Application of an artificial neural network coupled to a genetic algorithm for the production of polyphenols in Parachlorella kessleri grown under mixotrophic conditions

Abstract

Microalgae are vital sources of high-value compounds, like polyphenols. Boosting polyphenol production requires considering diverse factors and their impacts on synthesis. This study aims to optimise polyphenol yield in microalga Parachlorella kessleri using an artificial neural network coupled with a genetic algorithm (ANN-GA). P. kessleri was grown under mixotrophic conditions and subjected to 13 experimental treatments. Factors studied included glucose, NaNO3, KH2PO4, LED light wavelength, intensity, and photoperiod. Total polyphenol concentration was quantified with Folin-Ciocalteu reagent; the polyphenol profile was obtained via HPLC. A database was constructed for ANN training to predict polyphenol production. Developed models were compared using indicators: coefficient of determination (R2pred), Mean Absolute Error (MAE), and Mean Square Error (MSE). Optimal models were fine-tuned with a genetic algorithm and validated using randomised experiments. The optimal ANN architecture for P. kessleri polyphenol production is: 6 inputs, 4 hidden layers, 6 neurons per layer, one output. This design achieved high prediction accuracy (R2 = 0.93). The genetically algorithm-optimised ANN model has these conditions: 26.60 g/L glucose; 1.42 g/L NaNO3; 0.59 g/L KH2PO4; white light; 1000 lx light intensity; 12:12 photoperiod. In experimental validation, 28 mg gallic acid equivalents per gram dry weight (GAE/g DW) were obtained, surpassing all models. An ANN-GA optimisation procedure was used for the first time for polyphenol production in P. kessleri. The model under mixotrophic conditions showed higher polyphenol production than autotrophic conditions. Polyphenol composition in P. kessleri varied between autotrophic and mixotrophic cultures, indicating inducible polyphenol production. The ANN-GA approach for polyphenol production in P. kessleri proved effective, with potential for maximising other high-value microalgae metabolites.