Lignocellulosic resources are naturally renewable, low-priced, and abundant. They comprise three kinds of polymers, cellulose, hemicelluloses, and lignin, bonded to each other with the help of covalent cross-linking and non-covalent forces. Cellulose is the world's most abundant organic material and the chief constituent of biomass energy. Moreover, production of calluses from Bacillus valezensis strain 2H was statistically optimized by substrate carrot in submerged fermentation using Response Surface Methodology (RSM) and Artificial Neural Network (ANN). The RSM and ANN were used to successfully optimize Carboxymethyl cellulase (CMCase) and Filter paper (FPase). Moreover, RSM was utilized for optimization of the target enzyme using a newly identified Bacillus velezensis strain. Plackett-Burman design (PBD) was adopted for screening of the most significant factors affecting cellulase production. Central composite design (CCD) was utilized to optimize enzyme production. Biochemical characterization revealed that both enzymes were optimally active under acidic conditions (pH 6.0), with the optimum temperature observed to be 60 °C, and stability was maintained up to 60 °C. The tested metal ions affected CMCase and FPase activities to varying degrees but did not enhance enzyme activity compared with the control. Similarly, the tested organic solvents generally decreased enzyme activities, although the extent of inhibition varied depending on the enzyme and solvent. R2 values for the CMCase were 95.43% and for FPase 99.58%, calculated by RSM analysis. R2 values for CMCase were 91.44% (Overall R = 0.95622), and for FPase were 53.70% (Overall R = 0.7328), calculated by ANN analysis. Our results showed that the RSM and ANN models provided different predictive performances for CMCase and FPase. This study elucidates the potential of Bacillus velezensis for efficient cellulase production under optimized submerged fermentation conditions.
Citation: Iram Yasmeen, Muhammad Zubair Ali, Sri Rahayu, Hafiz Abdullah Shakir, Muhammad Khan, Yuliya Kulikova, Olga Babich, Muhammad Irfan, Trisanti Anindyawati. Cellulase production, optimization and characterization from Bacillus velezensis 2H using carrot waste as a substrate; response surface methodology (RSM) and Artificial Neural Network (ANN) modeling[J]. AIMS Microbiology, 2026, 12(3): 742-773. doi: 10.3934/microbiol.2026032
Lignocellulosic resources are naturally renewable, low-priced, and abundant. They comprise three kinds of polymers, cellulose, hemicelluloses, and lignin, bonded to each other with the help of covalent cross-linking and non-covalent forces. Cellulose is the world's most abundant organic material and the chief constituent of biomass energy. Moreover, production of calluses from Bacillus valezensis strain 2H was statistically optimized by substrate carrot in submerged fermentation using Response Surface Methodology (RSM) and Artificial Neural Network (ANN). The RSM and ANN were used to successfully optimize Carboxymethyl cellulase (CMCase) and Filter paper (FPase). Moreover, RSM was utilized for optimization of the target enzyme using a newly identified Bacillus velezensis strain. Plackett-Burman design (PBD) was adopted for screening of the most significant factors affecting cellulase production. Central composite design (CCD) was utilized to optimize enzyme production. Biochemical characterization revealed that both enzymes were optimally active under acidic conditions (pH 6.0), with the optimum temperature observed to be 60 °C, and stability was maintained up to 60 °C. The tested metal ions affected CMCase and FPase activities to varying degrees but did not enhance enzyme activity compared with the control. Similarly, the tested organic solvents generally decreased enzyme activities, although the extent of inhibition varied depending on the enzyme and solvent. R2 values for the CMCase were 95.43% and for FPase 99.58%, calculated by RSM analysis. R2 values for CMCase were 91.44% (Overall R = 0.95622), and for FPase were 53.70% (Overall R = 0.7328), calculated by ANN analysis. Our results showed that the RSM and ANN models provided different predictive performances for CMCase and FPase. This study elucidates the potential of Bacillus velezensis for efficient cellulase production under optimized submerged fermentation conditions.
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Figures(18) / Tables(7)
Iram Yasmeen, Muhammad Zubair Ali, Sri Rahayu, Hafiz Abdullah Shakir, Muhammad Khan, Yuliya Kulikova, Olga Babich, Muhammad Irfan, Trisanti Anindyawati. Cellulase production, optimization and characterization from Bacillus velezensis 2H using carrot waste as a substrate; response surface methodology (RSM) and Artificial Neural Network (ANN) modeling[J]. AIMS Microbiology, 2026, 12(3): 742-773. doi: 10.3934/microbiol.2026032
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