2026-04-09 ワシントン州立大学（WSU）
＜関連情報＞
- https://news.wsu.edu/news/2026/04/09/wsu-researchers-test-ai-driven-spectral-imaging-for-identifying-recyclable-plastics/
- https://www.sciencedirect.com/science/article/abs/pii/S0921344925006445
振動分光法による一般的なプラスチックの種類識別 Identification of common types of plastics by vibrational spectroscopic techniques
Maria P.Garcia Tovar, Maria A.Villarreal Blanco, Oliva M. Primera-Pedrozo, Macy K. Christianson, Chih-Feng Wang, Pavel Valencia Acuña, John H. Miller, Luis de la Torre, Samuel P.Hernández Rivera
Resources, Conservation and Recycling Available online: 30 December 2025
DOI:https://doi.org/10.1016/j.resconrec.2025.108767
Graphical abstract
Abstract
Polyethylene Terephthalate (PET), High-Density Polyethylene (HDPE), Polyvinyl Chloride (PVC), Low-Density Polyethylene (LDPE), Polypropylene (PP), and Polystyrene (PS) account for most plastic use worldwide, with production nearing 380 million tons annually. A considerable portion enters municipal solid waste and landfills, creating long-term environmental concerns. Scaling recycling operations requires automated sorting technologies, with spectroscopy and machine learning offering promising solutions. In this study, a six-class convolutional neural network (CNN) was developed for plastic identification using vibrational spectroscopies. Raman Scattering (RS) spectra collected from recycling samples enabled accurate chemical differentiation while assessing the influence of visible features such as color. A CNN trained on RS data achieved 100 % classification accuracy. To strengthen field applicability, Attenuated Total Reflectance–Fourier Transform Infrared (ATR-FTIR) spectroscopy was incorporated, achieving 95% accuracy with a similar CNN model. These findings demonstrate the potential of integrating spectroscopy with deep learning for reliable plastic classification, advancing development of scalable, field-ready recycling technologies.
