Mango seed, a waste material that is disposed of after consumption of mangos, was studied for its potential use as a resource for extraction of starch. The study revealed that mango seeds are a good source of starch: physicochemical characterisations confirmed that the extracted material was indeed a starch material. The starch was tested for use in textile applications and the results indicated that the material performed as well as a standard starch sample. The extraction of starch from mango seeds is facile and does not require sophisticated technology. Response surface methodology was used to optimise the extraction of starch from mango seeds. The experimental parameters optimised were concentration of mango seeds, extraction temperature, and extraction time, while the measured response factors were starch yield and whiteness of the starch. Thus the critical values for optimal whiteness of the extracted starch were calculated to be: concentration = 0.35 (%w/v); temperature = 26.74 oC; extraction time = 6.46 (hr). A techno-economic analysis of the starch extraction process showed that the technology is viable and could be taken up by SMMEs.
Reference:
Tesfaye, T. and Sithole, B.B. 2018. Valorisation of mango seeds via extractioof starch: Using response surface methodology to optimise the extraction process. Opportunities for Biomass and Organic Waste Valorisation: Finding Alternative Solutions to Disposal in South Africa, pp. 158-180
Tesfaye, T., & Sithole, B. B. (2018). Valorisation of mango seeds via extractioof starch: Using response surface methodology to optimise the extraction process., Worklist;17779 UNISA. http://hdl.handle.net/10204/10830
Tesfaye, T, and Bishop B Sithole. "Valorisation of mango seeds via extractioof starch: Using response surface methodology to optimise the extraction process" In WORKLIST;17779, n.p.: UNISA. 2018. http://hdl.handle.net/10204/10830.
Tesfaye T, Sithole BB. Valorisation of mango seeds via extractioof starch: Using response surface methodology to optimise the extraction process.. Worklist;17779. [place unknown]: UNISA; 2018. [cited yyyy month dd]. http://hdl.handle.net/10204/10830.
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