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Plastic sorting is a very essential step in waste management, especially dueto the presence of multilayer plastics. These monomaterial and multimaterialplastics are widely employed to enhance the functional properties of packaging,combining beneficial properties in thickness, mechanical strength, and heattolerance. However, materials containing multiple polymer species need to bepretreated before they can be recycled as monomaterials and therefore shouldnot end up in monomaterial streams. Industry 4.0 has significantly improvedmaterials sorting of plastic packaging in speed and accuracy compared to manualsorting, specifically through Near Infrared Hyperspectral Imaging (NIRHSI) thatprovides an automated, fast, and accurate material characterization, withoutsample preparation. Identification of multimaterials with HSI however requiresnovel dedicated approaches for chemical pattern recognition. Non negativeMatrix Factorization, NMF, is widely used for the chemical resolution ofhyperspectral images. Chemically relevant model constraints may make itspecifically valuable to identify multilayer plastics through HSI.Specifically, Multi Block Non Negative Matrix Factorization (MBNMF) withcorrespondence among different chemical species constraint may be used toevaluate the presence or absence of particular polymer species. To translatethe MBNMF model into an evidence based sorting decision, we extended the modelwith an F test to distinguish between monomaterial and multimaterial objects.The benefits of our new approach, MBNMF, were illustrated by the identificationof several plastic waste objects.

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