The impact of dual-frequency ultrasound on the interaction between soy protein isolate and allicin: structural modifications and emulsifying potential
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Yunliang Li
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Man Zhou
Abstract
In order to improve the emulsifying capacity of soy protein isolate (SPI), allicin was introduced to SPI with the assistance of dual-frequency ultrasound. Sulfhydryl (SH) content was determined to compare the effects of ultrasound on the pretreatment of SPI and the interaction process between SPI and allicin. It was found that using ultrasound during the interaction process between SPI and allicin was more beneficial for exposing the SH groups, which facilitated the binding between the two. The highest SH content was observed when the ultrasound frequency was set at 20/28 kHz, power at 50 W/L, and time at 30 min. The hydrophobicity index of the conjugates prepared by ultrasound with allicin/SH ratios of 0.5 (US0.5) and 1 (US1) was maximized, with values of 1,015.6 and 1,018.0, respectively, representing 17.6 % and 17.9 % increases over the control (P < 0.05). This enhanced hydrophobicity was closely linked to improved emulsifying activity and emulsion stability, as both US0.5 and US1 demonstrated superior performance in these areas. The emulsion viscosity of US1 was the highest at a shear rate of 1.0 s−1, reaching 0.412 Pa s, significantly greater than the viscosity of US0.5 emulsion (P < 0.05). This research showed that applying dual-frequency ultrasound during the interaction between SPI and allicin facilitated the formation of conjugates, which subsequently adsorbed efficiently at the oil-water interface.
Funding source: the Primary Research & Development Plan of Shandong Province
Award Identifier / Grant number: 2022CXGC010603
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Research ethics: Not applicable.
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Informed consent: Not applicable.
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Author contributions: Yunliang Li: investigation, formal analysis, methodology, writing-original draft; Shifang Jia: investigation, data curation, formal analysis; Liurong Huang: supervision, conceptualization, formal analysis, writing-review and editing; Man Zhou: methodology, supervision; Ronghai He: conceptualization, funding acquisition, writing-review and editing.
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Use of Large Language Models, AI and Machine Learning Tools: None declared.
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Conflicts of Interest: The authors state no conflict of interest.
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Research funding: This study was supported by the Primary Research & Development Plan of Shandong Province (2022CXGC010603).
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Data availability: Data will be available on request.
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Copyright and license: The authors confirm that the manuscript is original, has not been published, and is not under consideration elsewhere. All authors have read and approved the final manuscript and agree with its submission.
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