Mono/Multi-material Characterization Using Hyperspectral Images and Multi-Block Non-Negative Matrix Factorization

TL;DR

This paper introduces a novel hyperspectral imaging and multi-block non-negative matrix factorization method to accurately identify monomaterial and multimaterial plastics, improving waste sorting efficiency in recycling processes.

Contribution

The paper develops an extended MBNMF model with an F test for chemical pattern recognition, enabling precise differentiation of monomaterial and multimaterial plastics using hyperspectral images.

Findings

Successfully identified multilayer plastics in waste objects.

Enhanced accuracy in plastic sorting compared to existing methods.

Demonstrated practical application in recycling industry.

Abstract

Plastic sorting is a very essential step in waste management, especially due to the presence of multilayer plastics. These monomaterial and multimaterial plastics are widely employed to enhance the functional properties of packaging, combining beneficial properties in thickness, mechanical strength, and heat tolerance. However, materials containing multiple polymer species need to be pretreated before they can be recycled as monomaterials and therefore should not end up in monomaterial streams. Industry 4.0 has significantly improved materials sorting of plastic packaging in speed and accuracy compared to manual sorting, specifically through Near Infrared Hyperspectral Imaging (NIRHSI) that provides an automated, fast, and accurate material characterization, without sample preparation. Identification of multimaterials with HSI however requires novel dedicated approaches for chemical pattern recognition. Non negative Matrix Factorization, NMF, is widely used for the chemical resolution of hyperspectral images. Chemically relevant model constraints may make it specifically valuable to identify multilayer plastics through HSI. Specifically, Multi Block Non Negative Matrix Factorization (MBNMF) with correspondence among different chemical species constraint may be used to evaluate the presence or absence of particular polymer species. To translate the MBNMF model into an evidence based sorting decision, we extended the model with an F test to distinguish between monomaterial and multimaterial objects. The benefits of our new approach, MBNMF, were illustrated by the identification of several plastic waste objects.