Please use this identifier to cite or link to this item: http://hdl.handle.net/11455/91531
標題: 以超音波輔助探討微脂體包覆白藜蘆醇苷 之最適化研究
Optimization of liposome encapsulation of piceid assisted by sonication
作者: Chun-An Chen
陳俊安
關鍵字: Piceid
Liposome
Sonication
RSM
ANN
白藜蘆醇苷
微脂體
超音波震盪
類神經網路
反應曲面法
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摘要: 白藜蘆醇苷(Piceid)近年來被應用於心血管疾病的研究,但由於在水中溶解度極低,不利於進入人體,因此包覆於微脂體之中,以利人體吸收,本文中進一步研究超音波震盪下微脂體包覆白藜蘆醇苷之包覆率變化,並使用不同的模型分析結果。 本實驗分成兩部分,第一部分使用L-α-phosphatidylcholine與膽固醇為脂質加入白藜蘆醇苷溶於有機溶劑製備微脂體,並抽乾有機溶劑進行水合,待平衡後進行超音波震盪實驗,實驗探討脂質含量(120~180 mg)、超音波震盪功率(90~150 W)及超音波震盪時間(10~50 min)對包覆率,總負載率及粒徑大小之影響。結果顯示脂質含量對包覆率及總負載率皆有顯著影響,而超音波功率及時間對於粒徑大小則有影響。以三階層三變數之Box-Behnken design(BBD)及反應曲面法探討微脂體包覆白藜蘆醇苷之包覆率,總負載率及粒徑大小。 第二部分使用類神經網路模型與反應曲面法模型進行比較,首先先建立類神經網路模型,將BBD數據代入分析,由1,000~100,000循環次數,Tanh、Sigmoid及Gaussian學習函數,不同的學習模式,及3~6個隱藏神經元中尋找均方根誤差值(RMSE)最小及檢定係數(R2)最高的組合。在類神經網路下,以循環次數為10,000次、學習函數為Sigmoid、學習模式為Levenberg-Marquardt演算法及6個隱藏神經元的條件下,並進一步使用均方根誤差值(RMSE),絕對誤差值(AAD)及檢定係數(R2)對於類神經網路與反應曲面法進行比較。 結果顯示反應曲面法預測值對實測數據之檢定係數為0.955,而類神經網路預測值對實測數據之檢定係數為0.997,藉由檢定係數之比較即可看出,類神經網路系統在預測此實驗設計模組上比反應曲面法之適切性來得高。在比較RMSE方面,RSM之RMSE為1.842,ANN模組之RMSE為0.744,在比較AAD方面,RSM之AAD為2.476,ANN模組之AAD為0.595表示ANN比RSM更為適合解釋本研究的數據。
Piceid is used in cardiovascular disease in recently years; however, its low water solubility causes a problem for human body absorption. Therefore, encapsulated piceid into liposome is a feasible way to improve piceid water solubility, we observe the encapsulation efficiency on piceid liposome under sonication process. This study is divided into two parts. In the first part, L-α-phosphatidylcholine and cholesterol were used as lipid content and are added to piceid dissolved in organic solvent, The conditions of encapsulation were under vaccum and hydration, experiment condition under lipid content (120~180 mg), sonication power(90~150 W) and sonication time(10~50 min) with effect of encapsulation efficiency(E.E%), absolute loading(A.L%) and particle size(PS). The 3-level-3factor Box-Behnken design was applied to optimize the response. The result indicate lipid content, sonication power and sonication time have significant effect.In second part, Response surface methodology (RSM) and artificial neural network (ANN). First step is establish a model of ANN, BBD data for further analysis. The learning cycle number is ranging from 1,000~100,000 times, and transfer function including Gaussian, Tanh and Sigmoid. Hidden layer including 3~6 nodes. To find minimum root mean square error (RMSE) and absolute average deviation(AAD) and maximum R2. As result the model with the condition of 10,000 learning cycle number, sigmoid transfer function, Levenberg-Marquardt algorithm and 6 nodes in hidden layer were the best combination for ANN. Comparing with RSM, the R2 of ANN is 0.997, better than the R2 of RSM is 0.955. The RMSE and AAD in ANN model are 0.744% and 0.595% respectively, and RMSE and AAD in RSM model are 1.842% and 2.476% respectively. The lower RMSE and AAD values represent the more precise prediction of E.E%. Therefore the ANN model is more suitable than the RSM model to explain the data of this study.
URI: http://hdl.handle.net/11455/91531
其他識別: U0005-1307201510555900
文章公開時間: 2018-07-24
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