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<p>Indole-3-acetic acid (IAA) is essential in abiotic stress tolerance via signalling molecules between plants and microorganisms, contributing to sustainable agricultural practices. The present study investigates a combinatorial artificial neural network (ANN) modelling and particle swarm optimisation (PSO) algorithm to optimise the process parameters for enhanced IAA production by <i>Enterobacter hormaechei</i> APSB3. Hence, to improve IAA production, single-factor experiments and a design matrix generated by central composite design were employed to explore the significant input variables, including temperature, pH, carbon source, and nitrogen source, which were subsequently validated through the application of ANN-PSO. Thus, under the optimised ANN-PSO conditions, i.e. carbon source (2.11%), nitrogen source (2.37%), pH (9), and temperature (45&#xa0;℃), IAA production was improved to 94.76 ± 0.03&#xa0;µg/mL (2.90-fold) as compared to un-optimised condition (33.04 ± 0.58&#xa0;µg/mL). The IAA production was further confirmed by TLC and HPLC analyses, exhibiting an Rf value of 0.77 and a retention time of 3.301&#xa0;min. Thus, the present work could conclude that the hybrid heuristic ANN-PSO, an empirical and decision-making tool, significantly improves efficiency and scalability for IAA production by <i>E. hormaechei</i> APSB3.</p>

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Enhanced indole-3-acetic acid production by Enterobacter hormaechei APSB3 through heuristic artificial neural network and particle swarm optimisation

  • 期刊论文

  • 发表日期：

    2025年5月20日

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  • Aditya Sharma,
  • Guddu Kumar Gupta,
  • Deepak Chhabra,
  • Piyush Pandey,
  • Pratyoosh Shukla

摘要

Indole-3-acetic acid (IAA) is essential in abiotic stress tolerance via signalling molecules between plants and microorganisms, contributing to sustainable agricultural practices. The present study investigates a combinatorial artificial neural network (ANN) modelling and particle swarm optimisation (PSO) algorithm to optimise the process parameters for enhanced IAA production by Enterobacter hormaechei APSB3. Hence, to improve IAA production, single-factor experiments and a design matrix generated by central composite design were employed to explore the significant input variables, including temperature, pH, carbon source, and nitrogen source, which were subsequently validated through the application of ANN-PSO. Thus, under the optimised ANN-PSO conditions, i.e. carbon source (2.11%), nitrogen source (2.37%), pH (9), and temperature (45 ℃), IAA production was improved to 94.76 ± 0.03 µg/mL (2.90-fold) as compared to un-optimised condition (33.04 ± 0.58 µg/mL). The IAA production was further confirmed by TLC and HPLC analyses, exhibiting an Rf value of 0.77 and a retention time of 3.301 min. Thus, the present work could conclude that the hybrid heuristic ANN-PSO, an empirical and decision-making tool, significantly improves efficiency and scalability for IAA production by E. hormaechei APSB3.