Quantification of moisture in household plastic packaging waste using near-infrared hyperspectral imaging (NIR-HSI)

Pim van den Brink; Stefan Bontekoe; Homer C. Genuino; Marcel C. P. van Eijk; Pim van den Brink; Stefan Bontekoe; Homer C. Genuino; Marcel C. P. van Eijk

doi:10.3934/ctr.2025003

Clean Technologies and Recycling

2025, Volume 5, Issue 1: 44-63. doi: 10.3934/ctr.2025003

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Research article

Quantification of moisture in household plastic packaging waste using near-infrared hyperspectral imaging (NIR-HSI)

1.
NTCP, Duitslanddreef 7, 8447 SE Heerenveen, the Netherlands
2.
Circular Chemical Engineering, Maastricht University, Urmonderbaan 22, 6167 RD Geleen, the Netherlands

Received: 04 November 2024 Revised: 07 April 2025 Accepted: 27 May 2025 Published: 05 June 2025

Quantifying moisture in plastic waste is crucial for optimizing recycling processes and improving the quality of recycled materials. Conventional methods, such as gravimetric analysis, are laborious and energy-intensive, limiting their efficiency in high-throughput industrial environments. This study presents and validates the use of near-infrared hyperspectral imaging (NIR-HSI) as a rapid, non-destructive method for moisture analysis in household plastic packaging waste (i.e., PE and PP films and rigids, PET, mixed plastics). By utilizing an NIR-HSI camera on a data collection conveyor belt, samples with varying moisture levels were analyzed. The method employs univariate calibration, correlating NIR absorbance from water with moisture concentration determined by the standard gravimetric method. To ensure accuracy, NIR absorbance from water was isolated by identifying and eliminating polymer-related absorbance through peak annotation. Principal component analysis (PCA) was subsequently applied to distinguish between rigids and films. Further refinement was achieved by normalizing the spectra and subtracting a dry reference spectrum, effectively eliminating the polymer signal. This approach enabled accurate quantification of moisture content and provided spatially resolved information on moisture distribution, including subsurface moisture. The method was successfully implemented in a pilot-scale sorting facility, where 95% of measurements achieved an accuracy within 2.6 percentage points. This integration underscores the significant potential of NIR-HSI for inline analysis and real-time feedback in recycling operations, offering significant advancements for future research and industrial applications in plastic waste recycling.
- plastic recycling,
- moisture quantification,
- near-infrared hyperspectral imaging (NIR-HSI),
- principal component analysis (PCA),
- inline analysis
Citation: Pim van den Brink, Stefan Bontekoe, Homer C. Genuino, Marcel C. P. van Eijk. Quantification of moisture in household plastic packaging waste using near-infrared hyperspectral imaging (NIR-HSI)[J]. Clean Technologies and Recycling, 2025, 5(1): 44-63. doi: 10.3934/ctr.2025003

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Abstract

Quantifying moisture in plastic waste is crucial for optimizing recycling processes and improving the quality of recycled materials. Conventional methods, such as gravimetric analysis, are laborious and energy-intensive, limiting their efficiency in high-throughput industrial environments. This study presents and validates the use of near-infrared hyperspectral imaging (NIR-HSI) as a rapid, non-destructive method for moisture analysis in household plastic packaging waste (i.e., PE and PP films and rigids, PET, mixed plastics). By utilizing an NIR-HSI camera on a data collection conveyor belt, samples with varying moisture levels were analyzed. The method employs univariate calibration, correlating NIR absorbance from water with moisture concentration determined by the standard gravimetric method. To ensure accuracy, NIR absorbance from water was isolated by identifying and eliminating polymer-related absorbance through peak annotation. Principal component analysis (PCA) was subsequently applied to distinguish between rigids and films. Further refinement was achieved by normalizing the spectra and subtracting a dry reference spectrum, effectively eliminating the polymer signal. This approach enabled accurate quantification of moisture content and provided spatially resolved information on moisture distribution, including subsurface moisture. The method was successfully implemented in a pilot-scale sorting facility, where 95% of measurements achieved an accuracy within 2.6 percentage points. This integration underscores the significant potential of NIR-HSI for inline analysis and real-time feedback in recycling operations, offering significant advancements for future research and industrial applications in plastic waste recycling.

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