Artificial intelligence driven advanced optimization strategy vis-à-vis response surface optimization of production medium: Bacterial exopolysaccharide production as a case-study

Abstract

In the present study, Artificial Intelligence (AI) driven multivariate-optimization strategy, hybrid-support vector machine (h-SVM) was employed and compared with conventional response surface methodology (RSM) in terms of its performance efficiency to optimize the medium composition for improved exopolysaccharide (EPS) production by a thermophilic isolate of Bacillus licheniformis. Firstly, the application of Plackett-Burman design and analysis indicated that sucrose, KH2PO4 and NaCl have significant positive effects, whereas NaNO3 showed a significant negative effect on EPS production. Then, experimental data of a central-composite design was leveraged by RSM and the h-SVM to optimize these significant variables. SVM showed better performance in data modeling (R2 = 0.9874, Root-mean-square error = 0.693) compared to conventional RSM. Subsequently, the SVM model was coupled with optimization algorithms, Teaching–learning-based optimization (TLBO) and Particle swarm optimization (PSO). As a result, a maximum EPS concentration of 64 ± 1.1 g L‐ was obtained from h-SVM predicted model (SVM-PSO, SVM-TLBO), which is about a 5-fold increase in EPS production to that obtained with the un-optimized medium. Within our knowledge, this is the first report on the implementation of SVM-TLBO to improve the production of bioproducts like bacterial EPS while comparing it with other techniques like SVM-PSO and RSM.