海峡药学
海峽藥學
해협약학
STRAIT PHARMACEUTICAL JOURNAL
2014年
8期
8-11
,共4页
王燕%刘霞%符俊%方芳%周小方
王燕%劉霞%符俊%方芳%週小方
왕연%류하%부준%방방%주소방
中心点设计%细辛脑%乳剂%旋转蒸发%优化
中心點設計%細辛腦%乳劑%鏇轉蒸髮%優化
중심점설계%세신뇌%유제%선전증발%우화
Central composite design%Asarone%Emulsion%Rotary evaporator%Optimization
目的:筛选细辛脑乳剂旋转蒸发工艺的参数条件,提高制剂质量,并对优化条件可行性进行预测。方法采用中心点设计法优化细辛脑乳剂的旋转蒸发工艺,自变量为进料速率,加热温度,转速及真空度,以甲醇及氯仿的去除率,收率,平均粒径及有关物质为因变量,对各水平的自变量进行多元线性回归和拟合,选择较佳工艺条件。结果甲醇及氯仿的去除率、收率、总杂质模型拟合较好,相关系数r分别为0.9306、0.95123、0.8465、0.9752,预测值与实测值的RSD%均小于2%。结论效应面优化法具有试验次数少,精确度高,使用方便,优选条件预测性好等特点。
目的:篩選細辛腦乳劑鏇轉蒸髮工藝的參數條件,提高製劑質量,併對優化條件可行性進行預測。方法採用中心點設計法優化細辛腦乳劑的鏇轉蒸髮工藝,自變量為進料速率,加熱溫度,轉速及真空度,以甲醇及氯倣的去除率,收率,平均粒徑及有關物質為因變量,對各水平的自變量進行多元線性迴歸和擬閤,選擇較佳工藝條件。結果甲醇及氯倣的去除率、收率、總雜質模型擬閤較好,相關繫數r分彆為0.9306、0.95123、0.8465、0.9752,預測值與實測值的RSD%均小于2%。結論效應麵優化法具有試驗次數少,精確度高,使用方便,優選條件預測性好等特點。
목적:사선세신뇌유제선전증발공예적삼수조건,제고제제질량,병대우화조건가행성진행예측。방법채용중심점설계법우화세신뇌유제적선전증발공예,자변량위진료속솔,가열온도,전속급진공도,이갑순급록방적거제솔,수솔,평균립경급유관물질위인변량,대각수평적자변량진행다원선성회귀화의합,선택교가공예조건。결과갑순급록방적거제솔、수솔、총잡질모형의합교호,상관계수r분별위0.9306、0.95123、0.8465、0.9752,예측치여실측치적RSD%균소우2%。결론효응면우화법구유시험차수소,정학도고,사용방편,우선조건예측성호등특점。
OBJECTIVE To screen the parameters of the rotary evaporation process of Asarone emulsion , im-prove the quality of preparations and forecast the predictive optimization conditions.METHODS The central com-posite design method was used to optimize the rotary evaporation process of Asarone emulsion.For the feed rate,tem-perature ,speed and vacuum degree as the independent variables ,the removal of methanol and chloroform ,yield,aver-age particle size and the related substance as dependent variables ,multiple linear regression equation and fitting was set up for independent variables in every level and the better technology conditions was choosed .RESULTS The model of the removal rate of methanol and chloroform ,yield,total impurity had good fitting ,the correlation coefficient r were 0.9306 ,0.95123 ,0.8465 ,0.9306 ,RSD%of the predicted values and the measured values was less than 2%.CONCLUSION The Design-Response Surface Method has the characteristic of fewer tests ,high precision ,easy to use,and good predictive optimization conditions.