Comparative Analysis of Multispectral LED–Sensor Architectures for Scalable Waste Material Classification

Abstract

We present a comprehensive study of LED-based optical sensing systems for the classification of waste materials, analyzing recent developments in the field. Accurate identification of materials such as plastics, glass, aluminum, and paper is a crucial yet challenging task in waste management for recycling. The first approach uses short-wave infrared reflectance spectroscopy with commercial Germanium photodetectors and selected LEDs to keep data complexity and cost at a minimum while achieving classification accuracies up to 98% with machine learning algorithms. The second system employes a voltage-tunable Germanium-on-Silicon photodetector that operates across a broader spectral range (400–1600 nm), in combination with three LEDs in both the visible and short-wave infrared bands. This configuration enables an adaptive spectral response and simplifies the optical setup, supporting energy-efficient and scalable integration. Accuracies up to 99% were obtained with the aid of machine learning algorithms. Across all systems, the strategic use of low-cost LEDs as light sources and compact optical sensors demonstrates the potential of light-emitting devices in the implementation of compact, intelligent, and sustainable solutions for real-time material recognition. This article explores the design, characterization, and performance of such systems, providing insights into the way light-emitting and optoelectronic components can be leveraged for advanced sensing in waste classification applications.