Screening of extraction buffer concentration for protease extraction from the leaves of Syzygium polyanthum using full factorial design

This study aimed to identify the factors that significantly affect the extraction of protease from the leaves of Syzygium polyanthum. The extraction of protease from the leaves of Syzigium polyanthum was studied at different concentration of substances in the extraction buffer. A 24 full factorial e...

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Bibliographic Details
Main Authors: Noorhafiza Diyana, Yahya, Zatul Iffah, Mohd Arshad, Siti Kholijah, Abdul Mudalip, Siti Zubaidah, Sulaiman, Rohaida, Che Man, Shalyda, Md Shaarani @ Md Nawi, Mohd Hairul, Ab. Rahim
Format: Conference or Workshop Item
Language:English
Published: Institute of Physics Publishing 2020
Subjects:
Online Access:http://umpir.ump.edu.my/id/eprint/36011/1/Screening%20of%20extraction%20buffer%20concentration%20for%20protease%20extraction%20from%20the%20leaves%20of%20Syzygium%20polyanthum%20using%20full%20factorial%20design.pdf
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Summary:This study aimed to identify the factors that significantly affect the extraction of protease from the leaves of Syzygium polyanthum. The extraction of protease from the leaves of Syzigium polyanthum was studied at different concentration of substances in the extraction buffer. A 24 full factorial experimental design was performed. The effect of four independent operating factors including; A, the concentration of potassium phosphate (KPO4); B, the concentration of Triton X-100; C, the concentration of glycerol; and D, the concentration of dithiotreitol (DTT) on the protease activity were evaluated statistically. All regression models gave a good fit to the experimental data (R2 > 99%). The maximum protease activity was detected when the extraction was performed using 3.0 M KPO4, 1% Triton X-100, 80% glycerol and 1.5 M DTT. The results revealed that the concentration of KPO4, DTT, and their interaction were statistically significant to affect the protease activity. Thus, the results obtained from this study indicate that full factorial design is useful to screen the significant factors and minimise the number of experiments.