PMID- 37481925
OWN - NLM
STAT- PubMed-not-MEDLINE
LR  - 20230906
IS  - 1873-3557 (Electronic)
IS  - 1386-1425 (Linking)
VI  - 302
DP  - 2023 Dec 5
TI  - Multi-level color classification of post-consumer plastic packaging flakes by 
      hyperspectral imaging for optimizing the recycling process.
PG  - 123157
LID - S1386-1425(23)00842-9 [pii]
LID - 10.1016/j.saa.2023.123157 [doi]
AB  - In a circular economy perspective, the development of fast and efficient 
      sensor-based recognition strategies of plastic waste, not only by polymer but 
      also by color, plays a crucial role for the production of high quality secondary 
      raw materials in recycling plants. In this work, mixed colored flakes of 
      high-density polyethylene (HDPE) from packaging waste were simultaneously 
      classified by hyperspectral imaging working in the visible range (400-750 nm), 
      combined with machine learning. Two classification models were built and 
      compared: (1) Partial Least Square-Discriminant Analysis (PLS-DA) for 6 HDPE 
      macro-color classes identification (i.e., white, blue, green, red, orange and 
      yellow) and (2) hierarchical PLS-DA for a more accurate discrimination of the 
      different HDPE color tones, providing as output 14 color classes. The obtained 
      classification results were excellent for both models, with values of Recall, 
      Specificity, Accuracy, and F-score in prediction close to 1. The proposed 
      methodological approach can be utilized as sensor-based sorting logic in plastic 
      recycling plants, tuning the output based on the required needs of the recycling 
      plant, allowing to obtain a high-quality recycled HDPE of different colors, 
      optimizing the plastic recycling process, in agreement with the principles of 
      circular economy.
CI  - Copyright (c) 2023 The Authors. Published by Elsevier B.V. All rights reserved.
FAU - Cucuzza, Paola
AU  - Cucuzza P
AD  - Department of Chemical Engineering, Materials & Environment, Sapienza University 
      of Rome, Rome, Italy.
FAU - Serranti, Silvia
AU  - Serranti S
AD  - Department of Chemical Engineering, Materials & Environment, Sapienza University 
      of Rome, Rome, Italy. Electronic address: silvia.serranti@uniroma1.it.
FAU - Capobianco, Giuseppe
AU  - Capobianco G
AD  - Department of Chemical Engineering, Materials & Environment, Sapienza University 
      of Rome, Rome, Italy.
FAU - Bonifazi, Giuseppe
AU  - Bonifazi G
AD  - Department of Chemical Engineering, Materials & Environment, Sapienza University 
      of Rome, Rome, Italy.
LA  - eng
PT  - Journal Article
DEP - 20230714
PL  - England
TA  - Spectrochim Acta A Mol Biomol Spectrosc
JT  - Spectrochimica acta. Part A, Molecular and biomolecular spectroscopy
JID - 9602533
SB  - IM
OTO - NOTNLM
OT  - Circular economy
OT  - Color sorting
OT  - HDPE
OT  - Hierarchical model
OT  - Hyperspectral imaging
OT  - Machine learning
OT  - PLS-DA
OT  - Plastic waste
OT  - Recycling
COIS- Declaration of Competing Interest 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- 2023/07/24 00:41
MHDA- 2023/07/24 00:42
CRDT- 2023/07/23 18:06
PHST- 2023/03/31 00:00 [received]
PHST- 2023/06/25 00:00 [revised]
PHST- 2023/07/13 00:00 [accepted]
PHST- 2023/07/24 00:42 [medline]
PHST- 2023/07/24 00:41 [pubmed]
PHST- 2023/07/23 18:06 [entrez]
AID - S1386-1425(23)00842-9 [pii]
AID - 10.1016/j.saa.2023.123157 [doi]
PST - ppublish
SO  - Spectrochim Acta A Mol Biomol Spectrosc. 2023 Dec 5;302:123157. doi: 
      10.1016/j.saa.2023.123157. Epub 2023 Jul 14.