Potential use of multivariate curve resolution for the analysis of mass spectrometry images

In this work the application of multivariate curve resolution is proposed for the analysis of Mass Spectrometry Imaging (MSI) data. Recently, developments in the ionization of samples have dramatically expanded the number of applications of MSI due to the possibility of collecting the mass spectrum for each pixel of a considered surface in a reasonable time. Using this method, both spatial distribution and spectral information of analyzed samples can be obtained. However, there are major drawbacks inherent to MSI related to the high complexity of the data obtained from real samples and to the extremely huge size of the datasets generated by this technique. Therefore, the potential of chemometrical tools in different steps of the analysis process is unquestionable, from data compression to data resolution of the different components present at each pixel of the image. In this work, this data analysis is carried out by means of the multivariate curve resolution method. The benefits of the application of this method are shown for two examples consisting of a MS image of two platted microbes and a MS image of a mouse lung section. The results show that multivariate curve resolution allows us to obtain distribution maps of different components and their identification from resolved high-resolution mass spectra.