Multispectral Integrated System with Discrete Light Sources for Material Classification - Anju Manakkakudy Kumaran; Federica Mitri; Emanuele Maiorana; Andrea De Iacovo; Lorenzo Colace (Proceedings)
This paper presents a compact and portable classification system that utilizes a discrete light source method combining near-infrared (NIR) reflectance spectroscopy with a Support Vector Machine (SVM) to identify and classify waste materials. The system operates by sequentially activating 10 light-emitting diodes (LEDs) of different wavelengths and measuring their reflectance using a photodetector. This system incorporates a DAQ card using the LabView program for data acquisition and system control. The proposed model achieved an identification accuracy of up to 98% using different input features and training batches. This efficient and cost-effective solution provides an innovative approach to waste management.