The Planetary Spectroscopy Laboratory (PSL) in the Department of Planetary Laboratories (PLL), German Aerospace Center (DLR) in Berlin is working on a database that will encompass a wide range of spectral measurements, studying different effects such as grain sizes, temperature, mixing and weathering. Our goal is to create a database large enough to address the diverse range of spectral characteristics that might be observed on planetary surfaces, thereby enhancing our capabilities to interpret orbital data coming from their surfaces. A part of this database is an extensive spectral library to interpret and validate data from the Venus Emissivity Mapper (VenSpec-M/VEM) of the ESA EnVision and NASA VERITAS missions. VenSpec-M/VEM will remotely collect data about the surface of Venus in the Near-InfraRed (NIR) spectral range, covering five spectral windows in the Venus atmosphere between 0.85 and 1.18 μm, to map rock types and study Venus surface composition and activity, by comparing orbital data with laboratory spectra. Here we present the first results of a study aimed at understanding the NIR spectral behavior of particulate mixtures in emissivity at Venus surface temperature varying the grain size of the components and the actual physical distribution of particles in the mixture. Spectra are acquired using a high-temperature emissivity setup coupled with a Bruker FTIR 80V spectrometer to measure NIR emissivity spectra of Venus analogs at relevant Venus surface temperatures in a vacuum environment. Detailed image analyses are performed using a Digital Microscope Keyence VHX-7000 to ensure precise mixing ratios and quantify modal mineralogy. Results show that linear mixing does not reproduce laboratory emissivity spectral behavior accurately in this wavelength range, leading to the future exploration of non-linear mixing approaches. This data provides important insight on the need for Machine Learning models as they are planned for the VEM and VenSpec-M data analysis and will be invaluable to train those algorithms. Furthermore, the datasets can help in the planning of instrumentation for future aerial and lander missions exploring the surface of Venus.