Thomas Becker’s research while affiliated with Technische Universität München and other places

3D printing of starch-based materials has become of great interest during the last few years. However, the characterization of the printing inks and the prediction of the printing accuracy is still challenging. Therefore, the surface chemistry and particle size distribution of starches from different organic sources (wheat, potato, rice) were characterized, and their influence on printing accuracy was investigated. Starch granules surface is covered with different lipids (e.g. phospholipids) and protein (e.g. puroinduline), which are known to influence the properties of starch and the interaction with other ingredients. These surface-associated lipids (SSAL) and proteins (SSAP) were removed individually from starch granules’ surfaces to investigate the influence of particle-particle interplay on the printing behavior. Therefore, the amount of surface proteins was calculated by XPS analysis based on the nitrogen to carbon (N/C) ratio of each starch granules’ surface. There was a linear cor­relation (r = −0.84) between the N/C ratio and the printing accuracy, measured by a geometrical deviation, indicating a dominating influence of the surface composition of the individual starch granules. The deviation from the geometrical template was higher for printed samples with smaller N/C ratio and therefore less protein on the starch granules’ surface. No influence of the particle size was found, as the samples from different starches containing the same amount of SSAPs had the same printing accuracy. These results reveal that the particle-particle and particle-polymer interactions mainly influence by the protein content on the starch granule sur­ face seem to be decisive for the geometrical stability of 3D food printing. It is therefore recommended to use starches with a high amount of SSAPs for 3D printing applications.

Obligate anaerobic beer spoilage bacteria have been a menace to the brewing industry for several decades. Technological advances in the brewing process aimed at suppressing aerobic spoilers gave rise to problems with obligate anaerobes. In previous studies, the metabolic spectrum of Pectinatus and Megasphaera species has been described, but their metabolism in the beer environment remains largely unknown. We used high-performance anion exchange chromatography with pulsed amperometric detection (HPAEC-PAD) and headspace solid-phase microextraction–gas chromatography–mass spectrometry (HS-SPME-GCMS) to further characterize beer spoiled by 30 different strains from six beer-spoiling species of Pectinatus and Megasphaera (P. cerevisiiphilus, P. frisingensis, P. haikarae, M. cerevisiae, M. paucivorans, and M. sueciensis). We detected differences in carbohydrate utilization and the volatile organic compounds (volatilome) produced during beer spoilage by all six species. We were able to show that glycerol, one of the basic components of beer, is the common carbon source used by all strains. It appears that this carbon source allows for anaerobic beer spoilage by Pectinatus and Megasphaera despite the spoilage-preventing intrinsic barriers of beer (iso-α-acids, ethanol, low pH, scarce nutrients); thus, extrinsic countermeasures are key for prevention.

In fermentation processes, changes in yeast cell count and substrate concentration are indicators of yeast performance. Therefore, monitoring the composition of the biological suspension, particularly the dispersed solid phase (i.e., yeast cells) and the continuous liquid phase (i.e., medium), is a prerequisite to ensure favorable process conditions. However, the available monitoring methods are often invasive or restricted by detection limits, sampling requirements, or susceptibility to masking effects from interfering signals. In contrast, ultrasound measurements are non-invasive and provide real-time data. In this study, the suitability to characterize the dispersed and the liquid phase of yeast suspensions with ultrasound was investigated. The ultrasound signals collected from three commercially available Saccharomyces yeast were evaluated and compared. For all three yeasts, the attenuation coefficient and speed of sound increased linearly with increasing yeast concentrations (0.0–1.0 wt%) and cell counts (R2 > 0.95). Further characterization of the dispersed phase revealed that cell diameter and volume density influence the attenuation of the ultrasound signal, whereas changes in the speed of sound were partially attributed to compositional variations in the liquid phase. This demonstrates the ability of ultrasound to monitor industrial fermentations and the feasibility of developing targeted control strategies.

Background The market for beverages is highly changing within the last years. Increasing consumer awareness towards healthier drinks led to the revival of traditional and the creation of innovative beverages. Various protein-rich legumes were used for milk analogues, which might be also valuable raw materials for refreshing, protein-rich beverages. However, no such applications have been marketed so far, which might be due to unpleasant organoleptic impressions like the legume-typical “beany” aroma. Lactic acid fermentation has already been proven to be a remedy to overcome this hindrance in consumer acceptance. Results In this study, a statistically based approach was used to elucidate the impact of the fermentation parameters temperature, inoculum cell concentration, and methionine addition on the fermentation of lupine- and faba bean-based substrates. A total of 39 models were found and verified. The majority of these models indicate a strong impact of the temperature on the reduction of aldehydes connected to the “beany” impression (e.g., hexanal) and on the production of pleasantly perceived aroma compounds (e.g., β-damascenone). Positively, the addition of methionine had only minor impacts on the negatively associated sulfuric compounds methional, dimethyl sulfide, dimethyl disulfide, and dimethyl trisulfide. Moreover, in further fermentations, the time was added as an additional parameter. It was shown that the strains grew well, strongly acidified the both substrates (pH ≤ 4.0) within 6.5 h, and reached cell counts of > 9 log10 CFU/mL after 24 h. Notably, most of the aldehydes (like hexanal) were reduced within the first 6–7 h, whereas pleasant compounds like β-damascenone reached high concentrations especially in the later fermentation (approx. 24–48 h). Conclusions Out of the fermentation parameters temperature, inoculum cell concentration, and methionine addition, the temperature had the highest influence on the observed aroma and taste active compounds. As the addition of methionine to compensate for the legume-typical deficit did not lead to an adverse effect, fortifying legume-based substrates with methionine should be considered to improve the bioavailability of the legume protein. Aldehydes, which are associated with the “beany” aroma impression, can be removed efficiently in fermentation. However, terminating the process prematurely would lead to an incomplete production of pleasant aroma compounds.

During the primary fermentation of beer, the yeast simultaneously carries out a series of processes such as cell growth, pH shift, and the formation and degradation of essential flavour components. Harvested yeast from a previous fermentation can be used to inoculate the fermentation or fresh cells can be produced through aerobic propagation. This study investigated the influence of different aeration conditions during Saccharomyces pastorianus ssp. carlsbergensis propagation on the cell count development and the production of secondary metabolites during the subsequent primary beer fermentation. Propagations were conducted by applying six different dissolved oxygen concentrations, and the cells were used as inoculum for the subsequent fermentation. Cell count, pH shift, and the development of key aroma compounds were monitored throughout the primary fermentation to evaluate any difference between the conducted fermentations. The outcomes revealed significant distinctions between fermentations using yeast propagated under elevated oxygen levels and those propagated under reduced oxygen levels. Cells propagated using lower oxygen concentrations showed earlier cell growth with 40% lower final cell counts, resulting in 50% reduced biomass yields. Additionally, lower oxygen concentrations during propagation led to lower pH shifts during primary fermentation with 20% more higher alcohols and elevated formation of acetaldehyde, and esters.

Lupines and faba beans are rising stars among legumes as sources of valuable, vegan plant‐based proteins. To enter new application areas like the production of protein‐rich refreshing beverages, the typical beany aroma impression has to be overcome, and the sensory appearance has to be improved, as it can be accomplished with lactic acid fermentation. An extensive strain screening of 70 lactic acid bacteria from 16 genera was performed to identify suitable strains to transform substrates made from lupines and faba beans into refreshing beverages and to improve their sensory characteristics. By analyzing carbohydrate utilization, production of organic acids and aroma compounds, and sensory appearance, 22 strains for lupine and eight strains for faba bean were preselected. Subsequently, the most suitable strains (five for lupine and three for faba beans) were identified by a trained sensory panel, and finally their growth kinetics were discussed. Generally, the aroma profile varied highly with the utilized strain. However, by selecting suitable strains, the beany impression can be highly reduced and pleasant aroma impressions (e.g., fruity and buttermilk) can be added. Most strikingly, it was proven that using germinated lupines and faba beans instead of raw ones can bypass the usual growth restriction, and the strain selection can be focused exclusively on sensory aspects. This opens the option to use strains usually excluded for the fermentation of legumes due to their lack of utilization of the legume‐typical α‐galactosides.

Innovative soft sensor concepts can recalibrate automatically when the prediction performance decreases due to variations in raw materials, biological variability, and changes in process strategies. For automatic recalibration, data sets are selected from a data pool based on distance-based similarity criteria and then used for calibration. Nevertheless, the most appropriate data sets often are not reliably selected due to variances in the location of landmarks and process length of the bioprocesses. This can be overcome by synchronization methods that align the historical data sets with the current process and increase the accuracy of automatic selection and recalibration. This study investigated two different synchronization methods (dynamic time warping and curve registration) as preprocessing for the automatic selection of data sets using a distance-based similarity criterion for soft sensor recalibration. The prediction performance of the two soft sensors without synchronization was compared to the variants with synchronization and evaluated by comparing the normalized root mean squared errors. Curve registration improved the prediction performance on average by 24% ( Pichia pastoris ) and 9% ( Bacillus subtilis ). Using dynamic time warping, no substantial improvement in prediction performance could be achieved. A major factor behind this was the loss of information due to singularities caused by the changing process characteristics. The evaluation was performed on two target variables of real bioprocesses: biomass concentration prediction in P. pastoris and product concentration prediction in B. subtilis .

An ultrasonic-based method was developed to enable in-line measurements of foam structure parameters for highly aerated batters by mode conversion. Biscuit batters were foamed to different degrees (density: 364–922 g/L) by varying the mixing head speed and pressure. Density and foam structure changes were detected by efficient offline analytics (nref measurement = 96). Ultrasonic signal data were recorded using two ultrasonic sensors attached to an industry-standard tube. Mode conversion effects in the ultrasonic signals were obtained to predict the rheological parameters of the batters. The frequency range in which surface waves are expected was particularly suitable for detecting rheological changes in highly aerated batters. An ultrasonic-based, online-capable method for process monitoring was implemented and established regarding feature selection in combination with machine learning and 5-fold cross-validation. The developed ultrasonic sensor system shows high accuracy for online density measurement (R² = 0.98) and offers decent accuracy for measurements of foam structure parameters (Bubble count: R² = 0.95, Relative span: R² = 0.93, Sauter diameter: R² = 0.83). The main benefit of this novel technique is that integrating ultrasonic signal features based on mode conversion leads to a robust foam structure analysis, which has the advantage of being retrofitable into existing processes.