NDLI: Response surface methodology for the optimization of beta-lactoglobulin nano-liposomes

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Food & Function Vol - 5 Issue - 3

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Response surface methodology for the optimization of beta-lactoglobulin nano-liposomes

Content Provider	Royal Society of Chemistry (RSC)
Author	Wang, Yanbo Chen, Xiaoqiang Ye, Xingqian Ma, Jieqing Guan, Rongfa Hao, Yunbin Liu, Mingqi
Copyright Year	2014
Abstract	Response surface methodology based on a central composite rotatable design has been successfully used to model and optimize biochemical and biotechnological processes. Heat treatment could have an effect on beta-lactoglobulin (β-Lg) and lead to allergic reaction. To reduce this phenomenon, liposomes were used as carriers in this research. The mass ratio of phosphatidylcholine and cholesterol (2.5–12.5), β-Lg concentration (2.5–12.5 mg mL−1), sonication time (5–25, min) and temperature (25–40 °C) were selected as independent variables with encapsulation efficiency as a dependent variable. For each response, a second-order polynomial model was developed using multiple linear regression analysis. Applying a desirability function method the optimum parameters were: phosphatidylcholine to cholesterol ratio of 8.05, β-Lg concentration of 9.09 mg mL−1, sonication time of 17.71 min and temperature of 30 °C. The particle size and encapsulation efficiency were found to be 189 nm and 61.55%, respectively. Furthermore, the in vitro stability of β-Lg nano-liposomes in simulated gastrointestinal juice was evaluated. The nano-liposomes showed an acceptable stability in simulated gastrointestinal juice at 37 °C for 4 h.
Starting Page	748
Ending Page	754
Page Count	7
File Format	HTM / HTML PDF
ISSN	20426496
Volume Number	5
Issue Number	4
Journal	Food & Function
DOI	10.1039/c3fo60476d
Language	English
Publisher	Royal Society of Chemistry
Access Restriction	Open
Subject Keyword	Response surface methodology Beta-lactoglobulin Phosphatidylcholine Cholesterol Sonication Dependent and independent variables Time complexity Linear regression Regression analysis
Content Type	Text
Resource Type	Article
Subject	Medicine Food Science

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