Comparison of latent variable‐based and artificial intelligence methods for impurity detection in PET recycling from NIR hyperspectral images

Galdón-Navarro, Borja; Prats-Montalbán, José Manuel; Cubero, Sergio; Blasco, José; Ferrer, Alberto

doi:10.1002/cem.2980

Cited by 16 publications

(7 citation statements)

References 37 publications

(70 reference statements)

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“…To categorize different polymers, data analysis methods and classification algorithms are needed to analyze the NIR spectral data. Some multivariate statistical methods are often adopted, like principal component analysis (PCA), , linear discriminant analysis (LDA), classification and regression trees (CART), partial least-squares discriminant analysis (PLS-DA), − spectral angle mapper (SAM), etc.…”

Section: Introductionmentioning

confidence: 99%

Classification of Common Household Plastic Wastes Combining Multiple Methods Based on Near-Infrared Spectroscopy

Duan

2021

ACS EST Eng.

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This work aims to classify seven common household plastic types which include polyethylene terephthalate (PET), high density polyethylene (HDPE), polyvinyl chloride (PVC), low density polyethylene (LDPE), polypropylene (PP), polystyrene (PS), and polycarbonate (PC) utilizing near-infrared (NIR) spectroscopy. Four methods including linear discriminant analysis (LDA), partial least-squares discriminant analysis (PLS-DA), spectral angle mapper (SAM), and support vector machine (SVM) were tested for their classification performances, and principal component analysis (PCA) was applied before LDA and SVM. All the classification models were built based on virgin plastics. The results showed that seven types of plastic could be classified excellently with all the methods when the test sets were composed of virgin samples. When the models were tested on waste plastics, most types could be well classified, and all the misclassifications occurred between HDPE and LDPE and PET and PC. Then for HDPE and LDPE and PET and PC that were prone to be misidentified, some specific spectral bands were reselected for further classification. To achieve the best result, an approach combining PCA, SVM, LDA, and PLS-DA was presented. The validation results showed significant improvement, with the F1 scores of LDPE and HDPE increasing from 65.2% to 86.7% and 24.2% to 84.7%, respectively, and 100% accuracy was achieved for the other five types.

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Section: Introductionmentioning

confidence: 99%

Classification of Common Household Plastic Wastes Combining Multiple Methods Based on Near-Infrared Spectroscopy

Duan

2021

ACS EST Eng.

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“…Much attention has been paid to this, and several publications deal with this problem [12,13], in particular, considering that r-PET for food packaging may derive from PET not used for food [3,14]. Moreover, the physical properties of r-PET have been studied [15][16][17], and several possible advanced applications, other than food packaging, have been evaluated [18][19][20].…”

Section: Introductionmentioning

confidence: 99%

Quantification of Recycled PET in Commercial Bottles by IR Spectroscopy and Chemometrics

Zappi,

Biancolillo,

Kassouf

et al. 2024

Analytica

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A novel approach for the quantification of recycled polyethylene terephthalate (r-PET) in commercial bottles is presented. Fifty-eight bottle samples from several brands and producers containing different percentages of r-PET were purchased from the market. Samples were analyzed by two spectroscopic methods: near-infrared (NIR) spectroscopy and attenuated total reflection (ATR) spectroscopy in the mid-infrared (MIR) region. No chemical pre-treatment was applied before analyses. The spectra were analyzed by partial-least squares (PLS) regression, and two models for NIR and MIR data were computed. Then, a multi-block regression was applied to join the two datasets. All models were validated by cross-validation and by excluding and projecting onto the model the replicated spectra of one sample at a time. Results demonstrated the potential of this approach, especially considering the variability of commercial samples in terms of additives, shape, or thickness of the bottles: for samples close to the centroids of the models (i.e., from 10 to 50% r-PET), the predictions of multi-block method seldom departed from the expected values of ±10%. Only for samples with 0% declared r-PET, the models showed poor prediction abilities.

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“…Information on the composition of the material can also be obtained by using spectrometry [14,15]. For better performance, NIR hyperspectral methods are integrated with artificial intelligence methods [16]. These methods are very effective in separating different materials from one another.…”

Section: Introductionmentioning

confidence: 99%

Vision-Based Sorting Systems for Transparent Plastic Granulate

Peršak

Viltužnik²,

Hernavs

et al. 2020

Applied Sciences

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Granulate material sorting is a mature and well-developed topic, due to its presence in various fields, such as the recycling, mining, and food industries. However, sorting can be improved, and artificial intelligence has been used for this purpose. This paper presents the development of an efficient sorting system for transparent polycarbonate plastic granulate, based on machine vision and air separation technology. The developed belt-type system is composed of a transparent conveyor with an integrated vision camera to detect defects in passing granulates. The vision system incorporates an industrial camera and backlight illumination. Individual particle localization and classification with the k-Nearest Neighbors algorithm were performed to determine the positions and conditions of each particle. Particles with defects are further separated pneumatically as they fall from the conveyor belt. Furthermore, an experiment was conducted whereby the combined performances of our sorting machine and classification method were evaluated. The results show that the developed system exhibits promising separation capabilities, despite numerous challenges accompanying the transparent granulate material.

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Comparison of latent variable‐based and artificial intelligence methods for impurity detection in PET recycling from NIR hyperspectral images

Cited by 16 publications

References 37 publications

Classification of Common Household Plastic Wastes Combining Multiple Methods Based on Near-Infrared Spectroscopy

Classification of Common Household Plastic Wastes Combining Multiple Methods Based on Near-Infrared Spectroscopy

Quantification of Recycled PET in Commercial Bottles by IR Spectroscopy and Chemometrics

Vision-Based Sorting Systems for Transparent Plastic Granulate

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