As a result of public pressure and government legislation to reduce plastic waste there has been a sharp rise in the manufacture and use of alternatives to conventional plastics including compostable and biodegradable plastics. If these plastics are not collected separately, they can contaminate plastic recycling, organic waste streams, and the environment. To deal with this contamination requires effective identification and sorting of these different polymer types to ensure they are separated and composted at end of life. This review provides the comprehensive overview of the identification and sorting technologies that can be applied to sort compostable and biodegradable plastics including gravity-based sorting, flotation sorting, triboelectrostatic sorting, image-based sorting, spectral based sorting, hyperspectral imaging and tracer-based sorting. The advantages and limitations of each sorting approach are discussed within a circular economy framework.
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.
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