Advancing 2D fluorescence online monitoring in microtiter plates by separating scattered light and fluorescence measurement, using a tunable emission monochromator.

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.

Online Monitoring of Sourdough Fermentation Using a Gas Sensor Array with Multivariate Data Analysis.

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.

Consumption of Yeast-Fermented Wheat and Rye Breads Increases Colitis and Mortality in a Mouse Model of Colitis.

BACKGROUND: Cereals are known to trigger for wheat allergy, celiac disease and non-celiac wheat sensitivity (NCWS). Inflammatory processes and intestinal barrier impairment are suspected to be involved in NCWS, although the molecular triggers are unclear. AIMS: We were interested if different bread types influence inflammatory processes and intestinal barrier function in a mouse model of inflammatory bowel disease. METHODS: Epithelial caspase-8 gene knockout (Casp8ΔIEC) and control (Casp8fl) mice were randomized to eight groups, respectively. The groups received different diets for 28 days (gluten-free diet, gluten-rich diet 5 g%, or different types of bread at 50 g%). Breads varied regarding grain, milling and fermentation. All diets were isocaloric. RESULTS: Regardless of the diet, Casp8ΔIEC mice showed pronounced inflammation in colon compared to ileum, whereas Casp8fl mice were hardly inflamed. Casp8fl mice could tolerate all bread types. Especially yeast fermented rye and wheat bread from superfine flour but not pure gluten challenge increased colitis and mortality in Casp8ΔIEC mice. Hepatic expression of lipopolysaccharide-binding protein and colonic expression of tumor necrosis factor-α genes were inversely related to survival. The bread diets, but not the gluten-rich diet, also decreased colonic tight junction expression to variable degrees, without clear association to survival and inflammation. CONCLUSIONS: Bread components, especially those from yeast-fermented breads from wheat and rye, increase colitis and mortality in Casp8ΔIEC mice highly susceptible to intestinal inflammation, whereas control mice can tolerate all types of bread without inflammation. Yet unidentified bread components other than gluten seem to play the major role.

Generic Chemometric Models for Metabolite Concentration Prediction Based on Raman Spectra.

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.

Comparison of methods for wavelength combination selection from multi-wavelength fluorescence spectra for on-line monitoring of yeast cultivations.

The on-line monitoring with two-dimensional (2D) fluorescence spectroscopy of Saccharomyces cerevisiae batch cultivations was applied to monitor glucose, ethanol, and biomass concentrations. The measurement of one spectrum by the 2D fluorescence spectrometer has 120 fluorescence intensity values of excitation and emission wavelength combinations (WLCs); scattered light is not considered here. To identify which WLCs of the multi-wavelength fluorescence spectra carry important and relevant information regarding the analyte concentrations, three different methods were compared: a method based on loadings, variable importance in projection, and ant colony optimization. The five selected WLCs for a particular analyte from each method were evaluated by multiple linear regression models to find the most significant variable subsets for predicting the sample concentrations. The most significant WLCs relevant to the three sample properties contained seven different excitation and emission wavelengths, which can combine with each other to have 38 possible wavelength combinations in the fluorescence measurement. Partial least squares (PLS) models were calibrated with the 38 possible variables and the off-line data for the prediction of glucose, ethanol, and biomass concentrations. The best prediction from the PLS models had the percentage of root mean square error of prediction (pRMSEP) in the range of 3.1-6.3 %, which was similar to pRMSEPs of the PLS models with the full variables. Based on these results, it is promising to build up a specific inexpensive fluorescence sensor for the yeast cultivation process using light-emitting diodes and photodiodes. Graphical abstract Flow chart of the selection of important and relevant WLCs by the three methods to predict glucose, ethanol, and biomass concentrations.

Fluorescence spectroscopy and chemometric modeling for bioprocess monitoring.

On-line sensors for the detection of crucial process parameters are desirable for the monitoring, control and automation of processes in the biotechnology, food and pharma industry. Fluorescence spectroscopy as a highly developed and non-invasive technique that enables the on-line measurements of substrate and product concentrations or the identification of characteristic process states. During a cultivation process significant changes occur in the fluorescence spectra. By means of chemometric modeling, prediction models can be calculated and applied for process supervision and control to provide increased quality and the productivity of bioprocesses. A range of applications for different microorganisms and analytes has been proposed during the last years. This contribution provides an overview of different analysis methods for the measured fluorescence spectra and the model-building chemometric methods used for various microbial cultivations. Most of these processes are observed using the BioView® Sensor, thanks to its robustness and insensitivity to adverse process conditions. Beyond that, the PLS-method is the most frequently used chemometric method for the calculation of process models and prediction of process variables.

Modeling and optimization of bakery production scheduling to minimize makespan and oven idle time.

Makespan dominates the manufacturing expenses in bakery production. The high energy consumption of ovens also has a substantial impact, which bakers may overlook. Bakers leave ovens running until the final product is baked, allowing them to consume energy even when not in use. It results in energy waste, increased manufacturing costs, and CO2 emissions. This paper investigates three manufacturing lines from small and medium-sized bakeries to find optimum makespan and ovens' idle time (OIDT). A hybrid no-wait flow shop scheduling model considering the constraints that are most common in bakeries is proposed. To find optimal solutions, non-dominated sorting genetic algorithm (NSGA-II), strength Pareto evolutionary algorithm (SPEA2), generalized differential evolution (GDE3), improved multi-objective particle swarm optimization (OMOPSO), and speed-constrained multi-objective particle swarm optimization (SMPSO) were used. The experimental results show that the shortest makespan does not always imply the lowest OIDT. Even the optimized solutions have up to 231 min of excess OIDT, while the makespan is the shortest. Pareto solutions provide promising trade-offs between makespan and OIDT, with the best-case scenario reducing OIDT by 1348 min while increasing makespan only by 61 min from the minimum possible makespan. NSGA-II outperforms all other algorithms in obtaining a high number of good-quality solutions and a small number of poor-quality solutions, followed by SPEA2 and GDE3. In contrast, OMOPSO and SMPSO deliver the worst solutions, which become pronounced as the problem complexity grows.

Online Monitoring of the Growth of Probiotic Bacteria and Metabolites in the Fermentation of a Teff Substrate Using Model-Based Calibration of 2D Fluorescence Spectra.

The demand for probiotic bacteria-fermented food products is increasing; however, the monitoring of the fermentation process is still challenging when using conventional approaches. A classical approach requires a large amount of offline data to calibrate a chemometric model using fluorescence spectra. Fluorescence spectra provide a wide range of online information during the process of cultivation, but they require a large amount of offline data (which involves laborious work) for the calibration procedure when using a classical approach. In this study, an alternative model-based calibration approach was used to predict biomass (the growth of Lactiplantibacillus plantarum A6 (LPA6) and Lacticaseibacillus rhamnosus GG (LCGG)), glucose, and lactic acid during the fermentation process of a teff-based substrate inoculated with mixed strains of LPA6 and LCGG. A classical approach was also applied and compared to the model-based calibration approach. In the model-based calibration approach, two-dimensional (2D) fluorescence spectra and offline substituted simulated data were used to generate a chemometric model. The optimum microbial specific growth rate and chemometric model parameters were obtained simultaneously using a particle swarm optimization algorithm. The prediction errors for biomass, glucose, and lactic acid concentrations were measured between 6.1 and 10.5%; the minimum error value was related to the prediction of biomass and the maximum one was related to the prediction of glucose using the model-based calibration approach. The model-based calibration approach and the classical approach showed similar results. In conclusion, the findings showed that a model-based calibration approach could be used to monitor the process state variables (i.e., biomass, glucose, and lactic acid) online in the fermentation process of a teff-based substrate inoculated with mixed strains of LPA6 and LCGG. However, glucose prediction showed a high error value.

Assessing the capabilities of 2D fluorescence monitoring in microtiter plates with data-driven modeling for secondary substrate limitation experiments of Hansenula polymorpha.

BACKGROUND: Non-invasive online fluorescence monitoring in high-throughput microbioreactors is a well-established method to accelerate early-stage bioprocess development. Recently, single-wavelength fluorescence monitoring in microtiter plates was extended to measurements of highly resolved 2D fluorescence spectra, by introducing charge-coupled device (CCD) detectors. Although introductory experiments demonstrated a high potential of the new monitoring technology, an assessment of the capabilities and limits for practical applications is yet to be provided. RESULTS: In this study, three experimental sets introducing secondary substrate limitations of magnesium, potassium, and phosphate to cultivations of a GFP-expressing H. polymorpha strain were conducted. This increased the complexity of the spectral dynamics, which were determined by 2D fluorescence measurements. The metabolic responses upon growth limiting conditions were assessed by monitoring of the oxygen transfer rate and extensive offline sampling. Using only the spectral data, subsequently, partial least-square (PLS) regression models for the key parameters of glycerol, cell dry weight, and pH value were generated. For model calibration, spectral data of only two cultivation conditions were combined with sparse offline sampling data. Applying the models to spectral data of six cultures not used for calibration, resulted in an average relative root-mean-square error (RMSE) of prediction between 6.8 and 6.0%. Thus, while demanding only sparse offline data, the models allowed the estimation of biomass accumulation and glycerol consumption, even in the presence of more or less pronounced secondary substrate limitation. CONCLUSION: For the secondary substrate limitation experiments of this study, the generation of data-driven models allowed a considerable reduction in sampling efforts while also providing process information for unsampled cultures. Therefore, the practical experiments of this study strongly affirm the previously claimed advantages of 2D fluorescence spectroscopy in microtiter plates.

Application of CO2 Gas Hydrates as Leavening Agents in Black-and-White Cookies.

In this unprecedented study, the application of CO2 gas hydrates (GH) as a leavening agent to produce black-and-white cookies by replacing ammonium bicarbonate is investigated. Ammonium bicarbonate, the principal leavening ingredient in black-and-white cookies, has been linked to the creation of a carcinogenic substance known as acrylamide. Three distinct GH concentrations, 20%, 40%, and 50%, were utilized to determine the necessary amount to obtain a good leavening effect. However, the abrupt reduction in temperature brought on by the addition of GH had an inadmissible effect on the cookie dough. Consequently, an innovative kneading method carried out in a closed mixing unit at a high temperature was developed. The specific volume of the cookies when employing 50% GH as a baking agent was more than half that produced when using ammonium bicarbonate. In the cookies with GH, the springiness and hardness, which are the quality-determining textural characteristics of the pastry, remained within an acceptable range. The amount of acrylamide was reduced from 24.8 µg/Kg to around 18 µg/Kg by this research. Therefore, the presented study demonstrates the possibility of using CO2 GH as a leavening agent in black-and-white cookies and in other products for a healthier future.

Influence of Non-Thermal Plasma Treatment on Structural Network Attributes of Wheat Flour and Respective Dough.

Due to its "generally recognized as safe status" (GRAS) and moderate treatment temperatures, non-thermal plasma (NTP) has lately been considered a suitable replacement for chemicals in the modification of food properties and for preserving food quality. One of the promising areas for the application of NTP is the treatment of wheat flour, leading to improved flour properties and product quality and consequently to higher customer satisfaction. In the present research, the German wheat flour type 550, equivalent to all-purpose flour, was treated using NTP in a rotational reactor to determine the influence of short treatment times (≤5 min) on the properties of flour (moisture and fat content, protein, starch, color, microbial activity, and enzymes), dough (visco-elastic properties, starch, wet and dry gluten, and water absorption), and baking products (color, freshness, baked volume, crumb structure, softness, and elasticity). Based on the properties of NTP, it was expected that even very short treatment times would have a significant effect on the flour particles, which could positively affect the quality of the final baking product. Overall, the experimental analysis showed a positive effect of NTP treatment of wheat flour, e.g., decreased water activity value (<0.7), which is known to positively affect flour stability and product shelf life; dough stability increased (>8% after 5 min. treatment); dough extensibility increased (ca. 30% after 3 min treatment); etc. Regarding the baking product, further positive effects were detected, e.g., enhanced product volume (>9%), improved crumb whiteness/decreased crumb yellowness, softening of breadcrumb without a change in elasticity, and limited microorganism and enzymatic activity. Furthermore, no negative effects on the product quality were observed, even though further food quality tests are required. The presented experimental research confirms the overall positive influence of NTP treatment, even for very low treatment times, on wheat flour and its products. The presented findings are significant for the potential implementation of this technique on an industrial level.

Improving the Leavening Effect of Ice like CO2 Gas Hydrates by Addition of Gelling Agents in Wheat Bread.

This article brings together the application of ice-like CO2 gas hydrates (GH) as a leavening agent in wheat bread along with the incorporation of some natural gelling agents or flour improvers into the bread to enhance the textural properties of the wheat bread. The gelling agents used for the study were ascorbic acid (AC), egg white (EW), and rice flour (RF). These gelling agents were added to the GH bread containing different amounts of GH (40, 60, and 70% GH). Moreover, a combination of these gelling agents in a wheat GH bread recipe was studied for each respective percentage of GH. The combinations of gelling agents used in the GH bread were as follows: (1) AC, (2) RF + EW, and (3) RF + EW + AC. The best combination of GH wheat bread was 70% GH + AC + EW + RF combination. The primary goal of this research is to gain a better understanding of the complex bread dough created by CO2 GH and its influence on product quality when certain gelling agents are added to the dough. Moreover, the prospect of managing and modifying wheat bread attributes by the use of CO2 GH with the addition of natural gelling agents has not yet been researched and is a fresh idea in the food industry.

Chemometric modeling and two-dimensional fluorescence analysis of bioprocess with a new strain of Klebsiella pneumoniae to convert residual glycerol into 1,3-propanediol.

The goal of this study was to show that the metabolism of Klebsiella pneumoniae under different aeration strategies could be monitored and predicted by the application of chemometric models and fluorescence spectroscopy. Multi-wavelength fluorescence was applied to the on-line monitoring of process parameters for K. pneumoniae cultivations. Differences observed in spectra collected under aerobiosis and anaerobiosis can be explained by the different metabolic states of the cells. To predict process variables such as biomass, glycerol, and 1,3-propanediol (1,3-PD), chemometric models were developed on the basis of the acquired fluorescence spectra, which were measured continuously. Although glycerol and 1,3-PD are not fluorescent compounds, the results showed that this technique could be successfully applied to the on-line monitoring of variables in order to understand the process and thus improve 1,3-PD production. The root mean square errors of predictions were 0.78 units, 10 g/L, and 2.6 g/L for optical density, glycerol, and 1,3-PD, respectively.

Comparison of various classification techniques for supervision of milk processing.

Detecting the types of anomalies that can occur throughout the milk processing process is an important task since it can assist providers in maintaining control over the process. The Raman spectrometer was used in conjunction with several classification approaches-linear discriminant analysis, decision tree, support vector machine, and k nearest neighbor-to establish a viable method for detecting different types of anomalies that may occur during the process-temperature and fat variation and added water or cleaning solution. Milk with 5% fat measured at 10°C was used as the reference milk for this study. Added water, cleaning solution, milk with various fat contents and different temperatures were used to detect abnormal conditions. While decision trees and linear discriminant analysis were unable to accurately categorize the various type of anomalies, the k nearest neighbor and support vector machine provided promising results. The accuracy of the support vector machine test set and the k nearest neighbor test set were 81.4% and 84.8%, respectively. As a result, it is reasonable to conclude that both algorithms are capable of appropriately classifying the various groups of samples. It can assist milk industries in determining what is wrong during milk processing.

Tailoring the Textural Characteristics of Fat-Free Fermented Concentrated Milk-Protein Based Microgel Dispersions by Way of Upstream, Downstream and Post-Production Thermal Inputs.

There is a growing demand for new strategies to tailor the texture of fat-free fermented concentrated milk products, also referred to as milk protein-based (MPb) microgel dispersions. Methods should be easy to incorporate into the production scheme, offer labelling without added components and be cost-efficient. Thermal treatments are traditionally used upstream (milk heating) and downstream (pre-concentration heating) in the production of these dispersions, though there is little knowledge as to the effects that combinations of different thermal input levels have on final texture. Therefore, this study investigated combinations of thermal input at different intensities and steps in the production scheme at the pilot scale and the relationships with texture. We demonstrated that increasing the intensity of upstream milk heat treatment, in combination with downstream pre-concentration heating, increases gel firmness and apparent viscosity. Downstream pre-concentration heating produces final fat-free fermented concentrated MPb microgel particles that are resistant to post-heating aggregation. On the other hand, omission of downstream pre-concentration heating results in smaller particles that are sensitive to post-heating aggregation. Furthermore, gel firmness and apparent viscosity increase with post-heating. Consequently, combining different levels of thermal inputs upstream, downstream (pre-concentration) and post-production, can produce fat-free fermented concentrated MPb microgel dispersions with a range of different textures.

Experimental investigation of CO2 uptake in CO2 hydrates formation with amino acids as kinetic promoters and its dissociation at high temperature.

The dissociation of CO2 gas hydrates (GH) with amino acid kinetic promoters and without promoters was studied at a high temperature of 90 °C for a period of 20 min to understand the percentage of CO2 gas and to select the best promoter that aids CO2 gas entrapment along with stability at a high temperature. The possibility of using four hydrophobic food grade amino acids, namely cysteine, valine, leucine, and methionine, and one surfactant, lecithin, as kinetic promoters for CO2 GH has been studied. The amino acids were added 0.5 g (wt%), and lecithin was added 5 g for the GH production. Furthermore, the amino acids leucine and methionine gave some positive results, therefore, these amino acids were carried further for the experimentation purpose in the production of CO2 GH. Also, a combinational use of these amino acids was studied to investigate the effect on % CO2 retention in comparison to the normal GH. From the results, 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.

Application of Two-Dimensional Fluorescence Spectroscopy for the On-Line Monitoring of Teff-Based Substrate Fermentation Inoculated with Certain Probiotic Bacteria.

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.

Online 2D Fluorescence Monitoring in Microtiter Plates Allows Prediction of Cultivation Parameters and Considerable Reduction in Sampling Efforts for Parallel Cultivations of Hansenula polymorpha.

Multi-wavelength (2D) fluorescence spectroscopy represents an important step towards exploiting the monitoring potential of microtiter plates (MTPs) during early-stage bioprocess development. In combination with multivariate data analysis (MVDA), important process information can be obtained, while repetitive, cost-intensive sample analytics can be reduced. This study provides a comprehensive experimental dataset of online and offline measurements for batch cultures of Hansenula polymorpha. In the first step, principal component analysis (PCA) was used to assess spectral data quality. Secondly, partial least-squares (PLS) regression models were generated, based on spectral data of two cultivation conditions and offline samples for glycerol, cell dry weight, and pH value. Thereby, the time-wise resolution increased 12-fold compared to the offline sampling interval of 6 h. The PLS models were validated using offline samples of a shorter sampling interval. Very good model transferability was shown during the PLS model application to the spectral data of cultures with six varying initial cultivation conditions. For all the predicted variables, a relative root-mean-square error (RMSE) below 6% was obtained. Based on the findings, the initial experimental strategy was re-evaluated and a more practical approach with minimised sampling effort and elevated experimental throughput was proposed. In conclusion, the study underlines the high potential of multi-wavelength (2D) fluorescence spectroscopy and provides an evaluation workflow for PLS modelling in microtiter plates.

An Innovative Approach in the Baking of Bread with CO2 Gas Hydrates as Leavening Agents.

Gas (guest) molecules are trapped in hydrogen-bonded water molecules to form gas hydrates (GH), non-stoichiometric solids that resemble ice. High pressure and low temperature are typical conditions for their development, with van der Waals forces joining the host and guest molecules. This article study investigates the application of CO2 gas hydrates (CO2 GH) as a leavening agent in baking, with particular reference to the production of wheat bread. The main intention of this study is to better understand the complex bread dough formed by CO2 GH and its impact on product quality. This may enable the adaptation of CO2 GH in baking applications, such as those that can specifically influence wheat bread properties, and so the final bread quality. The present research further examines the comparative evaluation of yeast bread with the GH bread's impact on bread quality parameters. The amount of GH was varied from 10 to 60%/amount of flour for the GH breads. The GH breads were compared with the standard yeast bread for different quality parameters such as volume, texture, and pore analysis. The results show that the bread with 20% and 40% GH obtained the best results in terms of volume and pore size. Moreover, this article also sheds some light on the future applications of the use of CO2 GH as leavening agents in foods. This knowledge could help to create new procedures and criteria for improved GH selection for applications in bread making and other bakery or food products.

Teff-Based Probiotic Functional Beverage Fermented with Lactobacillus rhamnosus and Lactobacillus plantarum.

Consumers are demanding healthier foods, and the increasing drawbacks associated with dairy-based products have driven efforts to find plant-based probiotic alternatives. Consequently, this study aimed to evaluate the suitability of a teff-based substrate for delivering the potential probiotics, Lactobacillus rhamnosus GG (LGG) and Lactobacillus plantarum A6 (LA6) with a view to developing probiotic functional beverages. Single-strain and mixed-strain fermentations were performed without any pH control. In single-strain fermentation, LA6 grew to 8.157-8.349 log cfu/mL. Titratable acidity (TA) and pH were measured between 0.513-1.360 g/L and 4.25-3.91, respectively. The explored optimum variables were fermentation time (15 h) and inoculum (6 log cfu/mL). As a result of fermentation, maltose and glucose decreased, but lactic and acetic acids increased. In mixed-strain fermentation, LGG and LA6 were able to grow to 8.247 and 8.416 log cfu/mL, respectively. The pH, TA, lactic, and acetic acids varied between 6.31-3.92, 0.329-1.501 g/L, 0-1672 mg/L, and 20-231.5 mg/L, respectively. In both fermentations, microbial growth reached the stationary phase close to a pH of 4.21-4.82 while sugars were not consumed completely. Less than 5% ethanol was detected, which indicated a non-alcoholic beverage. A combination of the two evaluated lactobacilli strains reduced fermentation time. In conclusion, a substrate made of whole grain teff flour without any supplement could be used as a substrate to produce functional probiotic beverages.