Automatic identification and classification of compostable and biodegradable plastics using hyperspectral imaging

Abstract

In the UK waste management systems biodegradable and compostable packaging are not automatically detected and separated. As a result, their fate is generally landfill or incineration, neither of which is an environmentally good outcome. Thus, effective sorting technologies for compostable plastics are needed to help improve composting rates of these materials and reduce the contamination of recycling waste streams. Hyperspectral imaging (HSI) was applied in this study to develop classification models for automatically identifying and classifying compostable plastics with the analysis focused on the spectral region 950–1,730 nm. The experimental design includes a hyperspectral imaging camera, allowing different chemometric techniques to be applied including principal component analysis (PCA) and partial least square discriminant analysis (PLS-DA) to develop a classification model for the compostable materials plastics. Materials used in this experimental analysis included compostable materials (sugarcane-derived and palm leaf derived), compostable plastics (PLA, PBAT) and conventional plastics (PP, PET, and LDPE). Our strategy was to develop a classification model to identify and categorize various fragments over the size range of 50 x 50 mm to 5 x 5 mm. Results indicated that both PCA and PLS-DA achieved classification scores of 100% when the size of material was larger than 10 mm x 10 mm. However, the misclassification rate increased to 20% for sugarcane-derived and 40% for palm leaf-based materials at sizes of 10 x 10 mm or below. In addition, for sizes of 5 x 5 mm, the misclassification rate for LDPE and PBAT increased to 20%, and for sugarcane and palm-leaf based materials to 60 and 80% respectively while the misclassification rate for PLA, PP, and PET was still 0%. The system is capable of accurately sorting compostable plastics (compostable spoons, forks, coffee lids) and differentiating them from identical looking conventional plastic items with high accuracy.

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

“…It was employed to move samples from left to right at an adjustable speed. The geometry of the experimental set-up is shown in our previous work, described in detail in Xiong et al (2014) and Taneepanichskul et al (2023). HyspexGround software was used for SWIR-HSI image acquisition, raw absorbance spectra collection and preliminary spectral analysis.…”

A Survey of the State of the Art in Sensor-Based Sorting Technology and Research

“…This is the author's version which has not been fully edited and content may change prior to final publication. Citation information: DOI 10.1109/ACCESS.2024.3350987 and biodegradable plastics, sorting of these has also drawn scientific attention [75].…”

Molecular Characterization of Plastic Waste Using Standoff Photothermal Spectroscopy

“…Efforts are presently underway in several research groups to solve this challenge by developing automated plastic waste sorting techniques, for example, Unisort, MAX-AI, MSS CIRCUS PlasticMax, TOMRA, etc. [28][29][30][31] These advanced plastic sorters employ various techniques, including near-infrared radiation (NIR), Artificial Intelligence (AI) robotic arm, and hyperspectral imaging. [31][32][33][34] However, these techniques have their own limitations.…”