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超微粉碎对白术饮片粉体学性质和溶出度的影响 被引量:9

Influence of superfine grinding on the micromeritic properties and in vitro dissolution of Atractylodes macrocephala
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摘要 目的研究超微粉碎技术对白术粉体学性质及体外溶出行为的影响,为中药制剂投料提供依据。方法采用振动磨制备白术不同粒径的超微粉,并与细粉进行微观特征、润湿性、比表面积与孔隙率、红外光谱等粉体学性质的对比;采用自身对照紫外分光光度法评价不同粒径粉体的溶出特性;利用热图和主成分分析对数据进行分析和综合评价。结果白术饮片经超微粉碎后粒径减小,比表面积显著增加,溶出度得到了有效改善,但润湿性略有降低;粉体红外特征谱图未发生变化;热图结果表明:粒径是影响溶出的关键因素。结论超微粉碎技术对于改善白术粉体的制剂学性质和溶出度具有一定的应用价值。 OBJECTIVE To investigate the dissolution behavior of the different size of Atractylodes macrocephala powders,and to build the feeding reference of traditional Chinese medicine.METHODS Compared the wettability,microscopic characteristics,specific surface area and porosity,and the infrared spectrum of the fine powder and ultrafine powder of A. macrocephala.Their dissolution were tested by using self -control ultraviolet spectrophotometry.Heat map and principal component analysis were utilized to analyze and assess the results.RESULTS The particle size of A.macrocephala was reduced after ultrafine grinding,the specific surface area and the dissolution were significantly increased,but the wettability was slightly decreased.The infrared spectrum of the ultrafine particle did not changed.The heatmap results showed that particle size was a key factor affecting the dissolution.CONCLUSION The ultrafine grinding technology plays an important role in improving the preparation properties and dissolution of A.macrocephala powder.
作者 李婧琳 王媚 史亚军 邹俊波 崔春利 陈兴宇 张怡成 王浩蓉 LI Jinglin;WANG Mei;SHI Yajun;ZOU Junbo;CUI Chunli;CHEN Xingyu;ZHANG Yicheng;WANG Haorong(College of Medicine,Shaanxi University of Chinese Medicine,Xianyang,Shaanxi,712046 P.R.China)
出处 《华西药学杂志》 CAS CSCD 2019年第1期22-26,共5页 West China Journal of Pharmaceutical Sciences
基金 陕西省教育厅项目(编号:16JK1217) 陕西省科技厅项目(编号:2017SF-308 2018JM7157)
关键词 白术 饮片 超微粉碎 扫描电镜 比表面积 润湿性 红外光谱 紫外分光光度法 溶出性能 热图 主成分分析 Atractylodes macrocephala Decoction pieces Ultrafine grinding Scanning electronmicroscope Specific surface area Wettability Infrared spectrum Ultraviolet spectrophotometry Dissolution Heat map Principal component analysis
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