Influence of reflective materials, emitter intensity and foil thickness on the variability of near-infrared spectra of 2D plastic packaging materials

Koinig, Gerald; Friedrich, Karl; Rutrecht, Bettina; Barretta, Chiara; Vollprecht, Daniel

doi:10.1016/j.wasman.2021.12.019

Cited by 17 publications

(10 citation statements)

References 9 publications

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“…When focusing solely on bright plastic samples, the model exhibited decreased accuracy in detecting thinner plastic due to the loss of spectral information. This finding is consistent with the observations made by Koinig et al (2022) and Masoumi et al (2012). For LDPE and PP, plastic within the range of 2 mm to 4 mm demonstrates the highest accuracy, reaching 100%.…”

Section: Identification Of Field Samples Collected From Industrial Co...supporting

confidence: 94%

Using hyperspectral imaging to identify and classify large microplastic contamination in industrial composting processes

Taneepanichskul,

Hailes,

Miodownik

2024

Front. Sustain.

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Compostable plastics are used as alternatives to conventional (non-compostable) plastics due to their ability to decompose through industrial composting comingled with food waste. However conventional (non-compostable) plastics sometimes contaminate this industrial composting process resulting in the formation of microplastics in the end compost. Therefore, it is crucial to effectively identify the types of plastics entering industrial composters to improve composting rates and enhance compost quality. In this study, we applied Hyperspectral Imaging (HSI) with various pre-processing techniques in the short-wave infrared (SWIR) region to develop an efficient model for identifying and classifying plastics and large microplastics during the industrial composting process. The materials used in the experimental analysis included compostable plastics such as PLA and PBAT, and conventional (non-compostable) plastics including PP, PET, and LDPE. Chemometric techniques, namely Partial Least Squares Discriminant Analysis (PLS-DA), was applied to develop a classification model. The Partial Least Squares Discriminant Analysis (PLS-DA) model effectively distinguished between virgin PP, PET, PBAT, PLA, and PHA plastics and soil-contaminated plastics measuring larger than 20 mm × 20 mm, achieving accuracy of 100%. Furthermore, it demonstrated a 90% accuracy rate in discriminating between pristine large microplastics and those contaminated with soil. When we tested our model on plastic samples during industrial composting we found that the accuracy of identification depended on parameters such as darkness, size, color, thickness and contamination level. Nevertheless, we achieved 85% for plastics and large microplastics detected within compost.

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Section: Identification Of Field Samples Collected From Industrial Co...supporting

confidence: 94%

Using hyperspectral imaging to identify and classify large microplastic contamination in industrial composting processes

Taneepanichskul,

Hailes,

Miodownik

2024

Front. Sustain.

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“…Irrespective of the used reflector, the quality of the PP spectra is more susceptible to the thickness of the specimen. PP specimens exhibit sine wave-like noise disturbances of the spectra at a higher thickness than other polyolefins such as PE [ 8 , 17 ]. The spectra are shown in Figure 14 .…”

Section: Resultsmentioning

confidence: 99%

“…Secondly, the illumination intensity had to be lowered to prevent overexposed pixels. This was performed despite the results of previous research stating that increased illumination intensity improves the spectral quality [ 17 ].…”

Section: Methodsmentioning

confidence: 95%

“…Additionally, the minuscule amount of reflected radiation has not interacted sufficiently with the material to cause alterations in the spectra. Preliminary studies have shown that the minor signal alterations caused by the low material thickness in reflectance mode can be alleviated by adapting the experimental sensor-based sorting setup for measurements in transflectance mode [ 7 , 17 ].…”

Section: Methodsmentioning

confidence: 99%

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Evaluation of Improvements in the Separation of Monolayer and Multilayer Films via Measurements in Transflection and Application of Machine Learning Approaches

et al. 2022

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Small plastic packaging films make up a quarter of all packaging waste generated annually in Austria. As many plastic packaging films are multilayered to give barrier properties and strength, this fraction is considered hardly recyclable and recovered thermally. Besides, they can not be separated from recyclable monolayer films using near-infrared spectroscopy in material recovery facilities. In this paper, an experimental sensor-based sorting setup is used to demonstrate the effect of adapting a near-infrared sorting rig to enable measurement in transflection. This adaptation effectively circumvents problems caused by low material thickness and improves the sorting success when separating monolayer and multilayer film materials. Additionally, machine learning approaches are discussed to separate monolayer and multilayer materials without requiring the near-infrared sorter to explicitly learn the material fingerprint of each possible combination of layered materials. Last, a fast Fourier transform is shown to reduce destructive interference overlaying the spectral information. Through this, it is possible to automatically find the Fourier component at which to place the filter to regain the most spectral information possible.

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“…The sorting of the materials on the belt is achieved by means of compressed air blasts, which target the individual objects and transport them to a different belt than the rest of the materials. The plants have several NIR sorters, each filtering out different materials and, thus, forming the individual fractions [5,7,9,[11][12][13][14][15][16][17].…”

Section: Introduction 1recycling Of Disposable Packaging In Germanymentioning

confidence: 99%

Amount of Fill Product Residues in Plastic Packagings for Recycling

Schinkel,

Küppers,

Reichenbach

et al. 2023

Waste

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Fill product residues in packagings are equivalent to product losses. They are washed out after sorting and before commencing recycling processes. Not much data have been published about how much fill product is still present in packagings dedicated for recycling. Results are often from laboratory trials. Therefore, several hundred packagings from a sorting plant of a dual system in Germany were analysed to determine the amount of fill product residues. Approximately 10 wt. % of highly viscous fill products in tubes were lost as residue. In the case of packagings that were easy to empty, such as cups, and in the case of low-viscosity fill products, such as water, less than 1 wt. % of the fill products remained in the packagings. The mean amount of residue in relation to clean packaging was 0.9 g residue in 1 g of packaging material (without residue) in tubes and 0.07 g in PET bottles. These values were significantly lower for low-viscosity fill products compared to high-viscosity fill products, as expected.

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Influence of reflective materials, emitter intensity and foil thickness on the variability of near-infrared spectra of 2D plastic packaging materials

Cited by 17 publications

References 9 publications

Using hyperspectral imaging to identify and classify large microplastic contamination in industrial composting processes

Using hyperspectral imaging to identify and classify large microplastic contamination in industrial composting processes

Evaluation of Improvements in the Separation of Monolayer and Multilayer Films via Measurements in Transflection and Application of Machine Learning Approaches

Amount of Fill Product Residues in Plastic Packagings for Recycling

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