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Artificial Neural Networks and Response Surface Methodology approach for optimization of an eco-friendly and detergent-stable lipase production from Actinomadura keratinilytica strain Cpt29

Noura Semache, Fatiha Benamia, Bilal Kerouaz, Inès Belhaj, Selma Bounour, Hafedh Belghith, Ali Gargouri, Ali Ladjama, Zeineddine Djeghaba

Abstract


This work mainly focused on the production of an efficient, economical, and eco-friendly lipase (AKL29) from Actinomadura keratinilytica strain Cpt29 isolated from poultry compost in north east of Algeria, for use in detergent industries. AKL29 shows a significant lipase activity (45 U/mL) towards hydrolyzed triacylglycerols, indicating that it is a true lipase. For maximum lipase production the modeling and optimization of potential culture parameters such as incubation temperature, cultivation time, and Tween 80 (v/v) were built using RSM and ANN approaches. The results show that both the two models provided good quality predictions, yet the ANN showed a clear superiority over RSM for both data fitting and estimation capabilities. A 4.1-fold increase in lipase production was recorded under the following optimal condition: incubation temperature (37.9 °C), cultivation time (111 h), and Tween 80 (3.27%, v/v). Furthermore, the partially purified lipase showed good stability, high compatibility, and significant wash performance with various commercial laundry detergents, making this novel lipase a promising potential candidate for detergent industries.


Keywords


Lipase; Actinomadura keratinilytica; Optimization; RSM; ANN; Detergent.

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DOI: http://dx.doi.org/10.17344/acsi.2020.6401

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Copyright (c) 2020 Noura Semache, Fatiha Benamia, Bilal Kerouaz, Inès Belhaj, Selma Bounour, Hafedh Belghith, Ali Gargouri, Ali Ladjama, Zeineddine Djeghaba

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