Please use this identifier to cite or link to this item: http://hdl.handle.net/11455/95875
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dc.contributor.author黃祥閔zh_TW
dc.contributor.authorHuang, Shang-Mingzh_TW
dc.contributor.authorKuo, Chia-Hungzh_TW
dc.contributor.authorChen, Chun-Anzh_TW
dc.contributor.authorLiu, Yung-Chuanzh_TW
dc.contributor.authorShieh, Chwen-Jenzh_TW
dc.date2017-05-
dc.date.accessioned2018-11-02T03:13:02Z-
dc.date.available2018-11-02T03:13:02Z-
dc.identifier.urihttp://hdl.handle.net/11455/95875-
dc.description.abstractPiceid, a naturally occurring derivative of resveratrol found in many plants, has recently been considered as a potential nutraceutical. However, its poorly water-soluble property could cause a coupled problem of biological activities concerning drug dispersion and absorption in human body, which is still unsolved now. Liposome, a well-known aqueous carrier for water-insoluble ingredients, is commonly applied in drug delivery systems. In this study, a feasible approach for solving the problem is that the targeted piceid was encapsulated into a liposomal formula as aqueous substrate to overcome its poor water-solubility. The encapsulation process was assisted by ultrasound, with investigation of lipid content, ultrasound power and ultrasound time, for controlling encapsulation efficiency (E.E%), absolute loading (A.L%) and particle size (PS). Moreover, both RSM and ANN methodologies were further applied to optimize the ultrasound-assisted encapsulation process. The data indicated that the most important effects on the encapsulation performance were found to be of lipid content followed by ultrasound time and ultrasound power. The maximum E.E% (75.82%) and A.L% (2.37%) were exhibited by ultrasound assistance with the parameters of 160mg lipid content, ultrasound time for 24min and ultrasound power of 90W. By methodological aspects of processing, the predicted E.E% and A.L% were respectively in good agreement with the experimental results for both RSM and ANN. Moreover, RMSE, R2 and AAD statistics were further used to compare the prediction abilities of RSM and ANN based on the validation data set. The results indicated that the prediction accuracy of ANN was better than that of RSM. In conclusion, ultrasound-assisted liposome encapsulation can be an efficient strategy for producing well-soluble/dispersed piceid, which could be further applied to promote human health by increased efficiency of biological absorption, and the process of ultrasound-mediated liposome encapsulation can be well established by a methodological approach using either RSM or ANN, but it is worth mentioning that the ANN model used here showed the superiority over RSM for predicting and optimizing encapsulation.zh_TW
dc.language.isoen_USzh_TW
dc.relationUltrasonics sonochemistry, Volume 36, Page(s) 112-122.zh_TW
dc.subjectANNzh_TW
dc.subjectLiposome encapsulationzh_TW
dc.subjectPiceidzh_TW
dc.subjectRSMzh_TW
dc.subjectUltrasoundzh_TW
dc.subjectWater-insolublezh_TW
dc.subjectCapsuleszh_TW
dc.subjectGlucosideszh_TW
dc.subjectLiposomeszh_TW
dc.subjectParticle Sizezh_TW
dc.subjectStilbeneszh_TW
dc.subjectNeural Networks (Computer)zh_TW
dc.subjectUltrasonic Waveszh_TW
dc.titleRSM and ANN modeling-based optimization approach for the development of ultrasound-assisted liposome encapsulation of piceidzh_TW
dc.typeJournal Articlezh_TW
dc.identifier.doi10.1016/j.ultsonch.2016.11.016zh_TW
item.languageiso639-1en_US-
item.cerifentitytypePublications-
item.grantfulltextopen-
item.openairetypeJournal Article-
item.openairecristypehttp://purl.org/coar/resource_type/c_18cf-
item.fulltextwith fulltext-
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