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<p>Chitinase is an industrially important enzyme with applications in agriculture, biotechnology, waste management and pharmaceuticals. Preliminary screening was conducted to identify a suitable chitinase-producing isolate and the study primarily focused on optimizing chitinase production from the selected isolate using statistical and computational approaches. Out of 22 isolates, BSUC-16, which had a chitinase activity of approximately 40&#xa0;mU/mL was selected. The strain was identified as <i>Stenotrophomonas maltophilia</i> BSUC-16 and its production was enhanced through an integrated optimization strategy. The One-Factor-At-a-Time (OFAT) approach increased the chitinase activity to 165&#xa0;mU/mL, while Plackett–Burman Design (PBD) optimization raised it further to 280&#xa0;mU/mL by identifying Na<sub>2</sub>HPO<sub>4</sub>, colloidal chitin, and yeast extract as significant factors (&gt; 95% CI). Experimental Central Composite Design (CCD) optimization of these variables demonstrated excellent model reliability (R<sup>2</sup> = 0.9852, 380&#xa0;mU/mL) and the Response surface Methodology (RSM) predicted a response of 369&#xa0;mU/mL. The optimization strategy resulted in 9.51-fold increase in chitinase production. Model validation using Artificial Neural Network (ANN)- Levenberg–Marquardt (LM) (R<sup>2</sup> = 0.9933; 376&#xa0;mU/mL) confirmed strong predictive accuracy, with all predictions closely matching CCD results. Visualization through 3D cube, surface, and contour plots demonstrated the interaction influence of the variables on chitinase production. Overall, this framework provides a robust and validated platform for enhancing microbial chitinase production.</p>

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Optimization of chitinase production by Stenotrophomonas maltophilia BSUC-16: A statistical approach with validation using artificial neural network

  • 期刊论文

  • 发表日期：

    2026年8月3日

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  • Bushrabanu U. Shaikh,
  • Urvish D. Chhaya

摘要

Chitinase is an industrially important enzyme with applications in agriculture, biotechnology, waste management and pharmaceuticals. Preliminary screening was conducted to identify a suitable chitinase-producing isolate and the study primarily focused on optimizing chitinase production from the selected isolate using statistical and computational approaches. Out of 22 isolates, BSUC-16, which had a chitinase activity of approximately 40 mU/mL was selected. The strain was identified as Stenotrophomonas maltophilia BSUC-16 and its production was enhanced through an integrated optimization strategy. The One-Factor-At-a-Time (OFAT) approach increased the chitinase activity to 165 mU/mL, while Plackett–Burman Design (PBD) optimization raised it further to 280 mU/mL by identifying Na2HPO4, colloidal chitin, and yeast extract as significant factors (> 95% CI). Experimental Central Composite Design (CCD) optimization of these variables demonstrated excellent model reliability (R2 = 0.9852, 380 mU/mL) and the Response surface Methodology (RSM) predicted a response of 369 mU/mL. The optimization strategy resulted in 9.51-fold increase in chitinase production. Model validation using Artificial Neural Network (ANN)- Levenberg–Marquardt (LM) (R2 = 0.9933; 376 mU/mL) confirmed strong predictive accuracy, with all predictions closely matching CCD results. Visualization through 3D cube, surface, and contour plots demonstrated the interaction influence of the variables on chitinase production. Overall, this framework provides a robust and validated platform for enhancing microbial chitinase production.