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.

塑料分类是废物管理中非常关键的一步，特别是由于多层塑料的存在。工业 4.0 通过近红外高光谱成像 (NIRHSI) 显著改善了塑料包装的材料分类速度和准确性，其中使用了非负矩阵分解 (NMF) 来识别多层塑料。