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J Lipids. 2018 Feb 14;2018:5659683. doi: 10.1155/2018/5659683. eCollection 2018.

Realm of Thermoalkaline Lipases in Bioprocess Commodities.

Author information

1
Department of Bioprocess Technology, Faculty of Biotechnology and Biomolecular Sciences, Universiti Putra Malaysia, 43400 Serdang, Malaysia.
2
Bioprocessing and Biomanufacturing Research Center, Faculty of Biotechnology and Biomolecular Sciences, Universiti Putra Malaysia, 43400 Serdang, Malaysia.

Abstract

For decades, microbial lipases are notably used as biocatalysts and efficiently catalyze various processes in many important industries. Biocatalysts are less corrosive to industrial equipment and due to their substrate specificity and regioselectivity they produced less harmful waste which promotes environmental sustainability. At present, thermostable and alkaline tolerant lipases have gained enormous interest as biocatalyst due to their stability and robustness under high temperature and alkaline environment operation. Several characteristics of the thermostable and alkaline tolerant lipases are discussed. Their molecular weight and resistance towards a range of temperature, pH, metal, and surfactants are compared. Their industrial applications in biodiesel, biodetergents, biodegreasing, and other types of bioconversions are also described. This review also discusses the advance of fermentation process for thermostable and alkaline tolerant lipases production focusing on the process development in microorganism selection and strain improvement, culture medium optimization via several optimization techniques (i.e., one-factor-at-a-time, surface response methodology, and artificial neural network), and other fermentation parameters (i.e., inoculums size, temperature, pH, agitation rate, dissolved oxygen tension (DOT), and aeration rate). Two common fermentation techniques for thermostable and alkaline tolerant lipases production which are solid-state and submerged fermentation methods are compared and discussed. Recent optimization approaches using evolutionary algorithms (i.e., Genetic Algorithm, Differential Evolution, and Particle Swarm Optimization) are also highlighted in this article.

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