Effective plastic recycling requires the accurate identification of polymer types. However, existing optical sorting systems often struggle to distinguish common household plastics reliably. To overcome this identification bottleneck, multispectral imaging is necessary to capture the unique near-infrared spectral fingerprints of different materials. To address this, a comprehensive multispectral household plastic material database (MHPM) was developed, which can be processed to train and evaluate automated material classifiers. Specifically, the database contains multispectral recordings of the seven most common household plastics (PET, PP, PS, HDPE, LDPE, PVC, PTFE), captured by a nine-camera array setup equipped with distinct NIR bandpass filters. The approach to extract the spectral fingerprints involves a dedicated preprocessing pipeline that spatially aligns and normalizes the raw images to derive features such as wavelength intensity differences and false-color representations. Of course, this database can also be used for many other tasks, where reliable material identification is necessary, for example, evaluating machine learning models, optimizing spectral band selection, and developing cost-efficient industrial sorting pipelines. For details, please refer to the MHPM.md file.
