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AuthorOusaadi M.I.
AuthorMerouane F.
AuthorBerkani M.
AuthorAlmomani F.
AuthorVasseghian Y.
AuthorKitouni M.
Available date2022-04-25T08:00:14Z
Publication Date2021
Publication NameEnvironmental Research
ResourceScopus
Identifierhttp://dx.doi.org/10.1016/j.envres.2021.111494
URIhttp://hdl.handle.net/10576/30270
AbstractThis 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.
Languageen
PublisherAcademic Press Inc.
SubjectArtificial neural network
Genetic algorithm
Orange waste
Response surface methodology
Streptomyces sp. (20r)
α-amylase
TitleValorization and optimization of agro-industrial orange waste for the production of enzyme by halophilic Streptomyces sp.
TypeArticle
Volume Number201
dc.accessType Abstract Only


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