Plastic product consumption and disposal are widespread. Given that these products are derived from crude oil, it is critical to reduce their consumption and effectively recycle plastic waste as recycled resources to achieve a low-carbon society. Japan enacted the “Containers and Packaging Recycling Law” in 2000, encouraging the recycling of plastic packaging and containers. However, material recycling of plastic waste has stalled due to recycling costs and technical challenges. This paper examines and evaluates the transition process and operational status of Japan’s recycling policy for plastic containers and packaging, as well as clarifies the limits of resources recycling. The limits and issues of current identification technology are discussed by analyzing the characteristics of plastic marks and detecting the material compositions of waste samples. The paper also discusses a new sorting technology that uses terahertz waves to improve plastic recycling. This analysis revealed that plastic containers are typically made of two or more plastic materials, which makes resources recycling more difficult. Terahertz waves are safer than other high-accuracy sorting technologies currently in use. Thus, material recycling can be expanded by accurately analyzing the composition of plastic waste and introducing sorting devices appropriate for achieving the circular economy with sustainable resource recycling.

Accurate identification of plastic materials from post-consumer food container and packaging waste is crucial for enhancing the purity and added value of recycled materials, thereby promoting recycling and addressing the issue of plastic pollution. However, the diverse characteristics of post-consumer plastics-such as variations in shape and additives-cause variations in spectral features like transmittance, even within the same material type. In this study, we combined near-infrared (NIR) and terahertz (THz) spectroscopies with machine learning (ML) techniques, specifically XGBoost and Bayesian optimization, to accurately identify transparent polyethylene terephthalate (PET), transparent polystyrene (PS), and black PS. We achieved a precision score exceeding 90%. Furthermore, using explainable AI (XAI) techniques, we evaluated the roles of NIR and THz waves in distinguishing between these plastics. We found that transmittance measured at a frequency of 0.140 THz was effective for identifying transparent PS, while the transmittance at 0.075 THz was crucial for identifying transparent PET. Additionally, NIR spectroscopy proved to be highly effective in distinguishing black PS from transparent plastics. Our findings indicate that the significance of THz frequencies varies depending on the material, highlighting that the identification technology developed in this study not only complements widely used NIR spectroscopy but also offers valuable insights into selecting effective frequencies for high-precision identification systems. Additionally, we discuss potential directions for further research to advance identification systems utilizing THz spectroscopy and ML techniques based on these findings.

An analytical processing design is proposed to accumulate nano-plastics diluted in water-based solvents and evaluate their individual IR spectral properties.