Abstract
Spirodela. polyrhiza as one of the potential feedstocks for industrial natural dye and pharmaceutical products, efficient process to produce from it were scarce. Here, Artificial Neural Network (ANN) and Genetic Algorithm (GA) were employed for modelling and optimization respectively. Current study focus on establishing useable models and anticipating the optimal concentration of nitrate (NO3−), phosphate (PO43−) and ammonium (NH4+) to give highest relative growth rate (RGR) and produce maximum amount of anthocyanins, chlorophylls, phenolic compounds and antioxidants. In short, 12 and 11 neurons in hidden layer were found to be most suitable neuron numbers used for the modelling of growth and secondary metabolites respectively. All the models exhibited a high degree of robustness and performance with a high r2 value (higher than 0.75) and a low mean squared error (lower than 0.05). GA also anticipated the best concentration of macronutrients to achieve the maximum growth and metabolites production with significant low errors (below 7 %). The employment of machine learning was useful for such applications.
Published Version
Talk to us
Join us for a 30 min session where you can share your feedback and ask us any queries you have
Schedule a callDisclaimer: All third-party content on this website/platform is and will remain the property of their respective owners and is provided on "as is" basis without any warranties, express or implied. Use of third-party content does not indicate any affiliation, sponsorship with or endorsement by them. Any references to third-party content is to identify the corresponding services and shall be considered fair use under The CopyrightLaw.
