Institute of Pharmacy and Molecular Biotechnology, Department of Pharmaceutical Technology and Biopharmaceutics, University of Heidelberg, Germany.
Eur J Pharm Biopharm. 2011 May;78(1):117-24. doi: 10.1016/j.ejpb.2010.12.035. Epub 2011 Jan 8.
Near-Infrared Chemical Imaging (NIR-CI) is rapidly gaining importance for the analysis of complex intermediate and final drug products. The availability of both spectral information from the sample and spatial information on the distribution of individual components offers access to greater understanding of manufacturing processes in many stages of pharmaceutical production. One major aspect in terms of chemical imaging is data analysis, since each measurement (image) generates a data cube containing several thousands of spectra (i.e., one spectrum per image pixel). The visual interpretation of component distribution (e.g., homogeneity) is an important issue but subjective. Chemometric methods are therefore required to extract qualitative and quantitative information from each image and enable comparison of several images. In this work, we describe a novel approach for the statistical evaluation of NIR-CI in terms of a multivariate treatment of univariate statistical descriptors characterizing image pixel (e.g., skewness and kurtosis). This technique was called by the authors "Symmetry Parameter Image Analysis" (SPIA), since it enables assessing the symmetry of pixel distributions in terms of different sample attributes. That approach is an innovative way of reporting results with a straightforward relation with attributes such as homogeneity, thus providing the basis for setting up acceptance criteria for good processing conditions or sample homogeneity. Furthermore, this procedure is applicable to determine product variability for large data sets without the need for explicit consideration of each image as its main attributes have been captured by the pixel distributions and their univariate descriptors. The approach is described by means of data obtained by NIR-CI on a powder blend case study (process application). Additionally, SPIA was used for the qualitative classification of tablets (sample application), showing that the approach can be generalized to set up criteria for sample-to-sample similarity and be useful in establishing criteria for e.g., counterfeiting.
近红外化学成像（NIR-CI）在分析复杂的中间和最终药物产品方面的重要性日益增加。由于样品的光谱信息和单个成分分布的空间信息都可用，因此可以更深入地了解药物生产的许多阶段的制造过程。在化学成像方面，数据分析是一个主要方面，因为每次测量（图像）都会生成一个包含数千个光谱的数据立方体（即，每个图像像素一个光谱）。成分分布的视觉解释（例如，均匀性）是一个重要问题，但主观性很强。因此，需要化学计量方法从每个图像中提取定性和定量信息，并能够比较多个图像。在这项工作中，我们描述了一种新颖的方法，用于通过对描述图像像素的单变量统计描述符（例如偏度和峰度）进行多元处理来统计评估 NIR-CI。该技术被作者称为“对称参数图像分析”（SPIA），因为它能够根据不同的样品属性评估像素分布的对称性。该方法是一种新颖的报告结果的方法，与均匀性等属性具有直接关系，从而为建立良好处理条件或样品均匀性的验收标准提供了基础。此外，该方法适用于确定具有大量数据集的产品可变性，而无需明确考虑每个图像，因为其主要属性已由像素分布及其单变量描述符捕获。该方法通过在粉末混合物案例研究（过程应用）中获得的 NIR-CI 数据进行描述。此外，SPIA 还用于片剂的定性分类（样品应用），表明该方法可以推广到建立样品间相似性的标准，并有助于建立例如假冒的标准。
