Plastics are widely used materials in society today, but only a small percentage of consumer plastics are recycled into new products. Plastics’ characteristics and costs favor their use in disposable and single-use products, which unfortunately gather as pollutants in our ecosystems due to poor waste management.
Image Credit: Nenad Nedomacki/Shutterstock.com
In a typical recycling process, plastic waste is reduced in size, for example, by chopping, washing, and then separating using flotation or mechanical sorting. Plastic purities range from 75-95% by mass, but the industrial sector requires plastic purities of 95%.
Henriksen, M. L. et al. analyzed plastics with a hyperspectral camera in the wavelength range of 1000-2500 nm, and the use of a hierarchical model helped identify individual plastic types.
This study determined whether detailed plastic-type recognition is possible utilizing reflection imaging using a hyperspectral camera (HC) technology in the wavelength range of 955-1700 nm with minimal data processing—only using normalization and a Savitzky-Golay filter. The spectral data was used to build an unsupervised machine learning model that can differentiate between types of plastic and categorize three unknown plastic samples.
Methodology
The study analyzed 13 commercially available plastics, set out in table 1.
Table 1. Plastic identification, plastic type, trade name, supplier for the materials included in this study. Source: Henriksen, et al., 2022
ID
Plastic type
Name
Supplier
HDPE
Polyethylene
PE 100
SIMONA
PP
Polypropylene
240-CA12
INEOS
PS
Polystyrene
CD cover
–
PVC
Poly(vinyl chloride)
PVC-U
GEHR
PVDF
Poly(vinylidene difluoride)
PVDF
GEHR
POM N
Polyoxymethylene
Ertacetal C
Mitsubishi
POM B
Polyoxymethylene
Ertacetal C
Mitsubishi
PEEK
Polyetheretherketone
Ketron PEEK 1000
Mitsubishi
ABS
Poly(acrylonitrile-butadiene-styrene)
Terluran GP35
INEOS
PMMA
Poly(methyl methacrylate)
Setacryl 1000
Madreperla
PC
Polycarbonate
Makrolon 2652
Covestro
PET
Poly(ethylene therphalate)
CB-602
UltraPurge
PA12
Polyamide 12
PA 12-TR
GEHR
U1
Unknown
Weighing boat (White)
VWR
U2
Unknown
Plexiglas
Rias
U3
Unknown
Safety glasses
VWR
The disks’ and unknown samples’ Fourier transformed infrared (FT-IR) spectra were gathered in an attenuated total reflection (ATR) mode on an iS5 spectrophotometer fitted with a ZnSe crystal.
Prior to measurement, a sample of 5–10 mg was cut from the sample discs and locked into aluminum pans. Samples of 5–10 mg are heated in a TG 209 F1 Libra from 35 °C to 800 °C at a rate of 10 °C min-1 in both laboratory air and nitrogen.
The hyperspectral camera (HC) setup is operated in its own space to reduce the impact of changing temperature, light, and humidity. In a typical experiment, the samples were loaded onto the conveyer belt and all lights, except the halogen lamps, were turned off. The samples were passed by the scan line at a speed of 3.75 m min-1 before being transferred to a laptop computer.
The spectra are documented as in 2.6.1, with the exceptions of 1) being kept as reflectance and 2) being cropped to 972–1675 nm. The loading matrix was extracted and used to post-process the unknown sample spectra, as shown in Table 1 by U1, U2, and U3.
Results and Discussion
Figure 1 shows the stacked FT-IR spectra of all materials.
Figure 1. FT-IR spectra for the materials tested. The spectra are shifted for visual clarity. Image Credit: Henriksen, et al., 2022
Table 2 shows melting temperature (Tm), glass transition temperature (Tg), and decomposition temperature (Td) determined in DSC and TGA.
Table 2. Glass transition temperature (Tg), melting temperature (Tm), and decomposition temperature (Td) for the materials analyzed by differential scanning calorimetry (DSC) and thermogravimetrical analysis (TGA). TGA data is given as the Td (mass loss%). Source: Henriksen, et al., 2022
Tg [°C]α
Tm [°C]α
Td [°C]β
Td [°C]γ
HDPE
–
138
427 (100)
473 (100)
PP
–
149
328 (100)
458 (100)
PS
101
–
367 (100)
415 (98)
PVC
82
–
284 (59) 433 (36)
292 (59) 468 (23)
PVDF
–
172
404 (57) 558 (41)
452 (68)
POM N
–
169
285 (94)
355 (99)
POM B
–
170
313 (100)
357 (100)
PEEK
–
341
580 (31) 661 (94)
592 (45)
ABS
110
–
411 (92) 552 (8)
428 (93)
PMMA
129
–
317 (98)
374 (99)
PC
149
–
506 (73) 618 (100)
513 (80)
PET
86
250
428 (85) 574 (100)
438 (84)
PA12
159
–
420 (93) 535 (100)
437 (98)
α DSC at 20 °C min−1.β TGA at 20 °C min−1 in air.γ TGA at 20 °C min−1 in N2.
The thermal properties in Table 2 show that the applied polymers contain little to no additives such as softeners, fillers, and so on. The materials are analyzed using an industrial HC setup, and Figure 2 shows the HC image of four discs at 1204 nm as well as the POM N spectra.
Related StoriesFigure 2. Left, four disks (left to right: PEEK, POM N, POM N, and POM B) presented at 1204 nm and POM N (marked with the area of averaging). Right, hyperspectral spectrum of POM N from scans averaged over 1, 10, 25, and 200 pixels, respectively. Image Credit: Henriksen, et al., 2022
Figure 2 depicts an HC image of four sample discs and the area over which spectral averaging is performed. Figure 3 depicts the spectra of ten ABS discs that were imaged sequentially.
Figure 3. Shows the spectra measured on ten different ABS specimens, each spectrum is an average of 200 pixels. Image Credit: Henriksen, et al., 2022
Figure 3 shows that the spectral data for the ten ABS samples is highly reproducible. Figure 4 depicts the HC spectra obtained from discs with modified surfaces.
Figure 4. Average spectra measured 10 ABS samples (Norm.), five ABS samples surface treated with a wood rasp (Rasp), and five ABS samples surface treated with screwdriver indentations. Image Credit: Henriksen, et al., 2022
The spectra of all 13 materials in Table 1 were obtained using industrial HC imaging, and the results are shown in Figure 5. Figure 6 shows a PCA score plot on the obtained spectra after Savitzky-Golay filtering.
Figure 5. Hyperspectral spectra for all materials averaged over 200 pixels and shifted for visual clarity. Image Credit: Henriksen, et al., 2022
Figure 6. Score plot of the first and second principal component (PC1 and PC2, respectively) from the principal component analysis made on the Savitzky-Golay filtered HC spectra. Calculated cluster centers (black circle), the unknown samples (X), symbols are the known material type, and a color is assigned to the predicted material type. Image Credit: Henriksen, et al., 2022
Figure 6’s score plot shows that the plastic types cluster with good separation.
Since this study is based on industrially available materials that may contain a wide range of additives, material verification was required. The results indicated that the included materials contain low or insignificant concentrations of organic additives.
The spectral quality as a function of surface texture and roughness was studied, and it was discovered that despite a reduction in overall intensity, presumably due to light scattering, the spectral information remained intact as shown in Figure 4.
The majority of the plastics examined are pure, but POM was added as both neutral and blue POM (Table 1 and Figure 5). The PCA (Figure 6) demonstrates that POM formed a dense cluster that was independent of color.
The PCA score-plots revealed that normalization and Savitzky-Golay filtering significantly improved polymer type grouping when compared to raw spectra analysis.
The close grouping of PS, PC, and ABS, on the other hand, increases the uncertainties in differentiating these plastic types.
The unidentified samples (U1, U2, and U3) were randomly chosen in the lab and imaged after the machine learning model was created, about a month after the reference plastics were calculated. The PCA of the unknown samples reveals that U1 is PS, U2 is PMMA, and U3 is PC.
Furthermore, the industrial potential is clearly illustrated because all samples are mapped on a running conveyer belt with an industrial spectrograph, data processing is reduced to a minimum, and machine learning demonstrates its strong predictive power to differentiate between types of plastic.
Conclusion
Hyperspectral imaging with wavelengths ranging from 955-1700 nm on 13 different plastics analyzed by PCA demonstrated that the spectral range is sufficient to distinguish plastics. Unsupervised machine learning has been shown to cluster plastic types, and the resulting loading matrix accurately classified unknown plastic samples.
Since all hyperspectral imaging is carried out with an industrially available spectrograph and with minimal data processing, it is possible to conclude that this evolving technology is the best tool for addressing recycled plastic purity and, thus, the global plastic challenge.
Journal Reference:
Henriksen, M. L., Karlsen, C. B., Klarskov, P., Hinge, M. (2022) Plastic classification via in-line hyperspectral camera analysis and unsupervised machine learning. Vibrational Spectroscopy, 118, p. 103329. Available Online: https://www.sciencedirect.com/science/article/pii/S0924203121001247?via%3Dihub.
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