Statistical Optimization of β-galactosidase production from Newly Isolated Bacillus licheniformis 17KAN-M3 Strain | ||
| Egyptian Journal of Chemistry | ||
| Volume 68, Issue 8, August 2025, Pages 341-351 PDF (1.29 M) | ||
| Document Type: Original Article | ||
| DOI: 10.21608/ejchem.2025.327019.10603 | ||
| Authors | ||
| Sara A. Kassem1; Shireen A.A. Saleh1; mohamed Ahmed Abdel-Naby1; Naziha M. Hassanein2; Samia Abdel-Aziz Ahmed* 1 | ||
| 1Chemistry of Natural and Microbial Products Department, National Research Centre, Dokki, Giza, Egypt | ||
| 2Microbiology Department, Faculty of Science, Ain Shams University, Abbaseyya, 1156 Cairo, Egypt | ||
| Abstract | ||
| β-galactosidase (EC 3.2.1.23) is the key enzyme in the dairy sector regarding the production of low-lactose foods to treat lactose intolerance. The present study aimed to identify the most effective bacterial isolates from different dairy product samples and screened for β- galactosidase (β gal) production. Among thirteen bacterial isolates, the highest β-gal production (2245 U/ml) was obtained by isolate no.6 which was isolated from fresh cheese. The potent isolate was identified and submitted to the gene bank as Bacillus licheniformis strain 17KAN-M3 (accession number OR742338). Some fermentation parameters were optimized separately via one–variable-at-a-time (OVAT) including incubation time, carbon sources, incubation temperature, initial pH, and inoculation level. Statistical factorial designs including a series of statistical designs, such as the Plackett–Burman design (PBd) and Central Composite design (CCd) was used to optimize the fermentation medium composition. Using the revised medium, the productivity of β-gal from Bacillus licheniformis 17KAN-M3 (14048 U/ml) was 6.26-fold higher than that produced from the basal medium. Since its ability to produce β-gal was greatly improved by RSM, Bacillus licheniformis strain 17KAN-M3 could serve as a potential source of β-gal for industrial-scale food applications. | ||
| Keywords | ||
| β-galactosidase; optimization; response surface methodology; milk whey | ||
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