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.
5 affiliations
- 1Department of Biotechnology, University of Sargodha, Sargodha, Pakistan.
- 2Biology Department, Faculty of Mathematics & Natural Sciences, Universitas Negeri Jakarta, Jakarta, Indonesia.
- 3Institute of Zoology, University of the Punjab, New Campus, Lahore, Pakistan.
- 4Research and Education Center of Industrial Biotechnologies, Immanuel Kant Baltic Federal University, Kaliningrad, Russia.
- 5Research Center for Applied Microbiology, National Research and Innovation Agency, Jl. Raya Bogor KM 46, Cibinong, Bogor 16911, West Java, Indonesia.
Abstract
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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BibTeX
@article{yasmeen2026cellulase,
title = {{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.}},
author = {Iram Yasmeen and Muhammad Zubair Ali and Sri Rahayu and Hafiz Abdullah Shakir and Muhammad Khan and Yuliya Kulikova and Olga Babich and Muhammad Irfan and Trisanti Anindyawati},
journal = {AIMS microbiology},
year = {2026},
month = oct,
doi = {10.3934/microbiol.2026032},
url = {https://europepmc.org/article/MED/42959700},
note = {CC BY},
}
RIS
TY - JOUR
TI - 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.
AU - Iram Yasmeen
AU - Muhammad Zubair Ali
AU - Sri Rahayu
AU - Hafiz Abdullah Shakir
AU - Muhammad Khan
AU - Yuliya Kulikova
AU - Olga Babich
AU - Muhammad Irfan
AU - Trisanti Anindyawati
PY - 2026
DA - 2026/10/14
JO - AIMS microbiology
DO - 10.3934/microbiol.2026032
UR - https://europepmc.org/article/MED/42959700
AB - 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.
SN - 2471-1888
ER -