One of the leading limiting factors for wider industrial production and commercialization of microbial biopesticides refers to the high costs of cultivation media. The selection of alternative sources of macronutrients crucial for the growth and metabolic activity of the producing microorganism is a necessary phase of the bioprocess development. Gaining a better understanding of the influence of the medium composition on the biotechnological production of biocontrol agents is enabled through bioprocess modelling and optimization. In the present study, after the selection of optimal carbon and nitrogen sources, two modelling approaches were applied to mathematically describe the behavior of the examined bioprocess—the production of biocontrol agents effective against aflatoxigenic Aspergillus flavus strains. The modelling was performed using four independent variables: cellulose, urea, ammonium sulfate and dipotassium phosphate, and the selected response was the inhibition-zone diameter. After the comparison of the results generated by the Response Surface Methodology (RSM) and the Artificial Neural Network (ANN) approach, the first model was chosen for the further optimization step due to the better fit of the experimental results. As the final investigation step, the optimal cultivation medium composition was defined (g/L): cellulose 5.0, ammonium sulfate 3.77, dipotassium phosphate 0.3, magnesium sulfate heptahydrate 0.3.

Degradation of environment is a challenge to crop production around the world. Biological control of various plant diseases using antagonistic bacteria is an encouraging alternative to traditionally used chemical control strategies. Chitosan as a well-known natural flocculation agent also exhibits antimicrobial activity. The goal of this study was to investigate a dual nature of chitosan in flocculation of Bacillus sp. BioSol021 cultivation broth intended for biocontrol applications. Experiments were performed based on L₁₈ standard Taguchi orthogonal array design with five input parameters (chitosan type and dosage, pH value, rapid and slow mixing rates). In this study, the grey relational analysis was used to perform multi-objective optimization of the chosen responses, i.e., flocculation efficiency and four inhibition zone diameters against the selected phytopathogens. The results have indicated a great potential of a highly efficient method for removal of the Bacillus bacteria from the cultivation broth using chitosan. The good flocculation efficiency and high precipitate antimicrobial activity against the selected phytopathogens were achieved. It has been shown that multiple flocculation performance parameters were improved, resulting in slightly improved response values.

Food industry effluents represent one of the major concerns when it comes to environmental impact; hence, their valorization through different chemical and biological routes has been suggested as a possible solution. The vast amount of organic and inorganic nutrients present in food industry effluents makes them suitable substrates for microbial growth. This study suggests two valorization routes for whey as dairy industry effluent and flotation wastewater from the wine industry through microbial conversion to biocontrol agents as value-added products. Cultivations of the biocontrol strain Bacillus sp. BioSol021 were performed in a 16 L bioreactor to monitor the bioprocess course and investigate bioprocess kinetics in terms of microbial growth, sugar substrate consumption and surfactin synthesis, as an antimicrobial lipopeptide. The produced biocontrol agents showed high levels of biocontrol activity against mycotoxigenic strains of Aspergillus flavus, followed by a significant reduction of sugar load of the investigated effluents by the producing microorganisms. With proven high potential of whey and winery flotation wastewater to be used as substrates for microbial growth, this study provides grounds for further optimization of the suggested valorization routes, mostly in terms of bioprocess conditions to achieve maximal techno-economical feasibility, energy saving and maximal reduction of effluents’ organic and inorganic burden.

Search for more environment-friendly methods for controlling plant diseases that would contribute to the goal of sustainability in agriculture is in focus. In the present study, the potential of Streptomyces hygroscopicus isolated from soil sample in the production of biocontrol agents, nigericin and niphimycin, effective against Alternaria alternata storage apple pathogen was examined. Also, modelling and optimization of medium composition for biocontrol agent biosynthesis was performed. The results showed that the optimum amount of C₃H₈O₃, (NH₄)₂SO₄ and K₂HPO₄ in the medium for Streptomyces hygroscopicus biosynthesis is 20, 0.25 and 1.46 g/L, respectively. Scale-up and validation of the obtained results performed in the 3 L laboratory-scale bioreactor showed that on the optimized medium at an aeration rate of 0.7 vvm and an agitation speed of 200 rpm, produced nigericin and niphimycin, showed high activity. Under the same conditions, cultivation of S. hygroscopicus was performed in a 7 L laboratory bioreactor in a medium with waste glycerol instead of pure glycerol. Results showed that the methanol extract of S. hygroscopicus cultivation liquid, containing nigericin and niphimycin, was high effective against two Alternaria isolates. This was confirmed in vitro by obtaining large inhibition zone diameters on A. alternata KA10 (47 mm) and T1Jg₃ (44.33 mm) isolates. After successful in vitro analysis, in planta testing was performed. It was found that necrosis diameters that were measured on artificially inoculated apple fruits with A. alternata compared to necrosis diameter measured on untreated, control fruits, were 4.47 and 3.56 times smaller. GRAPHICAL ABSTRACT

Black rot, caused by Xanthomonas campestris pv. campestris, is one of the most important diseases of cruciferous crops which causes significant yield losses. Biological control of black rot by microbial biocontrol agents represents a promising alternative to chemical treatments and good agricultural practices which show only limited success. This study was carried out to assess a potential of different antagonists, including Bacillus, Pseudomonas, Lactobacillus, Streptomyces, Saccharomyces and Trichoderma genera, for biological control of black rot. Cultivation broth samples and their filtrates were examined against seven X. campestris strains, isolated from diseased cruciferous plants, using the diffusion-disc method. Bacillus velezensis has showed the highest inhibition zone diameter of 35.62?3.76 mm. Afterwards, different combinations of carbon and nitrogen sources were used in the cultivation medium to maximize antagonistic activity of B. velezensis. The best combinations were glycerol and yeast extract, lactose and peptone, as well as sucrose and yeast extract, suggesting the potential of biodiesel, dairy and sugar industry effluents in the production of bioactive compounds effective against the black rot pathogen. The validation experiment was performed in a laboratory-scale bioreactor, in order to investigate bioprocess kinetics of biomass growth and carbon source consumption, using the cultivation medium containing the optimal carbon and nitrogen source.

Photodegradation of azo dyes from industrial wastewater is challenging due to their high stability and resistance to removal. In this study, a generalized predictive model for photodegradation behavior of TiO2 containing ZnAl layered double hydroxide (LDH) based materials in the removal process of cationic azo dyes (Rhodamine B and Methylene Blue) was proposed. The performed kinetic investigation suggested good correlation of the experimental results with theoretical settings and revealed that all photocatalysts in both photocatalytic removal reactions followed the pseudo-first order Langmuir-Hinshelwood reaction model. The inputs for artificial neural network (ANN) included four experimental variables: TiO2 loading onto LDHs, organic dye type used for the removal process, temperature of thermal treatment of photocatalysts and reaction time, whereas for the two ANN prediction outputs removal efficiency and photo-degradation rate constants were used. The optimal topology was determined to be a three-layer feed-forward ANN with 3 input neurons and 10 hidden neurons, 3-10-1.