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Please use this identifier to cite or link to this item: http://hdl.handle.net/123456789/11071

Title: Optimization of Enzyme-Assisted Hydrothermal Extraction of Shea Butter from Traditionally Pre-Treated Shea Kernels
Authors: Didia, Beauty
Issue Date: 20-Feb-2018
Abstract: Enzymes can be simultaneously used to selectively release targeted fats from pretreated Shea nut kernels and improve extraction efficiency at optimized conditions. Three different pre-treated Shea nut substrates: raw kernels, roasted kernels and Shea nut paste from roasted kernels were subjected to treatment with three different industrial enzymes: lipases, pectinases and cellulases, separately and in combination, for Shea butter extraction. The process for enzyme mixtures was optimized at pH of 6, 3 % Enzyme-Substrate concentration, 2 hour hydrolysis time at a temperature of 60 ºC. The results showed that Shea nut paste yielding 48 % fat with pectinases (P), 52% with lipases (L) and 46% with cellulases (C). The amount of percentage fat increased to 52, 54 and 56 at 1:1 P+C, L+C and P+L enzyme combinations respectively. The highest extraction efficiency of 69.96% was recovered from 1:1:1 combination of all three industrial enzymes. The free fatty acids and the peroxide value of the Shea butter recovered ranged from 1.6 ± 0.5 – 2.7 ± 0.1 mgKOH/kg and 3.6 ± 0.6 – 5.4 ± 0.2 mEq/kg respectively. Thus enzyme assisted aqueous hydrolysis of Shea nut biomass for Shea butter production is a promising technology capable of eliminating the drudgery with the mechanical extraction, the safety concerns with the chemical methods and the arduous and cumbersome nature of the traditional village extraction methods. To make this process financially practical, further work is suggested to minimize cost with applications of modified mixes of crude enzymes.
Description: A Thesis submitted to the Department of Biochemistry and Biotechnology, Faculty of Biosciences, College of Science in partial fulfilment of the requirements for the degree of Master of Science in Biotechnology, 2017
URI: http://hdl.handle.net/123456789/11071
Appears in Collections:College of Science

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