Hyperspectral imaging for VIS-SWIR classification of post-consumer plastic packaging products by polymer and color

Serranti, Silvia; Cucuzza, Paola; Bonifazi, Giuseppe

doi:10.1117/12.2580504

Cited by 9 publications

(8 citation statements)

References 8 publications

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“…SVM algorithm works based on the construction of a hyperplane that serves as a decision boundary between different classes (Musu et al, 2019;Yang et al, 2020;Yu et al, 2014;Zhu et al, 2019). These machine learning techniques have been found to give good results with IR spectra (Bonifazi et al, 2014;Calvini et al, 2018;Karaca et al, 2013;Rani et al, 2019;Said et al, 2020;S Serranti et al, 2020;Silvia Serranti et al, 2012Ulrici et al, 2013;Wu et al, 2020;Yang et al, 2020;Zhu et al, 2019), Raman spectra (Allen et al, 1999;L. Chen et al, 2017;Musu et al, 2019;Silva & Wiebeck, 2019) and LIBS spectra (Costa et al, 2017;Vahid Dastjerdi et al, 2018;Yan et al, 2021;Yu et al, 2014).…”

Section: Literature Reviewmentioning

confidence: 99%

Deep Learning for Chemometric Analysis of Plastic Spectral Data from Infrared and Raman Databases

Neo¹,

Low

Goodship³

et al. 2022

SSRN Journal

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Section: Literature Reviewmentioning

confidence: 99%

Deep Learning for Chemometric Analysis of Plastic Spectral Data from Infrared and Raman Databases

Neo¹,

Low

Goodship³

et al. 2022

SSRN Journal

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“…The sorting and processing will vary greatly from location to location due to different plastic waste collection processes, waste stream composition, recycling infrastructures, available equipment, and capacity, but also by the recycled plastic type market demand [21]. There are several separation and identification techniques which can be used, such as sinkfloat [65,66], froth flotation [67], near/mid infrared spectroscopy [68][69][70], X-ray fluorescence spectroscopy [71,72], hydrocyclones [73][74][75], tribo-electrostatic [76][77][78], magnetic density sorting [79,80], magnetic levitation [81,82], hyperspectral imaging [83][84][85], laser induced breakdown spectroscopy [86][87][88], hybrid jig [89], and many others. There are several reviews in the literature discussing the techniques, principles, advantages, and drawbacks in detail [18,46,79,[90][91][92][93][94].…”

Section: Mechanical Recyclingmentioning

confidence: 99%

Polyolefins and Polyethylene Terephthalate Package Wastes: Recycling and Use in Composites

et al. 2021

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Plastics are versatile materials used in a variety of sectors that have seen a rapid increase in their global production. Millions of tonnes of plastic wastes are generated each year, which puts pressure on plastic waste management methods to prevent their accumulation within the environment. Recycling is an attractive disposal method and aids the initiative of a circular plastic economy, but recycling still has challenges to overcome. This review starts with an overview of the current European recycling strategies for solid plastic waste and the challenges faced. Emphasis lies on the recycling of polyolefins (POs) and polyethylene terephthalate (PET) which are found in plastic packaging, as packaging contributes a signification proportion to solid plastic wastes. Both sections, the recycling of POs and PET, discuss the sources of wastes, chemical and mechanical recycling, effects of recycling on the material properties, strategies to improve the performance of recycled POs and PET, and finally the applications of recycled POs and PET. The review concludes with a discussion of the future potential and opportunities of recycled POs and PET.

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“…NIR hyperspectral imaging is also used in science [ 15 , 16 ]. Spectral data are preprocessed and analyzed using statistical, machine learning, and deep learning methods [ 17 ].…”

Section: Introductionmentioning

confidence: 99%

Detection of Plastic Granules and Their Mixtures

Kulko

Pletl

Hanus³

et al. 2023

Sensors

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Chemically pure plastic granulate is used as the starting material in the production of plastic parts. Extrusion machines rely on purity, otherwise resources are lost, and waste is produced. To avoid losses, the machines need to analyze the raw material. Spectroscopy in the visible and near-infrared range and machine learning can be used as analyzers. We present an approach using two spectrometers with a spectral range of 400–1700 nm and a fusion model comprising classification, regression, and validation to detect 25 materials and proportions of their binary mixtures. one dimensional convolutional neural network is used for classification and partial least squares regression for the estimation of proportions. The classification is validated by reconstructing the sample spectrum using the component spectra in linear least squares fitting. To save time and effort, the fusion model is trained on semi-empirical spectral data. The component spectra are acquired empirically and the binary mixture spectra are computed as linear combinations. The fusion model achieves very a high accuracy on visible and near-infrared spectral data. Even in a smaller spectral range from 400–1100 nm, the accuracy is high. The visible and near-infrared spectroscopy and the presented fusion model can be used as a concept for building an analyzer. Inexpensive silicon sensor-based spectrometers can be used.

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Hyperspectral imaging for VIS-SWIR classification of post-consumer plastic packaging products by polymer and color

Cited by 9 publications

References 8 publications

Deep Learning for Chemometric Analysis of Plastic Spectral Data from Infrared and Raman Databases

Deep Learning for Chemometric Analysis of Plastic Spectral Data from Infrared and Raman Databases

Polyolefins and Polyethylene Terephthalate Package Wastes: Recycling and Use in Composites

Detection of Plastic Granules and Their Mixtures

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