PMID- 37006394
OWN - NLM
STAT- PubMed-not-MEDLINE
LR  - 20230404
IS  - 2352-3409 (Electronic)
IS  - 2352-3409 (Linking)
VI  - 48
DP  - 2023 Jun
TI  - NIR-MFCO dataset: Near-infrared-based false-color images of post-consumer 
      plastics at different material flow compositions and material flow presentations.
PG  - 109054
LID - 10.1016/j.dib.2023.109054 [doi]
LID - 109054
AB  - Determining mass-based material flow compositions (MFCOs) is crucial for 
      assessing and optimizing the recycling of post-consumer plastics. Currently, 
      MFCOs in plastic recycling are primarily determined through manual sorting 
      analysis, but the use of inline near-infrared (NIR) sensors holds potential to 
      automate the characterization process, paving the way for novel sensor-based 
      material flow characterization (SBMC) applications. This data article aims to 
      expedite SBMC research by providing NIR-based false-color images of plastic 
      material flows with their corresponding MFCOs. The false-color images were 
      created through the pixel-based classification of binary material mixtures using 
      a hyperspectral imaging camera (EVK HELIOS NIR G2-320; 990 nm-1678 nm wavelength 
      range) and the on-chip classification algorithm (CLASS 32). The resulting 
      NIR-MFCO dataset includes n = 880 false-color images from three test series: 
      (T1) high-density polyethylene (HDPE) and polyethylene terephthalate (PET) 
      flakes, (T2a) post-consumer HDPE packaging and PET bottles, and 
      (T2b) post-consumer HDPE packaging and beverage cartons for n = 11 different HDPE 
      shares (0% - 50%) at four different material flow presentations (singled, 
      monolayer, bulk height H1, bulk height H2). The dataset can be used, e.g., to 
      train machine learning algorithms, evaluate the accuracy of inline SBMC 
      applications, and deepen the understanding of segregation effects of 
      anthropogenic material flows, thus further advancing SBMC research and enhancing 
      post-consumer plastic recycling.
CI  - (c) 2023 The Author(s).
FAU - Kroell, Nils
AU  - Kroell N
AD  - Department of Anthropogenic Material Cycles, RWTH Aachen University, Wuellnerstr. 
      2, Aachen D-52062, Germany.
FAU - Chen, Xiaozheng
AU  - Chen X
AD  - Department of Anthropogenic Material Cycles, RWTH Aachen University, Wuellnerstr. 
      2, Aachen D-52062, Germany.
FAU - Maghmoumi, Abtin
AU  - Maghmoumi A
AD  - Department of Anthropogenic Material Cycles, RWTH Aachen University, Wuellnerstr. 
      2, Aachen D-52062, Germany.
FAU - Lorenzo, Julius
AU  - Lorenzo J
AD  - Department of Anthropogenic Material Cycles, RWTH Aachen University, Wuellnerstr. 
      2, Aachen D-52062, Germany.
FAU - Schlaak, Matthias
AU  - Schlaak M
AD  - Department of Anthropogenic Material Cycles, RWTH Aachen University, Wuellnerstr. 
      2, Aachen D-52062, Germany.
FAU - Nordmann, Christian
AU  - Nordmann C
AD  - STADLER Anlagenbau GmbH, Max-Planck-Str. 2, Altshausen D-88361, Germany.
FAU - Kuppers, Bastian
AU  - Kuppers B
AD  - STADLER Anlagenbau GmbH, Max-Planck-Str. 2, Altshausen D-88361, Germany.
FAU - Thor, Eric
AU  - Thor E
AD  - Department of Anthropogenic Material Cycles, RWTH Aachen University, Wuellnerstr. 
      2, Aachen D-52062, Germany.
FAU - Greiff, Kathrin
AU  - Greiff K
AD  - Department of Anthropogenic Material Cycles, RWTH Aachen University, Wuellnerstr. 
      2, Aachen D-52062, Germany.
LA  - eng
PT  - Journal Article
DEP - 20230314
PL  - Netherlands
TA  - Data Brief
JT  - Data in brief
JID - 101654995
PMC - PMC10051025
OTO - NOTNLM
OT  - Circular economy
OT  - Computer vision
OT  - Lightweight packaging waste
OT  - Machine learning
OT  - Mechanical plastic recycling
OT  - NIR spectroscopy
OT  - Polymers
OT  - Sensor-based material flow characterization
COIS- The authors declare that they have no known competing financial interests or 
      personal relationships that could have appeared to influence the work reported in 
      this paper.
EDAT- 2023/04/04 06:00
MHDA- 2023/04/04 06:01
PMCR- 2023/03/14
CRDT- 2023/04/03 03:46
PHST- 2023/02/16 00:00 [received]
PHST- 2023/03/03 00:00 [revised]
PHST- 2023/03/06 00:00 [accepted]
PHST- 2023/04/04 06:01 [medline]
PHST- 2023/04/03 03:46 [entrez]
PHST- 2023/04/04 06:00 [pubmed]
PHST- 2023/03/14 00:00 [pmc-release]
AID - S2352-3409(23)00172-5 [pii]
AID - 109054 [pii]
AID - 10.1016/j.dib.2023.109054 [doi]
PST - epublish
SO  - Data Brief. 2023 Mar 14;48:109054. doi: 10.1016/j.dib.2023.109054. eCollection 
      2023 Jun.