Analysis of microplastic particles with a combination of vibrational spectroscopy and particle detection allows for obtaining the most detailed information regarding a sample's microplastic particle (MP) content. That includes MP quantification and material type identification as well as the determination of particle shapes and colors, which can be relevant for determining an MP's source, or for evaluating its biological impact. For particle-based approaches, the number of particles in a sample poses a considerable challenge. For particles ¡ 500 µm, common particle counts for environmental samples lie in the range of several thousand (riverine water) to hundred thousand particles (sewage sludge) per sample, which multiplies when analyzing a set of samples. This task demands automated analysis routines. To perform analyses in a comparable as well as time- and personnel-efficient way, the GEPARD (Gepard Enabled PARticle Detection) software1 was developed in our group. GEPARD is open-source and can be coupled to Raman or Fourier Transform Infrared (FTIR) microscopes, where it allows for running a highly automated combination of optical particle detection and Raman or FTIR measurements. It provides tools to handle the optical image acquisition, particle detection, spectroscopic measurement and data analysis seamlessly. The presentation will show the GEPARD-based analysis workflow, which our lab has applied to more than 100 samples from various environmental compartments over the past three years using a WITec alpha 300R Raman microscope. It covers the steps from filtering pre-treated samples onto measurement substrates in a contamination-minimizing environment to creating a summarized result output that includes the size, shape and color of all MP found. In addition, example analyses of freshwater and wastewater treatment plant samples concerning MP ¿ 50 µm will be shown. 1 Brandt, J. et al. (2020) ‘High-Throughput Analyses of Microplastic Samples Using Fourier Transform Infrared and Raman Spectrometry', Applied Spectroscopy. doi: 10.1177/0003702820932926.