皮克林乳液
乳状液
纤维素酶
化学工程
化学
结晶度
膳食纤维
材料科学
纤维
纤维素
食品科学
有机化学
工程类
作者
Kaili Gao,Tongying Liu,Leipeng Cao,Yuhuan Liu,Qi Zhang,Roger R. Ruan,Shuoru Feng,Xiaodan Wu
摘要
Abstract BACKGROUND The application of Pickering emulsions stabilized by food‐derived particles is of great interest, studies have focused on development of natural functional emulsifiers from agricultural byproducts. Dietary fiber (DF) has been recognizing for its excellent physiological functions. Moreover, physicochemical properties of pomelo peel DF (PDF) make it a potential emulsifier. However, pristine PDF is not suitable as emulsifier due to its compact physical structure and high hydrophobicity, which seriously limits its utilization. The objective of the study was to investigate the effects of cellulase on physicochemical properties of PDF and to illustrate the feasibility of cellulase modified PDF (MPDF) as natural functional emulsifier. RESULTS Cellulase modification significantly improved (P < 0.05) specific surface area, water‐holding capacity/oil‐holding capacity, viscoelasticity, hydrophobicity, and pore structure while decreased crystallinity index and particle size of PDF. Emulsion could remain stable over 30 days as MPDF concentrations up to 1 wt% and oil/water ratio 3:7. The appearance stability of emulsions was not influenced by temperature (4–60 °C), pH (3.0–12.0), and ion concentration (0–200 mmol L−1) which was similar to Pickering emulsions. The mechanism of MPDF as an emulsifier was mainly attributed to the combination of Pickering effect and the three‐dimensional network. In addition, MPDF showed higher antioxidant capacity in emulsions than other classical emulsifiers. CONCLUSION The results illustrated that MPDF has a favorable feasibility for preparation of stable Pickering‐type emulsions, which will be a practical support for application of PDF as a natural functional emulsifier and will be helpful to realize the resource utilization of DF in pomelo industries. © 2022 Society of Chemical Industry
科研通智能强力驱动
Strongly Powered by AbleSci AI