An artificial neuronal network coupled with a genetic algorithm to optimise the production of unsaturated fatty acids in Parachlorella kessleri

Abstract

In this study, an Artificial Neural Network-Genetic Algorithm (ANN-GA) approach was successfully applied to optimise the physicochemical factors influencing the synthesis of unsaturated fatty acids (UFAs) in the microalgae P. kessleri UCM 001. The optimized model recommended specific cultivation conditions, including glucose at 29 g/L, NaNO₃ at 2.4 g/L, K₂HPO₄ at 0.4 g/L, red LED light, an intensity of 1000 lx, and an 8:16-h light-dark cycle. Through ANN-GA optimisation, a remarkable 66.79% increase in UFAs production in P. kessleri UCM 001 was achieved, compared to previous studies. This underscores the potential of this technology for enhancing valuable lipid production. Sequential variations in the application of physicochemical factors during microalgae culture under mixotrophic conditions, as optimized by ANN-GA, induced alterations in UFAs production and composition in P. kessleri UCM 001. This suggests the feasibility of tailoring the lipid profile of microalgae to obtain specific lipids for diverse industrial applications. The microalgae were isolated from a high-mountain lake in Colombia, highlighting their adaptation to extreme conditions. This underscores their potential for sustainable lipid and biomaterial production. This study demonstrates the effectiveness of using ANN-GA technology to optimise UFAs production in microalgae, offering a promising avenue for obtaining valuable lipids. The microalgae's unique origin in a high-mountain environment in Colombia emphasises the importance of exploring and harnessing microbial resources in distinctive geographical regions for biotechnological applications.