Application of chemometric algorithms to MALDI mass spectrometry imaging of pharmaceutical tablets

•A new application of chemometric algorithms coupled with MALDI–MSI in the pharmaceutical field is shown.•Four different well-known multivariate data analysis algorithms were compared (PCA, ICA, NMF and MCR–ALS).•A specially manufactured in-house product and a commercialized tablet were analyzed.•The distribution of tablet compounds was possible without prior knowledge. During drug product development, the nature and distribution of the active substance have to be controlled to ensure the correct activity and the safety of the final medication. Matrix assisted laser desorption/ionization mass spectrometry imaging (MALDI–MSI), due to its structural and spatial specificities, provides an excellent way to analyze these two critical parameters in the same acquisition. The aim of this work is to demonstrate that MALDI–MSI, coupled with four well known multivariate statistical analysis algorithms (PCA, ICA, MCR–ALS and NMF), is a powerful technique to extract spatial and spectral information about chemical compounds from known or unknown solid drug product formulations. To test this methodology, an in-house manufactured tablet and a commercialized Coversyl® tablet were studied. The statistical analysis was decomposed into three steps: preprocessing, estimation of the number of statistical components (manually or using singular value decomposition), and multivariate statistical analysis. The results obtained showed that while principal component analysis (PCA) was efficient in searching for sources of variation in the matrix, it was not the best technique to estimate an unmixing model of a tablet. Independent component analysis (ICA) was able to extract appropriate contributions of chemical information in homogeneous and heterogeneous datasets. Non-negative matrix factorization (NMF) and multivariate curve resolution–alternating least squares (MCR–ALS) were less accurate in obtaining the right contribution in a homogeneous sample but they were better at distinguishing the semi-quantitative information in a heterogeneous MALDI dataset.