Machine Learning Improves LIBS Classification of Real Plastic Waste Under Variable Laser Conditions
Researchers are utilizing machine learning to improve the accuracy of laser-induced breakdown spectroscopy (LIBS) for sorting plastic waste. The study addresses the challenge of inconsistent laser performance caused by varying surface textures and heights in real-world recycling environments.
Why it matters
Improving automated sorting technology is essential for increasing the efficiency and viability of global plastic recycling efforts.
Ask our AI Assistant Search Menu Posted in | News | Plastics and Polymers | Sustainable Technologies | Materials Processing | Materials Analysis Machine Learning Improves LIBS Classification of Real Plastic Waste Under Variable Laser Conditions Download PDF Copy Add AZoM on Google as a preferred source By Akshatha Chandrashekar Reviewed by Susha Cheriyedath, M.Sc. Sep 28 2026 Researchers test whether machine learning can help LIBS classify real plastic waste when laser conditions vary.
Paper: Classification of real plastic waste using machine learning-assisted laser-induced breakdown spectroscopy . AI-generated abstract conceptual image created using ChatGPT/OpenAi
A recent study in Scientific Reports explores the use of machine learning-assisted laser-induced breakdown spectroscopy to classify real plastic waste. The researchers investigate four common polymers using three laser pulse energies to approximate changes in laser irradiance that can occur when sample height, surface topology, focusing, or system operation varies.
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