Liao Zhenggen, Zhang Nan, Zhao Guowei, Zhang Jing, Liang Xinli, Zhong Shaojin, Wang Guangfa, Chen Xulong
Key Laboratory of Modern Preparation of TCM, Ministry of Education, Jiangxi University of Traditional Chinese Medicine, Nanchang, Jiangxi, China.
Pharmazie. 2012 Sep;67(9):774-80.
In this study we applied statistical multivariate analysis techniques to establish correlations between material properties and tablet tensile strength (TS) of microcrystalline cellulose (MCC) with different types and manufacturers. There were sixteen MCC samples included in this analysis described by 22 material parameters. For data analysis, principal component analysis (PCA) was used to model and evaluate the various relationships between the material properties and TS. Furthermore, partial least squares regression (PLS) analysis was performed to quantify the relationships between the material properties and TS and to predict the most influential MCC parameters contributing to the compactibility. The results showed that the moisture content, hygroscopicity and crystallinity did not exhibit significant impact on TS. The turgidity, maximum water uptake, degree of polymerization and molecular weight presented a strong positive influence on TS, while the density property, bulk and tap density, exhibited an obvious negative impact. The present work demonstrated that multivariate data analysis techniques (PCA and PLS) are useful for interpreting complex relations between 22 material properties and the tabletting properties of MCC. Furthermore, the method can be used for material classification.
在本研究中，我们应用统计多变量分析技术，以建立不同类型和制造商的微晶纤维素（MCC）的材料特性与片剂抗张强度（TS）之间的相关性。本分析纳入了16个MCC样品，由22个材料参数描述。为了进行数据分析，主成分分析（PCA）用于对材料特性与TS之间的各种关系进行建模和评估。此外，进行了偏最小二乘回归（PLS）分析，以量化材料特性与TS之间的关系，并预测对可压性有最大影响的MCC参数。结果表明，水分含量、吸湿性和结晶度对TS没有显著影响。膨胀度、最大吸水量、聚合度和分子量对TS有强烈的正向影响，而密度特性、松密度和振实密度则表现出明显的负面影响。目前的工作表明，多变量数据分析技术（PCA和PLS）有助于解释22种材料特性与MCC压片特性之间的复杂关系。此外，该方法可用于材料分类。
