ABSTRACT
Kendrick mass defect (KMD) analysis is widely used for helping the detection and identification of chemically related compounds based on exact mass measurements. We report here the use of KMD as a criterion for filtering complex mass spectrometry data set. The method allow automated, easy and efficient data processing, enabling the reconstruction of 2D distributions of families of homologous compounds from MSI images. We show that KMD filtering, based on in-house software, is suitable and robust for high resolution (full width at half-maximum, fwhm, at m/z 410 of 20â¯000) and very high-resolution (fwhm, at m/z 410 of 160â¯000) MSI data. This method has been successfully applied to two different types of samples, bacteria cocultures, and brain tissue sections.
