In cartography, the combination of colour and contour lines is used to express a three-dimensional landscape on a two-dimensional map. We transferred this concept to the analysis of mass spectrometry imaging (MSI) data and developed a collection of R scripts for the efficient evaluation of .imzML archives in a four-step strategy: (1) calculation of the density distribution of mass-to-charge ratio (m/z) signals in the .imzML file and assembling of a pseudo-master spectrum with peak list, (2) automated generation of mass images for a defined scan range and subsequent visual inspection, (3) visualisation of individual ion distributions and export of relevant .mzML spectra and (4) creation of overlay graphics of ion images and photographies. The use of a Hue-Chroma-Luminance (HCL) colour model in MSI graphics takes into account the human perception for colours and supports the correct evaluation of signal intensities. Further, readers with colour blindness are supported. Contour maps promote the visual recognition of patterns in MSI data, which is particularly useful for noisy data sets. We demonstrate the scalability of MSI.R scripts by running them on different systems: on a personal computer, on Amazon Web Services (AWS) instances and on an institutional cluster. By implementing a parallel computing strategy, the execution speed for .imzML data scanning with image generation could be improved by more than an order of magnitude. Applying our MSI.R scripts ( http://www.bioprocess.org/MSI.R ) to low-temperature plasma (LTP)-MSI data shows the localisation of volatile and semi-volatile compounds in the cross-cut of a chilli (Capsicum annuum) fruit. The subsequent identification of compounds by gas and liquid chromatography coupled to mass spectrometry (GC-MS, LC-MS) proves that LTP-MSI enables the direct measurement of volatile organic compound (VOC) distributions from biological tissues.