Artificial intelligence-based approaches for modeling the effects of spirulina growth mediums on total phenolic compounds

Wubshet Asnake Metekia; Abdullahi Garba Usman; Beyza Hatice Ulusoy; Sani Isah Abba; Kefyalew Chirkena Bali

doi:10.1016/j.sjbs.2021.09.055

Artificial intelligence-based approaches for modeling the effects of spirulina growth mediums on total phenolic compounds

Saudi J Biol Sci. 2022 Feb;29(2):1111-1117. doi: 10.1016/j.sjbs.2021.09.055. Epub 2021 Sep 22.

Authors

Wubshet Asnake Metekia^{1

2}, Abdullahi Garba Usman³, Beyza Hatice Ulusoy¹, Sani Isah Abba⁴, Kefyalew Chirkena Bali¹

Affiliations

¹ Near East University, Faculty of Veterinary Medicine, Food Hygiene and Technology Department, Near East Boulevard, ZIP: 99138 Nicosia, Cyprus.
² Ethiopian Ministry of Agriculture, Fishery Development Directorate, PO Box 62347, Addis Ababa, Ethiopia.
³ Near East University, Faculty of Pharmacy, Department of Analytical Chemistry, Nicosia, Cyprus.
⁴ Baze University, Civil Engineering, Abuja, Nigeria.

Abstract

Spirulina is a microalga and its phenolic compound is affected by growth mediums. In this study, Artificial intelligence (AI) based models, namely the Adaptive-Neuro Fuzzy Inference System (ANFIS) and Multilayer perceptron (MLP) models, and Step-Wise-Linear Regression (SWLR) were used to predict total phenolic compounds (TPC) of the spirulina algae. Spirulina productivity (P), extraction yield (EY), total flavonoids (TF), percent of flavonoid (%F) and percent of phenols (%P) are considered as input variables with the corresponding TPC as an output variable. From the result, TPC has a high positive correlation with the input variables with R = 0.99999. Also, the models showed that the ANFIS and SWLR gives superior result in the testing phase and increased its accuracy by 2% compared to MLP model in the prediction of TPC.

Keywords: Artificial intelligence; Growth medium; Phenolic compound; Spirulina.