关键物料属性(CMAs)辨识是中药大品种健胃消食片质量控制的关键问题。该文以健胃消食片颗粒为研究对象，以片剂抗张强度为主要质量属性，建立了颗粒CMAs的辨识方法与设计空间，并基于傅里叶近红外光谱技术(FT-NIR)建立颗粒CMAs的预测模型。首先采用试验设计(design of experiments，DOE)中的部分因子试验设计方法制备不同性质的健胃消食片颗粒，测定颗粒的粉体学性质。采用正交偏最小二乘法(OPLS)建立颗粒粉体学性质与抗张强度间的关系模型。根据OPLS提取综合变量的特性，找出对抗张强度解释作用最强的自变量，再利用FT-NIR技术建立颗粒CMAs的预测模型。最终确定关键物料属性为吸湿率、含水率、D50、崩溃角、质量流速、振实密度;流动性、D50、含水率预测模型的预测集决定系数(R2p)与相对百分偏差(RPD)分别为0。891、0。994、0。998;2。97、12。4、20。7。建立的OPLS可明确各因素对抗张强度的影响程度大小，拟合效果良好。所建模型的预测准确度较高，可用于健胃消食片颗粒CMAs的快速、准确测定。
Identification of CMAs of Jianwei Xiaoshi Tablet granules based on QbD concept and construction of their predictive model
Identification of critical material attributes (CMAs) is a key issue in the quality control of large-scale TCM products like Jianwei Xiaoshi Tablets.This study focuses on the granules of Jianwei Xiaoshi Tablets,using tablet tensile strength as the primary quality attribute.A method for identifying the CMAs and a design space for the granules were established,along with a predictive model for the granule CMAs based on Fourier transform near-infrared spectroscopy (FT-NIR).First,granules of Jianwei Xiaoshi Tablets with different properties were prepared using a partial factorial design method from the design of experiments (DOE).The powder properties of the granules were measured.An orthogonal partial least squares (OPLS) model was established to correlate the powder properties with tensile strength.Based on the characteristics of the comprehensive variables extracted by OPLS,the independent variables with the greatest explanatory power for tensile strength were identified.FT-NIR technology was then employed to establish a predictive model for the granule CMAs.The final CMAs identified were hygroscopicity,moisture content,D50,collapse angle,mass flow rate,and tapped density.The coefficients of determination of the prediction set (R2p) and relative percentage deviation (RPD) of the prediction set for flowability,D50,and moisture content were 0.891,0.994,and 0.998;and 2.97,12.4,and 20.7,respectively.The established OPLS model clearly identified the impact of various factors on tensile strength,demonstrating good fit results.The model exhibited high prediction accuracy and can be used for the rapid and accurate determination of CMAs in granules of Jianwei Xiaoshi Tablets.
critical material attributesdesign spaceorthogonal partial least squaresFourier transform near-infrared spectroscopypredictive model
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