In-line identification of Pb-based pigments in fishing nets and ropes based on hyperspectral imaging and machine learning

Georgiana Amariei, Martin Lahn Henriksen, Jakob Brøndum Friis, Pernille Klarskov Pedersen, Mogens Hinge*

*Corresponding author for this work

Research output: Contribution to journal/Conference contribution in journal/Contribution to newspaperJournal articleResearchpeer-review

1 Citation (Scopus)
76 Downloads (Pure)

Abstract

Fishing lines, nets, and ropes represent a significant portion of plastic pollution in marine environments, and can contain hazardous additives. The development of less laborious and faster methods aiming at identifying plastic-related additives is therefore needed, in order to facilitate effective recycling. This work aims to develop an industrial inline method to identify lead-based pigments in fishnets by an industrial hyperspectral imaging (HSI) system working in visible-near-infrared spectral range (Vis-NIR, 450 to 1050 nm) and machine learning. A Vis-NIR spectral sample set comprising un-contaminated and lead contaminated (143 to 2430 mg L−1) plastic classes were used to build the classification model via Principal Component Analysis and clustering. The content of the samples was characterized by X-ray fluorescence (XRF), Attenuated Total Reflection (ATR-FTIR), differential scanning calorimetry, thermogravimetric analysis, and burning in astmospheric air. Fishnets containing lead-based pigments with lead concentrations > 1000 mg L−1 (0.1 wt%) were accurately identified by the industrial HSI, and the lead content was corroborated with ATR-FTIR and XRF measurements. In addition, lead contaminated plastic area and mass can be estimated via calibration curve using the pixels numbers vs mass of fibrous plastics with a detectability of 120 mg (R2 = 0.997).

Original languageEnglish
Article number114910
JournalMarine Pollution Bulletin
Volume191
ISSN0025-326X
DOIs
Publication statusPublished - Jun 2023

Keywords

  • Fishing nets and ropes
  • Hyperspectral imaging
  • Lead-based pigments
  • Machine learning
  • Ocean waste

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