Industrial Biotechnology

In this study, production of Vitamin K2 MK-7 or Vitamin K2-7 was performed by submerged fermentation of soybean. The production of Vitamin K2-7 was optimized for different time intervals (24–144 h), temperature (32°C, 37°C, and 40°C), salt concentrations (0.02 mg/mL–0.06 mg/mL) and different ratio of a mixture of salts, CaCl₂.2H₂O and MgSO₄.7H₂O (1:1, 1:2, 1:3, 3:1, 2:1). It was found that the maximum concentration of Vitamin K2-7 was obtained at fermentation time of 72 h. Salts were found to be more effective in production of Vitamin K2-7 as compared to the fermentation without salt. The mixture of salts also found to be significant for the increase in production of Vitamin K2-7. It was found that at ratio of 1:1 (CaCl₂.2H₂O:MgSO4.7H₂O), the production of Vitamin K2-7 was enhanced by 165% as compared to fermentation without salts. Temperature also plays critical role and it was found that at 37°C maximum production of VitaminK2-7 was observed. The work provides a sustainable alternate to Vitamin K2-7 production over conventional chemical synthesis method.

Microalgae metabolites exhibit significant biological activity and can act as antibacterial agents (peptide and lipid fraction) against Gram-positive bacteria Bacillus and as a component of nutrient media in the cultivation of yeast Saccharomyces cerevisiae. In this study, a mixture of metabolites (triacylglycerides, fatty acids, o-dialkylmonoglycerides and glycerol trialkyl esters) extracted from disrupted Chlorella sorokiniana microalgae cells by non-polar solvent had inhibitory effect on the growth of Gram-positive bacteria at photosynthetically active radiation (PAR) levels of 100 ± 6 μmol photons/(m²·s). The minimum inhibitory concentration (MIC) of the extract was ≈80 μg/mL. Out of the isolated components of the mixture, triacylglycerides and fatty acids showed antibacterial properties, with MIC = 176 μg/mL and MIC = 445 μg/mL respectively. The water-soluble peptide fraction extracted from microalgae cells had an inhibitory effect on the growth of Gram-positive bacteria Bacillus both under white light illumination and in the dark; MIC of the peptide fraction is ≈125 μg/mL (when illuminated) and 170 μg/mL (in the dark). The water-soluble protein fraction had no antibiotic properties, but is of interest as a component of nutrient medium (0.01 mL/mL) for the cultivation of yeast Saccharomyces cerevisiae. The addition of this fraction allowed a 21% increase in population capacity compared with the control sample, a 1.4-fold increase in the specific growth rate in the exponential phase and a 28% decrease in cell generation time. The study has contributed to establishing the effect of light radiation on antibacterial properties of microalgae metabolites and identifying the minimal inhibitory concentration with regard to Gram-positive bacteria. Also, the potential of the Chlorella aqueous extract to serve as a component of nutrient media for the Saccharomyces cerevisiae cultivation has been researched.

Owing to the complicated biomass characteristics and a variety of operating parameters, it is challenging to predict the bioethanol yield (Y_beth, %) from various agricultural wastes by consolidated bioprocessing with a microbial consortium. In this study, Gaussian Process Regression (GPR) and Artificial Neural Networks (ANN), which are powerful supervised machine learning models, were employed as predictive models that can be used to estimate bioethanol yield from various agricultural wastes. Ninety-six experimental data points obtained from the literature were preprocessed to remove noise or outliers from the dataset. The Regression Learner App in MATLAB 2021a was used on the refined 50 original data points with parallel computing and cross-validation, and the best model was selected. The squared exponential GPR model gave the best training and testing results, with R² approaching 1, RMSE, MSE, and MAE approaching 0, the lowest training time, and the highest prediction speed. A larger dataset generally provides more opportunities for the neural network to learn and improve its performance. Therefore, 3500 synthetic data were generated with 35 original seed data using Gretel ACTGAN, which was preprocessed using assumptions from the seed data, reducing it to 1,615 data points. For the ANN model, the MSE and regression R for the refined synthetic dataset (1,615 data points) trained model were close to 0 and 1, respectively. Since consolidated bioprocessing is an economical method of producing bioethanol, further development using machine learning methods will aid in predicting and optimizing the best conditions required for greater yields.