Valorization and optimization of agro-industrial orange waste for the production of enzyme by halophilic Streptomyces sp
| Autor: | Mohammed Berkani, Fateh Merouane, M. Kitouni, Yasser Vasseghian, Mouna Imene Ousaadi, Fares Almomani |
|---|---|
| Rok vydání: | 2021 |
| Předmět: |
Artificial neural network
Central composite design Sodium chemistry.chemical_element Industrial Waste Orange (colour) 010501 environmental sciences 01 natural sciences Biochemistry Streptomyces 03 medical and health sciences 0302 clinical medicine Response surface methodology 030212 general & internal medicine Amylase 0105 earth and related environmental sciences General Environmental Science biology Orange waste biology.organism_classification Pulp and paper industry Streptomyces sp. (20r) Halophile Culture Media α-amylase chemistry Genetic algorithm Yield (chemistry) biology.protein Neural Networks Computer Citrus sinensis |
| Zdroj: | Environmental research. 201 |
| ISSN: | 1096-0953 |
| Popis: | This study underlines the biotechnical valorization of the accumulated and unusable remains of agro-industrial orange fruit peel waste to produce α-amylase under submerged conditions by Streptomyces sp. KP314280 (20r). The response surface methodology based on central composite design (RSM-CCD) and artificial neural network coupled with a genetic algorithm (ANN-GA) were used to model and optimize the conditions for the α-amylase production. Four independent variables were evaluated for α-amylase activity including substrate concentration, inoculum size, sodium chloride powder (NaCl), and pH. A ten-fold cross-validation indicated that the ANN has a greater ability than the RSM to predict the α-amylase activity (R2ANN = 0.884 and R2RSM = 0.725). The analysis of variance indicated that the aforementioned four factors significantly affected the α-amylase activity. Additionally, the α-amylase production experiments were conducted according to the optimal conditions generated by the GA. The results indicated that the amylase yield increased by 4-fold. Moreover, the α-amylase production (12.19 U/mL) in the optimized medium was compatible with the predicted conditions outlined by the ANN-GA model (12.62 U/mL). As such, the ANN and GA combination is optimizable for α-amylase production and exhibits an accurate prediction which provides an alternative to other biological applications. |
| Databáze: | OpenAIRE |
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