Natural aerosols, originating from uncontrollable processes, are widely distributed and often interfere with the remote sensing of anthropogenic aerosols. This interference occurs because distinguishing between particle types is challenging when they coexist. Despite their significant impact on radiative forcing and climate, research on natural aerosols remains limited due to their unpredictable nature. To address this, we implemented a pilot-scale open chamber system coupled with multi-wavelength elastic polarized LiDAR. This system enables the separation of target particles from ambient aerosols, enabling the development of a specialized analysis algorithm that calculates optical parameters-such as the Ångström Exponent (AE) and depolarization ratio (δ)- which serve as unique "fingerprints" for distinguishing aerosol types. Our experiments included some natural particles, such as yeast, whey protein, fly ash, flour, pine tree pollen, and kaolinite. Distinct optical properties were observed, with yeast exhibiting high δ values at 532 nm (0.31 ± 0.09) and 1064 nm (0.06 ± 0.01). Whey protein and fly ash were distinguishable based on AE values of -0.23 ± 1.16 and 0.31 ± 0.59, respectively. Pollen, another key natural aerosol, showed δ values of 0.33 ± 0.03 at 532 nm and 0.04 ± 0.01 at 1064 nm, enabling clear differentiation from other aerosol types. By incorporating infrared wavelengths into our LiDAR system, we enhanced the accuracy of aerosol characterization. This study highlights the approach for distinguishing natural aerosols and lays the groundwork for continuous monitoring systems to understand their atmospheric and climatic impacts better.