A modeling method for the development of a bioprocess to optimally produce umqombothi (a South African traditional beer)

Optimization of preparation conditions for Salsola laricifolia protoplasts using response surface methodology and artificial neural network modeling

BACKGROUND

Salsola laricifolia is a typical C3-C4 typical desert plant, belonging to the family Amaranthaceae. An efficient single-cell system is crucial to study the gene function of this plant. In this study, we optimized the experimental conditions by using Box-Behnken experimental design and Response Surface Methodology (RSM)-Artificial Neural Network (ANN) model based on the previous studies.

RESULTS

Among the 17 experiment groups designed by Box-Behnken experimental design, the maximum yield (1.566 × 106/100 mg) and the maximum number of viable cells (1.367 × 106/100 mg) were obtained in group 12, and the maximum viability (90.81%) was obtained in group 5. Based on these results, both the RSM and ANN models were employed for evaluating the impact of experimental factors. By RSM model, cellulase R-10 content was the most influential factor on protoplast yield, followed by macerozyme R-10 content and mannitol concentration. For protoplast viability, the macerozyme R-10 content had the highest influence, followed by cellulase R-10 content and mannitol concentration. The RSM model performed better than the ANN model in predicting yield and viability. However, the ANN model showed significant improvement in predicting the number of viable cells. After comprehensive evaluation of the protoplast yield, the viability and number of viable cells, the optimal results was predicted by ANN yield model and tested. The amount of protoplast yield was 1.550 × 106/100 mg, with viability of 90.65% and the number of viable cells of 1.405 × 106/100 mg. The corresponding conditions were 1.98% cellulase R-10, 1.00% macerozyme R-10, and 0.50 mol L-1 mannitol. Using the obtained protoplasts, the reference genes (18SrRNA, β-actin and EF1-α) were screened for expression, and transformed with PEG-mediated pBI121-SaNADP-ME2-GFP plasmid vector. There was no significant difference in the expression of β-actin and EF1-α before and after treatment, suggesting that they can be used as internal reference genes in protoplast experiments. And SaNADP-ME2 localized in chloroplasts.

CONCLUSION

The current study validated and evaluated the effectiveness and results of RSM and ANN in optimizing the conditions for protoplast preparation using S. laricifolia as materials. These two methods can be used independently of experimental materials, making them suitable for isolating protoplasts from other plant materials. The selection of the number of viable cells as an evaluation index for protoplast experiments is based on its ability to consider both protoplast yield and viability. The findings of this study provide an efficient single-cell system for future genetic experiments in S. laricifolia and can serve as a reference method for preparing protoplasts from other materials.

Statistical versus neural network-embedded swarm intelligence optimization of a metallo-neutral-protease production: activity kinetics and food industry applications

An integrated approach involving response surface methodology (RSM) and artificial neural network-ant-colony hybrid optimization (ANN-ACO) was adopted to develop a bioprocess medium to increase the yield of Bacillus cereus neutral protease under submerged fermentation conditions. The ANN-ACO model was comparatively superior (predicted r2 = 98.5%, mean squared error [MSE] = 0.0353) to RSM model (predicted r2 = 86.4%, MSE = 23.85) in predictive capability arising from its low performance error. The hybrid model recommended a medium containing (gL-1) molasses 45.00, urea 9.81, casein 25.45, Ca2+ 1.23, Zn2+ 0.021, Mn2+ 0.020, and 4.45% (vv-1) inoculum, for a 6.75-fold increase in protease activity from a baseline of 76.63 UmL-1. Yield was further increased in a 5-L bioreactor to a final volumetric productivity of 3.472 mg(Lh)-1. The 10.0-fold purified 46.6-kDa-enzyme had maximum activity at pH 6.5, 45-55 °C, with Km of 6.92 mM, Vmax of 769.23 µmolmL-1 min-1, kcat of 28.49 s-1, and kcat/Km of 4.117 × 103 M-1 s-1, at 45 °C, pH 6.5. The enzyme was stabilized by Ca2+, activated by Zn2+ but inhibited by EDTA suggesting that it was a metallo-protease. The biomolecule significantly clarified orange and pineapple juices indicating its food industry application.

Physicochemical and microbiological changes during two-stage fermentation production of umqombothi

Umqombothi is a traditional South African fermented beverage. The brewing process limits its consumption to a day or two after production due to the constant production of carbon dioxide. In this study the physicochemical and microbial changes in Umqombothi produced at two-stage fermentation temperatures [U1 (30-30 °C), U2 (30-25 °C), U3 (25-30 °C)] were studied over 52 h. Samples were collected before first fermentation (BFF), after first fermentation (AFF), before second fermentation (BSF), after second fermentation (ASF) and after final product (FP). For all three fermentation temperatures, there was a significant increase (p < 0.05) in microbial counts and a significant drop in pH following fermentation stages (AFF and ASF), with a considerable decrease in total soluble solids (TSS) after ASF. The total viable count (TVC), lactic acid bacteria (LAB), yeast, and mould were not detected in the BSF samples for all three fermentation temperatures. The LAB count was significantly (p < 0.05) different at 5.18, 5.36 and 5.25 log CFU/mL for U1, U2 and U3, respectively. The pH was 3.96, 4.12 and 4.34 for U1, U2 and U3, respectively, and was significantly (p < 0.05) different. Total soluble solids significantly (p < 0.05) increased at the BSF at all temperatures. There was no significant (p > 0.05) difference in specific gravity and ethanol content of Umqombothi at all fermentation temperatures. At all fermentation temperatures, Umqombothi was characterised by redness and yellowness, with that collected from U1 being the lightest in colour (L* = 71.24). Colour difference (ΔE) in the between of 4-8 was perceivable but acceptable as they had a ΔE value of 3.58, 2.07 and 2.02 for U1-U2, U1-U3 and U2-U3 respectively. Umqombothi produced at 30 °C for first and second fermentation (U1) was the most preferred by the consumer panellist and consequently, the best fermentation temperature to produce Umqombothi.

Mathematical modeling and optimization of pasteurization for the internal pressure and physical quality of canned beer

Globally, beer is the most popular alcoholic beverage. To accomplish microbial stabilization and extend the shelf life of beer, it is typically subjected to in-package pasteurization using a tunnel pasteurizer. However, high internal pressure can cause can bulging during pasteurization, leading to significant product loss. In this study, an empirical mathematical model was constructed to describe the effects of can thickness (0.245-0.270 mm), fill volume (320-338 mL), carbon dioxide content (5.70-6.10 g/L), and pasteurization temperature (59-66 °C) on the internal pressure inside canned beer. A laboratory-scale pasteurization setup was used to pasteurize samples based on the worst-case scenario of commercial pasteurization. The mathematical model (R2 = 0.90) showed that all parameters significantly influenced the internal pressure of pasteurized canned beer (p < 0.05). Additionally, the physical, chemical, and biological properties of pasteurized canned beer were assessed. All values fell within an acceptable range of industrial standards. A simplified 2nd-order polynomial equation (R2 = 0.90) was created and verified for industrial use. The data are well represented by the simplified model, which suggests that it could be used for optimization of product- and process parameters to reduce the occurrence of can bulging in commercial pasteurization of canned beer.

Analysis of the bacterial and fungal populations in South African sorghum beer (umqombothi) using full-length 16S rRNA amplicon sequencing

There is a need to profile microorganisms which exist pre-and-post-production of umqombothi, to understand its microbial diversity and the interactions which subsequently influence the final product. Thus, this study sought to determine the relative microbial abundance in umqombothi and predict the functional pathways of bacterial and fungal microbiota present. Full-length bacterial 16S rRNA and internal transcribed spacer (ITS) gene sequencing using PacBio single-molecule, real-time (SMRT) technology was used to assess the microbial compositions. PICRUSt2 was adopted to infer microbial functional differences. A mixture of harmful and beneficial microorganisms was observed in all samples. The microbial diversity differed significantly between the mixed raw ingredients (MRI), customary beer brew (CB), and optimised beer brew (OPB). The highest bacterial species diversity was observed in the MRI, while the highest fungal species diversity was observed in the OPB. The dominant bacterial species in the MRI, CB, and OPB were Kosakonia cowanii, Apilactobacillus pseudoficulneus, and Vibrio alginolyticus, respectively, while the dominant fungal species was Apiotrichum laibachii. The predicted functional annotations revealed significant (p < 0.05) differences in the microbial pathways of the fermented and unfermented samples. The most abundant pathways in the MRI were the branched-chain amino acid biosynthesis super pathway and the pentose phosphate pathway. The CB sample was characterised by folate (vitamin B9) transformations III, and mixed acid fermentation. Biotin (vitamin B7) biosynthesis I and L-valine biosynthesis characterised the OPB sample. These findings can assist in identifying potential starter cultures for the commercial production of umqombothi. Specifically, A. pseudoficulneus can be used for controlled fermentation during the production of umqombothi. Likewise, the use of A. laibachii can allow for better control over the fermentation kinetics such as carbohydrate conversion and end-product characteristics, especially esters and aroma compounds.

Experimental and Statistical Study on the Properties of Basic Oxygen Furnace Slag and Ground Granulated Blast Furnace Slag Based Alkali-Activated Mortar

Basic oxygen furnace slag (BOFS) is a waste material generated during the steelmaking process and has the potential to harm both the environment and living organisms when disposed of in a landfill. However, the cementitious properties of BOFS might help in utilizing this waste as an alternative material in alkali-activated systems. Therefore, in this study, BOFS and blast furnace slag were activated with varying dosages of NaOH, and the fresh, physical, mechanical, and microstructural properties were determined along with statistical analysis to reach the optimal mix design. The test results showed that an increase in BOFS content decreased compressive and flexural strengths, whereas it slightly increased the water absorption and permeable pores of the tested mortar samples. On the contrary, the increase in NaOH molarity resulted in a denser microstructure, reduced water absorption and permeable pores, and improved mechanical properties. Statistically significant relationships were obtained through response surface methodology with optimal mix proportions, namely, (i) 24.61% BOFS and 7.74 M and (ii) 20.00% BOFS and 8.90 M, which maximize the BOFS content with lower molarity and improve the mechanical properties with lower water absorption and porosity, respectively. The proposed methodology maximizes the utilization of waste BOFS in alkali-activated systems and may promote environmental and economic benefits.

Physicochemical, structural analysis of coal discards (and sewage sludge) (co)-HTC derived biochar for a sustainable carbon economy and evaluation of the liquid by-product

This study focused on the hydrothermal treatment (HTC) of coal tailings (CT) and coal slurry (CS) and the co-hydrothermal treatment (Co-HTC) of CT, CS and sewage sludge to assess the potential for increasing the carbon content of the hydrochar produced as an enabler for a sustainable carbon economy. The optimal combination methodology and response surface methodology were used to study the relationship between the important process parameters, namely temperature, pressure, residence time, the coal-to-sewage-sludge ratio, and the carbon yield of the produced hydrochar. The optimized conditions for hydrochar from coal tailing (HCT) and hydrochar from coal slurry (HCS) (150 °C, 27 bar, 95 min) increased fixed carbon from 37.31% and 53.02% to 40.31% and 57.69%, respectively, the total carbon content improved from 42.82 to 49.80% and from 61.85 to 66.90% respectively whereas the ash content of coal discards decreased from 40.32% and 24.17% to 38.3% and 20.0% when compared CT and CS respectively. Optimized Co-HTC conditions (208 °C, 22.5bars, and 360 min) for Hydrochar from the blend of coal discards and sewage sludge (HCB) increased the fixed carbon on a dry basis and the total carbon content from 38.67% and 45.64% to 58.82% and 67.0%, when compared CT and CS respectively. Carbonization yields for HCT, HCS, and HCB were, respectively, 113.58%, 102.42%, and 129.88%. HTC and Co-HTC increase the calorific value of CT and CS, to 19.33 MJ/kg, 25.79 MJ/kg, respectively. The results further show that under Co-HTC conditions, the raw biomass undergoes dehydration and decarboxylation, resulting in a decrease in hydrogen from 3.01%, 3.56%, and 3.05% to 2.87%, 2.98%, and 2.75%, and oxygen from 8.79%, 4.78, and 8.2% to 5.83%, 2.75%, and 6.00% in the resulting HCT, HCS, and HCB, respectively. HTC and Co-HTC optimal conditions increased the specific surface area of the feedstock from 6.066 m²/g and 6.37 m²/g to 11.88 m²/g and 14.35 m²/g, for CT and CS, respectively. Total pore volume rose to 0.071 cm³/g from 0.034 cm³/g, 0.048 cm³/g, and 0.09 cm³/g proving the ability of HTC to produce high-quality hydrochar from coal discards alone or in conjunction with sewage sludge as precursors for decontamination of polluted waters, soil decontamination applications, solid combustibles, energy storage, and environmental protection.

Machine learning approaches for biomolecular, biophysical, and biomaterials research

A fluent conversation with a virtual assistant, person-tailored news feeds, and deep-fake images created within seconds-all those things that have been unthinkable for a long time are now a part of our everyday lives. What these examples have in common is that they are realized by different means of machine learning (ML), a technology that has fundamentally changed many aspects of the modern world. The possibility to process enormous amount of data in multi-hierarchical, digital constructs has paved the way not only for creating intelligent systems but also for obtaining surprising new insight into many scientific problems. However, in the different areas of biosciences, which typically rely heavily on the collection of time-consuming experimental data, applying ML methods is a bit more challenging: Here, difficulties can arise from small datasets and the inherent, broad variability, and complexity associated with studying biological objects and phenomena. In this Review, we give an overview of commonly used ML algorithms (which are often referred to as "machines") and learning strategies as well as their applications in different bio-disciplines such as molecular biology, drug development, biophysics, and biomaterials science. We highlight how selected research questions from those fields were successfully translated into machine readable formats, discuss typical problems that can arise in this context, and provide an overview of how to resolve those encountered difficulties.

Influence of Fermentation Conditions (Temperature and Time) on the Physicochemical Properties and Bacteria Microbiota of Amasi

The aim of this present study was to optimize the fermentation conditions (time and temperature) of amasi (a Southern African fermented dairy product) using response surface methodology (RSM), and to determine the physicochemical properties, as well as the microbial composition, using next generation sequencing. Fermentation time and temperature were optimized to produce different amasi samples and different parameters, including pH, total soluble solids (TSS), total titratable acids (TTA), and consistency. All the variables studied were found to show significant (p ≤ 0.05) changes with increasing fermentation time and temperature. Numerical optimization was used to obtain the optimal fermentation conditions for amasi; based on RSM, it was 32 °C for 140 h, while with k-means clustering, it was 25 °C for 120 h. Under both conditions for the optimal samples, the pH reduced from 6.64 to 3.99, TTA increased from 0.02 to 0.11 (% lactic acid), TSS decreased from 9.47 to 6.67 °Brix, and the consistency decreased from 23 to 15.23 cm/min. Most of the identified bacteria were linked to lactic acid bacteria, with the family Lactobacillaceae being the most predominant in amasi, while in raw milk, Prevotellaceae was the most abundant. The fermentation conditions (time and temperature) had a significant influence on the parameters investigated in this study. Results of this study could provide information for the commercialization of quality amasi.