A multivariate approach for the statistical evaluation of near-infrared chemical images using Symmetry Parameter...

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.