Enrichment and Quantitation of Dipeptidyl Peptidase IV Inhibitory Peptides in Quinoa upon Systematic Malting.

Food-derived peptides with an inhibitory effect on dipeptidyl peptidase IV (DPP-IV) can be used as an additive treatment for type 2 diabetes. The inhibitory potential of food depends on technological protein hydrolysis and gastrointestinal digestion, as the peptides only act after intestinal resorption. The effect of malting as a hydrolytic step on the availability of these peptides in grains has yet to be investigated. In this study, quinoa was malted under systematic temperature, moisture, and time variations. In the resulting malts, the DPP-IV inhibition reached a maximum of 45.02 (±10.28) %, whereas the highest overall concentration of literature-known inhibitory peptides was 4.07 µmol/L, depending on the malting parameters. After in vitro gastrointestinal digest, the inhibition of most malts, as well as the overall concentration of inhibitory peptides, could be increased significantly. Additionally, the digested malts showed higher values in both the inhibition and the peptide concentration than the unmalted quinoa. Concerning the malting parameters, germination time had the highest impact on the inhibition and the peptide concentration after digest. An analysis of the protein sizes before and after malting gave first hints toward the origin of these peptides, or their precursors, in quinoa.

Studying the impact of cell age on the yeast growth behaviour of Saccharomyces pastorianus var. carlsbergensis by magnetic separation.

Despite the fact that yeast is a widely used microorganism in the food, beverage, and pharmaceutical industries, the impact of viability and age distribution on cultivation performance has yet to be fully understood. For a detailed analysis of fermentation performance and physiological state, we introduced a method of magnetic batch separation to isolate daughter and mother cells from a heterogeneous culture. By binding functionalised iron oxide nanoparticles, it is possible to separate the chitin-enriched bud scars by way of a linker protein. This reveals that low viability cultures with a high daughter cell content perform similarly to a high viability culture with a low daughter cell content. Magnetic separation results in the daughter cell fraction (>95%) showing a 21% higher growth rate in aerobic conditions than mother cells and a 52% higher rate under anaerobic conditions. These findings emphasise the importance of viability and age during cultivation and are the first step towards improving the efficiency of yeast-based processes.

Quantification methods of determining brewer's and pharmaceutical yeast cell viability: accuracy and impact of nanoparticles.

For industrial processes, a fast, precise, and reliable method of determining the physiological state of yeast cells, especially viability, is essential. However, an increasing number of processes use magnetic nanoparticles (MNPs) for yeast cell manipulation, but their impact on yeast cell viability and the assay itself is unclear. This study tested the viability of Saccharomyces pastorianus ssp. carlsbergensis and Pichia pastoris by comparing traditional colourimetric, high-throughput, and growth assays with membrane fluidity. Results showed that methylene blue staining is only reliable for S. pastorianus cells with good viability, being erroneous in low viability (R2 = 0.945; [Formula: see text] = 5.78%). In comparison, the fluorescence microscopy-based assay of S. pastorianus demonstrated a coefficient of determination of R2 = 0.991 at [Formula: see text] ([Formula: see text] = 2.50%) and flow cytometric viability determination using carboxyfluorescein diacetate (CFDA), enabling high-throughput analysis of representative cell numbers; R2 = 0.972 ([Formula: see text]; [Formula: see text] = 3.89%). Membrane fluidity resulted in a non-linear relationship with the viability of the yeast cells ([Formula: see text]). We also determined similar results using P. pastoris yeast. In addition, we demonstrated that MNPs affected methylene blue staining by overestimating viability. The random forest model has been shown to be a precise method for classifying nanoparticles and yeast cells and viability differentiation in flow cytometry by using CFDA. Moreover, CFDA and membrane fluidity revealed precise results for both yeasts, also in the presence of nanoparticles, enabling fast and reliable determination of viability in many experiments using MNPs for yeast cell manipulation or separation.

Autofluorescence prediction model for fluorescence unmixing and age determination.

BACKGROUND: Flow cytometry is a powerful tool for identifying and quantifying various cell markers, such as viability, vitality, and individual cell age, at single-cell stages. However, cell autofluorescence and marker fluorophore signals overlap at low fluorescence intensities. Thus, these signals must be unmixed before determining the age fraction. METHODS AND RESULTS: A comparison was made between principal component regression (PCR) and random forest (RF) to predict autofluorescence signals of Saccharomyces pastorianus var. carlsbergensis in a flow cytometer. RF provided better prediction results than the PCR and was therefore determined to be better suited for unmixing signals. In the subsequent application for unmixing the autofluorescence signal from the marker fluorophore signal, the Gaussian mixture analysis based on RF was in better agreement with the microscopy-determined replicative age distribution than the PCR-based method. CONCLUSION: The proposed approach of single-laser spectral unmixing and subsequent Gaussian mixture analysis showed that the microscopy data was consistent with the unmixed fluorescence spectra. The demonstrated approach enables fast and reliable unmixing of flow cytometric spectral data using a single-laser spectral unmixing method. This analysis method enables age determination of cells in industrial processes. This age determination allows for quantifying the yeast cell's age fractions, providing a detailed view of age-related changes. Additionally, the bud scar labeling technique can be used to determine age-related changes in Pichia pastoris yeast for biotechnological applications or recombinant protein expression.

Trigeminal Stimulus Menthol Masks Bitter Off-Flavor of Artificial Sweetener Acesulfame-K.

Consumer health concerns and regulatory policies lead to a growing demand for sugar-sweetened beverage alternatives. A reduced energy content can be achieved using artificial sweeteners, which often also convey a metallic or bitter off-flavor. Therefore, the alteration of sweetness perception and masking of potential off-flavors are paramount for improving sweet beverages. Trigeminal stimuli, such as capsaicin (spicy) or menthol (cooling), have been used to influence taste perception in food items, although their use in beverages has not yet been systematically investigated. Here, the influence of menthol on sweetness perception in an aqueous solution is examined both on the sensory and psychophysiological level. The addition of menthol had no sensory effect on sweetness perception; however, psychophysiological measurements suggest a boost in the physiological response to cold perception through the addition of sugar. Moreover, menthol addition shifted the recognition threshold of unpleasant bitterness of the sweetener acesulfame-K from 21.35 to 36.93 mg/L, masking the off-flavor. These findings illuminate the complexity of trigeminal perception influences on taste. Further investigation of these effects can render trigeminal stimuli an effective tool to enhance beverage aroma and flavor.

Impact of Storing Condition on Staling and Microbial Spoilage Behavior of Bread and Their Contribution to Prevent Food Waste.

The high loss rate of bread is generally known to contribute to the alarmingly high numbers in worldwide food waste. Correct storage techniques are believed to enable the reduction of preventable food waste. Therefore, the influence of storage parameters on staling and spoilage behavior of German bread within the limits of common household methods was investigated in this study. The aim was to generate reliable data for staling and spoilage using different storage methods (PE-layered microperforated paper bag, plastic bag, and fridge and bread box) to bridge the gap between consumer's needs and scientific research questions. Everyday routines of life, such as visual inspection, were compared with microbiological techniques and were found to represent an adequate tool for microbial safety control. Visually undetectable fungal growth has not been found to result in the production of mycotoxins (fumonisins B1 and B2 and ochratoxin A) in quantifiable or harmful concentrations. Thus, disgust should prevent any foodborne health risks as the visual appearance should lead to avoiding the consumption of spoiled food before mycotoxins are produced in amounts causing adverse health effects within the limits of this experimental setup. Additionally, the storage temperature especially was found to influence the kinetics of staling processes, as a reduction accelerated the staling process. Further, crumb moisture loss was found to contradict a long shelf life but, on the other hand, an elevated humidity was shown to provoke excessive microbial growth and should therefore be observed when designing suitable storage methods. Further, the correct choice of the bread type stored and a good sanitary practice represent simply accessible ways to prolong the storage period of bread loaves.

Recombinant protein linker production as a basis for non-invasive determination of single-cell yeast age in heterogeneous yeast populations.

The physiological and metabolic diversity of a yeast culture is the sum of individual cell phenotypes. As well as environmental conditions, genetics, and numbers of cell divisions, a major factor influencing cell characteristics is cell age. A postcytokinesis bud scar on the mother cell, a benchmark in the replicative life span, is a quantifiable indicator of cell age, characterized by significant amounts of chitin. We developed a binding process for visualizing the bud scars of Saccharomyces pastorianus var. carlsbergensis using a protein linker containing a polyhistidine tag, a superfolder green fluorescent protein (sfGFP), and a chitin-binding domain (His6-SUMO-sfGFP-ChBD). The binding did not affect yeast viability; thus, our method provides the basis for non-invasive cell age determination using flow cytometry. The His6-SUMO-sfGFP-ChBD protein was synthesized in Escherichia coli, purified using two-stage chromatography, and checked for monodispersity and purity. Linker-cell binding and the characteristics of the bound complex were determined using flow cytometry and confocal laser scanning microscopy (CLSM). Flow cytometry showed that protein binding increased to 60 455 ± 2706 fluorescence units per cell. The specific coupling of the linker to yeast cells was additionally verified by CLSM and adsorption isotherms using yeast cells, E. coli cells, and chitin resin. We found a relationship between the median bud scar number, the median of the fluorescence units, and the chitin content of yeast cells. A fast measurement of yeast population dynamics by flow cytometry is possible, using this protein binding technique. Rapid qualitative determination of yeast cell age distribution can therefore be performed.

Understanding the Impact of Industrial Stress Conditions on Replicative Aging in Saccharomyces cerevisiae.

In yeast, aging is widely understood as the decline of physiological function and the decreasing ability to adapt to environmental changes. Saccharomyces cerevisiae has become an important model organism for the investigation of these processes. Yeast is used in industrial processes (beer and wine production), and several stress conditions can influence its intracellular aging processes. The aim of this review is to summarize the current knowledge on applied stress conditions, such as osmotic pressure, primary metabolites (e.g., ethanol), low pH, oxidative stress, heat on aging indicators, age-related physiological changes, and yeast longevity. There is clear evidence that yeast cells are exposed to many stressors influencing viability and vitality, leading to an age-related shift in age distribution. Currently, there is a lack of rapid, non-invasive methods allowing the investigation of aspects of yeast aging in real time on a single-cell basis using the high-throughput approach. Methods such as micromanipulation, centrifugal elutriator, or biotinylation do not provide real-time information on age distributions in industrial processes. In contrast, innovative approaches, such as non-invasive fluorescence coupled flow cytometry intended for high-throughput measurements, could be promising for determining the replicative age of yeast cells in fermentation and its impact on industrial stress conditions.

Online sensor validation in sensor networks for bioprocess monitoring using swarm intelligence.

Sensor faults can impede the functionality of monitoring and control systems for bioprocesses. Hence, suitable systems need to be developed to validate the sensor readings prior to their use in monitoring and control systems. This study presents a novel approach for online validation of sensor readings. The basic idea is to compare the original sensor reading with predictions for this sensor reading based on the remaining sensor network's information. Deviations between original and predicted sensor readings are used to indicate sensor faults. Since especially batch processes show varying lengths and different phases (e.g., lag and exponential phase), prediction models that are dependent on process time are necessary. The binary particle swarm optimization algorithm is used to select the best prediction models for each time step. A regularization approach is utilized to avoid overfitting. Models with high complexity and prediction errors are penalized, resulting in optimal predictions for the sensor reading at each time step (5% mean relative prediction error). The sensor reliability is calculated by the Kullback-Leibler divergence between the distribution of model-based predictions and the distribution of a moving window of original sensor readings (moving window size = 10 readings). The developed system allows for the online detection of sensor faults. This is especially important when sensor data are used as input to soft sensors for critical quality attributes or the process control system. The proof-of-concept is exemplarily shown for a turbidity sensor that is used to monitor a Pichia pastoris-batch process.