Artificial intelligence-based approaches for modeling the effects of spirulina growth mediums on total phenolic compounds |
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Authors: | Wubshet Asnake Metekia Abdullahi Garba Usman Beyza Hatice Ulusoy Sani Isah Abba Kefyalew Chirkena Bali |
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Institution: | 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 |
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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. |
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Keywords: | Spirulina Growth medium Phenolic compound Artificial intelligence |
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