Material classification based on a SWIR discrete spectroscopy approach.

A crucial yet difficult task for waste management is the identification of raw materials like plastic, glass, aluminum, and paper. Most previous studies use the diffused reflection spectroscopy for classification purposes. Despite the benefits in terms of speed and simplicity offered by modern compact spectrometers, their cost and the need for an external, wide-spectrum source of illumination create complications. To address this issue, the present paper proposes a discrete spectroscopy method that utilizes short-wave infrared (SWIR) reflectance to identify waste materials, exploiting a small set of selected wavelengths. This approach reduces the complexity of the classification data analysis and offers a more practical alternative to the conventional method. The proposed system comprises a single germanium photodetector and 10 different light emitting diodes (LEDs). The LED wavelengths are selected to maximize the system sensitivity towards a set of seven different waste materials. Using a classification strategy relying on support vector machines, the proposed methodology reaches a classification accuracy up to 98%.