and TA increment were observed in storage time. Examined pathogenic microorganisms and hygiene indicators were not detected in the beverage. Sensory analysis of the beverage after 10 days of refrigerated storage was accepted by the panelists. Novelty Impact Statement Throughout refrigerated storage of teff-based probiotic beverage sugars and organic acids were produced. Sensory attributes of the newly produced teff-based probiotic beverage were accepted by the panelist after 10 days of refrigerated storage. The pH of the teff-based probiotic beverage became more acidic throughout 25 days of refrigerated storage.
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.
Fluorescence spectroscopy and chemometrics : an innovative approach for characterization of wheat flour and dough preparation
(2016) Ahmad, Muhammad Haseeb; Hitzmann, Bernd
Implementation of process analytical technologies (PAT) in food applications has attained a remarkable motivation due to higher quality and safety standards in this field. PAT applications also include rapid and non-invasive approaches which can be obtained from spectroscopic techniques. Fluorescence spectroscopy together with chemometrics is considered to be an outstanding analytical tool for fast and non-invasive technique for food analysis which can be used in various food applications on industrial scale. It is known for its sensitivity and specificity which can analyze the different foods and its ingredients while chemometrics helps to extract the useful information from the spectral data. The different chemometrics tools used for quantitative and qualitative analysis of spectral data, has increased the importance of this spectroscopic technique in generating the new ideas and hypothesis to develop new analytical methods which lead towards betterment in industrial operations for process and quality monitoring. In this doctoral project, fluorescence spectroscopic together with chemometric has been utilized to develop some new methods for determination of different parameters of wheat which provides the central idea of the thesis. First manuscript presents the potential of fluorescence spectroscopy to predict the analytical, rheological and baking parameters of different wheat flours by just taking the spectral signature without any sample preparation. Twelve different wheat flours milled from wheat cultivars were used to analyze the analytical, rheological and baking parameters using the conventional methods. These measured parameters were predicted from the spectral data taken for different wheat flours using genetic algorithm coupled with partial least square regression. The model obtained for protein, wet gluten and sedimentation value showing high R2 = 0.90, 0.92 and 0.81 respectively. Similarly, the rheological parameters like dough development time and water absorption were also predicted with low root mean square error of cross validation (RMSECV) and high R2 = 0.95 and 0.77 respectively while pasting temperature showed R2 = 0.78. Furthermore, moisture and volume of bread were predicted with high accuracy showing R2 = 0.86 and 0.95 respectively in the baking parameters. Other rheological and baking parameters like dough stability, softening, farinograph quality number, baking loss, crumb hardness and springiness were not predicted well due to poor correlation and high error. In the second paper, characterization of complex farinographic kneading process is performed by using the fluorescence spectroscopy in combination with chemometric tools. The aim of this investigation is to determine the impact of hydration of flour onto the spectral signals, classification of farinographic curve and separation of wheat flours based on their bread making performance. Secondly the middle curve of farinograph was predicted out of the fluorescence spectra using partial least square regression (PLSR) which can help to predict optimal dough development time. The spectra of the flour showed high intensities in protein, NADH and riboflavin regions which reduce to 36 %, 58 % and 61 % respectively after the hydration process depicting its influence due to structural changes in protein and oxidation of NADH. The farinographic curve was divided into four phases and principal component analysis (PCA) has been used to extract the qualitative information regarding the farinographic curve from the fluorescence spectra to categorize all farinographic phases into hydration, dough development, and stability and softening. Similarly, different pre-processing tools like standard normal variate and generalized least square weighting generate good separation of various wheat flours during the farinographic kneading process into different quality groups (E, A, B and C) on the basis of their bread baking performance from the spectral data using PCA. Additionally, PLSR was applied to predict the middle curve of farinograph out of spectral data showing a descent coefficient of determination R2 = 0.75 with RMSECV of 14 Brabender units. However, more research can lead towards the development of a sensor for determination of optimal dough development time. In another study, the nutritional parameters of 26 different types of wheat flour obtained from different vendors from the supermarket were predicted using fluorescence coupled with linear and non-linear chemometric tools. PCA applied on the spectral data for different types of the wheat flours showing a clear separation. On the other hand, PLSR was used to quantify the nutritional parameters of different types of wheat flours showing a good prediction for fat, moisture and carbohydrates using cross-validation, with a R2 of 0.88, 0.86 and 0.89, respectively whereas the protein, sucrose and salt contents presented a little correlation in PLSR. Therefore, locally weighted regression, a non-linear chemometric tool improves the prediction ability of all of the nutritional parameters by decreasing the error with an increasing R2. The energetic value, protein, fat, carbohydrate, moisture, sucrose, salt and saturated fatty acid contents showed R2 of 0.96, 0.93, 0.99, 0.99, 0.98, 0.88, 0.95, and 0.99 respectively, for different wheat flours. The aforementioned results clearly demonstrate the potential of the fluorescence spectroscopy in determination of analytical, rheological, baking and nutritional parameters of the wheat flours. They present that it can be used to characterize and categorize the farinographic kneading process, which is important in the bread-baking industry. More research in this direction can result in developing a sensor for predicting the quality parameters and processing operations in the cereal based industries rapidly and non-invasively which are important for regulatory and screening of the wheat on quality characteristics for marketing and end product evaluations.
Generic chemometric models for metabolite concentration prediction based on Raman spectra
(2022) Yousefi-Darani, Abdolrahim; Paquet-Durand, Olivier; von Wrochem, Almut; Classen, Jens; Tränkle, Jens; Mertens, Mario; Snelders, Jeroen; Chotteau, Veronique; Mäkinen, Meeri; Handl, Alina; Kadisch, Marvin; Lang, Dietmar; Dumas, Patrick; Hitzmann, Bernd
Chemometric models for on-line process monitoring have become well established in pharmaceutical bioprocesses. The main drawback is the required calibration effort and the inflexibility regarding system or process changes. So, a recalibration is necessary whenever the process or the setup changes even slightly. With a large and diverse Raman dataset, however, it was possible to generate generic partial least squares regression models to reliably predict the concentrations of important metabolic compounds, such as glucose-, lactate-, and glutamine-indifferent CHO cell cultivations. The data for calibration were collected from various cell cultures from different sites in different companies using different Raman spectrophotometers. In testing, the developed “generic” models were capable of predicting the concentrations of said compounds from a dilution series in FMX-8 mod medium, as well as from an independent CHO cell culture. These spectra were taken with a completely different setup and with different Raman spectrometers, demonstrating the model flexibility. The prediction errors for the tests were mostly in an acceptable range (<10% relative error). This demonstrates that, under the right circumstances and by choosing the calibration data carefully, it is possible to create generic and reliable chemometric models that are transferrable from one process to another without recalibration.
Improved methods in optimal design of experiments for determination of water absorption kinetics of cereal grains
(2016) Paquet-Durand, Olivier; Hitzmann, Bernd
In this thesis, the optimal design of experiments was applied to determine hydration kinetics of wheat grains. In the first study the used mathematical model was the Peleg model for which the optimal design of experiments was carried out while investigating how the optimization criterion will influence the result. The parameter estimation errors could be reduced by up to 62% compared to a non-optimal equidistant experimental design. It has been shown that the individual parameter estimation errors vary significantly depending on the used criterion. In this application only the D-optimal experimental design can reduce the parameter estimation errors of both parameters. In case of the A, Pr and E criterion at least one of the two parameter error could be reduced significantly. As the numerical optimization is computationally demanding, an alternative method for the entire optimal experimental design was developed. This alternative method is based on a mathematical function which depends on the rough initial parameter values. This function allows optimal measuring points to be calculated directly and therefore much faster, than the usual optimal design approach using numerical optimization techniques. In case of the very commonly used D-optimality criterion, the derived function is the exact solution. The deviation of the parameter estimation errors acquired by using the approximate optimal design instead of a normal optimal design are mostly around 0.01 % and therefore negligible. In the second study, the suitability of the Peleg model for water absorption kinetics of wheat grains was investigated closer. Cereal grains usually consist of three major components, bran layer, endosperm and germ. All these components have different water absorption kinetics. Therefore, the normal two parameter Peleg model might be insufficient to describe the water absorption process of cereal grains properly. To address this, the Peleg model was enhanced and a second Peleg like term was added to account for the two biggest fractions of the grain, namely the endosperm and the bran layer. Two experiments were carried out, an initial experiment to get rough parameter values and a second experiment, which was then optimally designed. The modified Peleg model had now four parameters and could be used to describe the hydration process of wheat grains much more accurate. Using the parameters calculated from the initial experiment the optimal measurement points where calculated in a way that the determination of the parameters of the modified Peleg model was as accurate as possible. The percentage parameter errors for the four parameters in the initial experiment were 669%, 24%, 12%, and 2.4%. By applying the optimal design, they were reduced to 38% 5.4%, 4.5% and 1.9% respectively. The modified Peleg model resulted in a very low root mean square error of prediction of 0.45% where the normal Peleg model results in a prediction error of about 3%. In the third study, it was investigated if bootstrapping could be used as a feasible alternative method for optimal experimental design. The classical procedure to determine parameter estimation errors is based on the Cramér-Rao lower bound but bootstrapping or re-sampling can also be used for the estimation of parameter variances. The newly developed method is more computationally demanding compared to the Cramér-Rao lower bound approach. However, bootstrapping is not bound to any restrictive assumptions about the measurement and parameter variations. An optimal experimental design based on the bootstrap method was calculated to determine optimal measurement times for the parameter estimation of the Peleg model. The Cramér-Rao based optimal design results were used as a benchmark. It was shown, that a bootstrap based optimal design of experiments yields similar optimal measurement points and therefore comparable results to the Cramér-Rao lower bound optimal design. The parameter estimation errors obtained from both optimal experimental design methods deviate on average by 1.5%. It has also been shown, that the probability densities of the parameters are asymmetric and not at all normal distributions. Due to this asymmetry, the estimated parameter errors acquired by bootstrapping are in fact likely to be more accurate. So bootstrapping can in fact be used in an optimal design context. However, this comes at the cost of a high computational effort. The computation time for a bootstrap based optimal design was around 25 minutes compared to only 5 seconds when using the Cramér-Rao lower bound method. But compared to the time required to carry out the experiments this is neglectable. Furthermore, as computers get faster and faster over time, the computational demand will become less relevant in future.
Novel method for the detection of adulterants in coffee and the determination of a coffee's geographical origin using near infrared spectroscopy complemented by an autoencoder
(2023) Munyendo, Leah; Njoroge, Daniel; Zhang, Yanyan; Hitzmann, Bernd
Coffee authenticity is a foundational aspect of quality when considering coffee's market value. This has become important given frequent adulteration and mislabelling for economic gains. Therefore, this research aimed to investigate the ability of a deep autoencoder neural network to detect adulterants in roasted coffee and to determine a coffee's geographical origin (roasted) 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. First, the autoencoder was trained using pure arabica coffee before being used to detect the presence of adulterants in the samples. Furthermore, it was used to determine the geographical origin of coffee. All samples adulterated with chicory were detectable by the autoencoder at all roast levels. In the case of robusta‐adulterated samples, detection was possible at adulteration levels above 7.5% at medium and dark roasts. Additionally, it was possible to differentiate coffee samples from different geographical origins. PCA analysis of adulterated samples showed grouping based on the type and concentration of the adulterant. In conclusion, using an autoencoder neural network in conjunction with NIR spectroscopy could be a reliable technique to ensure coffee authenticity.
Online monitoring of sourdough fermentation using a gas sensor array with multivariate data analysis
(2023) Anker, Marvin; Yousefi-Darani, Abdolrahim; Zettel, Viktoria; Paquet-Durand, Olivier; Hitzmann, Bernd; Krupitzer, Christian
Sourdough can improve bakery products’ shelf life, sensory properties, and nutrient composition. To ensure high-quality sourdough, the fermentation has to be monitored. The characteristic process variables for sourdough fermentation are pH and the degree of acidity measured as total titratable acidity (TTA). The time- and cost-intensive offline measurement of process variables can be improved by utilizing online gas measurements in prediction models. Therefore, a gas sensor array (GSA) system was used to monitor the fermentation process of sourdough online by correlation of exhaust gas data with offline measurement values of the process variables. Three methods were tested to utilize the extracted features from GSA to create the models. The most robust prediction models were achieved using a PCA (Principal Component Analysis) on all features and combined two fermentations. The calibrations with the extracted features had a percentage root mean square error (RMSE) from 1.4% to 12% for the pH and from 2.7% to 9.3% for the TTA. The coefficient of determination (R2) for these calibrations was 0.94 to 0.998 for the pH and 0.947 to 0.994 for the TTA. The obtained results indicate that the online measurement of exhaust gas from sourdough fermentations with gas sensor arrays can be a cheap and efficient application to predict pH and TTA.
Online process state estimation for Hansenula polymorpha cultivation with 2D fluorescence spectra-based chemometric model calibrated from a theoretical model in place of offline measurements
(2023) Babor, Majharulislam; Paquet-Durand, Olivier; Berg, Christoph; Büchs, Jochen; Hitzmann, Bernd
The use of 2D fluorescence spectra is a powerful, instantaneous, and highly accurate method to estimate the state of bioprocesses. The conventional approach for calibrating a chemometric model from raw spectra needs a large number of offline measurements from numerous runs, which is tedious, time-consuming, and error-prone. In addition, many process variables lack direct signal responses, which forces chemometric models to make predictions based on indirect responses. In order to predict glycerol and biomass concentrations online in batch cultivation of Hansenula polymorpha, this study substituted offline measurements with simulated values. The only data from cultivations needed to generate the chemometric model were the 2D fluorescence spectra, with the presumption that they contain sufficient information to characterize the process state at a measurement point. The remainder of the evaluation was carried out with the aid of a mathematical process model that describes the theoretical interferences between process variables in the system. It is shown that the process model parameters, including microbial growth rate, the yield of biomass from glycerol, and lag time can be determined from only the spectra by employing a model-based calibration (MBC) approach. The prediction errors for glycerol and biomass concentrations were 8.6% and 5.7%, respectively. An improved model-based calibration (IMBC) approach is presented that calibrates a chemometric model for only biomass. Biomass was predicted from a 2D fluorescence spectrum in new cultivations, and glycerol concentration was estimated from the process model utilizing predicted biomass as an input. By using this method, the prediction errors for glycerol and biomass were reduced to 5.2% and 4.7%, respectively. The findings indicate that model-based calibration, which can be carried out with only 2D fluorescence spectra gathered from prior runs, is an effective method for estimating the process state online.
Optimization of no-wait flowshop scheduling problem in bakery production with modified PSO, NEH and SA
(2021) Babor, Majharulislam; Senge, Julia; Rosell, Cristina M.; Rodrigo, Dolores; Hitzmann, Bernd
In bakery production, to perform a processing task there might be multiple alternative machines that have the same functionalities. Finding an efficient production schedule is challenging due to the significant nondeterministic polynomial time (NP)-hardness of the problem when the number of products, processing tasks, and alternative machines are higher. In addition, many tasks are performed manually as small and medium-size bakeries are not fully automated. Therefore, along with machines, the integration of employees in production planning is essential. This paper presents a hybrid no-wait flowshop scheduling model (NWFSSM) comprising the constraints of common practice in bakeries. The schedule of an existing production line is simulated to examine the model and is optimized by performing particle swarm optimization (PSO), modified particle swarm optimization (MPSO), simulated annealing (SA), and Nawaz-Enscore-Ham (NEH) algorithms. The computational results reveal that the performance of PSO is significantly influenced by the weight distribution of exploration and exploitation in a run time. Due to the modification to the acceleration parameter, MPSO outperforms PSO, SA, and NEH in respect to effectively finding an optimized schedule. The best solution to the real case problem obtained by MPSO shows a reduction of the total idle time (TIDT) of the machines by 12% and makespan by 30%. The result of the optimized schedule indicates that for small- and medium-sized bakery industries, the application of the hybrid NWFSSM along with nature-inspired optimization algorithms can be a powerful tool to make the production system efficient.
Process analytical technology in food biotechnology
(2017) Stanke, Marc; Hitzmann, Bernd
Biotechnology is an area where precision and reproducibility are vital. This is due to the fact that products are often in form of food, pharmaceutical or cosmetic products and therefore very close to the human being. To avoid human error during the production or the evaluation of the quality of a product and to increase the optimal utilization of raw materials, a very high amount of automation is desired. Tools in the food and chemical industry that aim to reach this degree of higher automation are summarized in an initiative called Process Analytical Technology (PAT). Within the scope of the PAT, is to provide new measurement technologies for the purpose of closed loop control in biotechnological processes. These processes are the most demanding processes in regards of control issues due to their very often biological rate-determining component. Most important for an automation attempt is deep process knowledge, which can only be achieved via appropriate measurements. These measurements can either be carried out directly, measuring a crucial physical value, or if not accessible either due to the lack of technology or a complicated sample state, via a soft-sensor.Even after several years the ideal aim of the PAT initiative is not fully implemented in the industry and in many production processes. On the one hand a lot effort still needs to be put into the development of more general algorithms which are more easy to implement and especially more reliable. On the other hand, not all the available advances in this field are employed yet. The potential users seem to stick to approved methods and show certain reservations towards new technologies.
Production of CO₂ gas hydrates with its application in wheat bread making process
(2023) Srivastava, Shubhangi; Hitzmann, Bernd
The basic requirements necessary for gas hydrate (GH) formation are low temperature, high pressure, the presence of guest molecules, and the desired amounts of water molecules. The most common guest molecules used for the GH are ethane, methane, butane, propane, nitrogen, and carbon dioxide. Hydrate based technological applications almost always require rapid hydrate formation along with high gas uptake to be economically viable. One possible approach to achieving the same is the introduction of particular additives into the system. These additives are known as hydrate promoters. In recent times, amino acids have emerged as a highly effective class of promoters, and unlike surfactants, they promise a clean mode of kinetic action, i.e., no foam formation. Hence, the first part of the thesis dealt with the optimisation of GH formation with the application of amino acid promoters. The optimisation of the GH production was performed with different combinations of promoter ingredients namely cysteine, valine, leucine, and methionine. The amino acids leucine and methionine gave some positive results with the application of promoters for the production of GH therefore, these two amino acids were carried further for the experimentation purpose in the production of GH. Also, a combinational use of these amino acids (leucine and methionine) was studied to investigate the effect on percentage CO₂ retention in comparison to the normal water GH. The conventional baker’s yeast, Saccharomyces cerevisiae, remains the popular leavening agent in the bread baking industry. Carbon dioxide required for the rising of dough is produced by the metabolism of yeast with the consumption of sugars in the dough, which is a time and energy-consuming process. This research attempts to utilize carbon dioxide gas hydrate as a leavening agent in bread. Despite plentiful experiments on CO₂ gas hydrates in other fields, there is still an urge to carry out more analysis to elucidate various applications of GH in baking and positively validate its sustainability. The temperature stability of GH is important while baking due to the exposure to high temperatures during the various steps involved. In order to effectively use CO₂ GH as a leavening agent in the baking industry, a concise evaluation of the formation of CO₂ GH and its gas containment capacity should be adequately analysed and documented. Also, the effect of CO₂ GH properties by the addition of promoters should be taken into consideration as baking involves higher temperatures, and stabilising the GH at higher temperatures is an important criterion in the context of baking different products. Hence, the effect of a higher temperature of 90 ℃; on the CO₂ gas entrapment of the produced GH with promoters was studied. It was observed that the stability of GH decreases with an increase in temperature, but the addition of promoters, especially leucine + methionine + lecithin increased the CO₂ uptake during GH formation. Another part of the thesis was the application of GH in the baking bread with/without promoters and the study of physio chemical properties of bread. By varying the percentage of gas hydrates from 10-60 %, analysis of the performance of CO₂ GH as a leavening agent during baking was done. The effectiveness of GH bread was evaluated by comparing its characteristics to those of standard bread made with yeast. Also, a comparative evaluation was made for bread with and without promoters GH as leavening agents in terms of different physio chemical characteristics of the bread, such as moisture analysis, volume analysis, pore analysis, texture profile analysis, and baking loss. The results show that the bread with 20 % and 40 % GH obtained the best results in terms of volume and pore size. The next part of the thesis dealt with a comparative analysis of the partial replacement of yeast with CO₂ GH as leavening agents in bread baking. By partially eliminating the yeast, variations of bread dough were produced by the addition of GH in different percentages (20-70 %). The effectiveness of GH on bread manufacture was evaluated by comparing its characteristics to that of standard bread made with yeast. Once the bread was baked, the texture profile, volume, moisture content, and pore size were recorded to compare the leavening effect of GH with the standard recipe when partial addition of yeast was done. The best results combinations with respect to specific volume, pore analysis and hardness were obtained with 70 % GH + 50 % yeast and 70 % GH + 75 % yeast, respectively. As the final part of the thesis, the influence of additives on wheat bread baked with promoter induced CO₂ GH as leavening agents was studied. The additives used for the study were ascorbic acid (AC), egg white (EW), and rice flour (RF). These additives were added to the GH bread containing different amounts of GH (40, 60, and 70 % GH). Also, a combination of these additives in a wheat GH bread recipe was studied for each respective percentage of GH. Based on the results of the study, it was found that 70 % GH+ AC+EW+RF wheat bread was found to be the best in terms of textural analysis, pore size analysis, and other physiochemical parameters. Therefore, this research study will help us in understanding the application of GH in the bread baking process with replacement of conventional baking agents such as yeast.