반도체디스플레이기술학회지 (Journal of the Semiconductor & Display Technology)

한국반도체디스플레이기술학회 (The Korean Society Of Semiconductor & Display Technology)
권호 표지

Near Field IR (NIR) 스펙트럼 및 결정 트리 기반 기계학습을 이용한 플라스틱 재질 분류 시스템

The Evaluation of a Plastic Material Classification System using Near Field IR (NIR) Spectrum and Decision Tree based Machine Learning

  • 국중진 (상명대학교 전자정보시스템공학과)
  • Kook, Joongjin (Dept. of Electronics and Information System Engineering, Sangmyung University)
  • 투고 : 2022.09.08
  • 심사 : 2022.09.19
  • 발행 : 2022.09.30
PDF 다운로드
⟨ 이전 논문 다음 논문 ⟩

초록

Plastics are classified into 7 types such as PET (PETE), HDPE, PVC, LDPE, PP, PS, and Other for separation and recycling. Recently, large corporations advocating ESG management are replacing them with bioplastics. Incineration and landfill of disposal of plastic waste are responsible for air pollution and destruction of the ecosystem. Because it is not easy to accurately classify plastic materials with the naked eye, automated system-based screening studies using various sensor technologies and AI-based software technologies have been conducted. In this paper, NIR scanning devices considering the NIR wavelength characteristics that appear differently for each plastic material and a system that can identify the type of plastic by learning the NIR spectrum data collected through it. The accuracy of plastic material identification was evaluated through a decision tree-based SVM model for multiclass classification on NIR spectral datasets for 8 types of plastic samples including biodegradable plastic.

키워드

참고문헌

  1. Kim, L., "Plastic Korea," Greenpeace, 2019.
  2. Urickova, V., Sadecka, J. Determination of geographical origin of alcoholic beverages using ultraviolet, visible and infrared spectroscopy: A review. Spectrochimica acta. Part A, Molecular and biomolecular spectroscopy 2015, 148, 131-137. https://doi.org/10.1016/j.saa.2015.03.111
  3. Rozenstein, O.; Puckrin, E.; Adamowski, J. Development of a new approach based on midwave infrared spectroscopy for post-consumer black plastic waste sorting in the recycling industry. Waste management (Elmsford) 2017, 68, 38-44. https://doi.org/10.1016/j.wasman.2017.07.023
  4. KAIHARA, M.; SATOH, M.; SATOH, M. Systematization Method for Distinguishing Plastic Groups by Using NIR Spectroscopy. Analytical sciences 2007, 23, 921-924. https://doi.org/10.2116/analsci.23.921
  5. Jeong, J., Cho, Joong Hwee., A Through-focus Scanning Optical Microscopy Dimensional Measurement Method based on a Deep-learning Regression Model, Journal of the Semiconductor & Display Technology 2022, 21(1), 108-113.
  6. Rinnan, A; Berg, F.v.d.; Engelsen, S.B. Review of the most common pre-processing techniques for near-infrared spectra. TrAC, Trends in analytical chemistry (Regular ed.) 2009, 28, 1201-1222. https://doi.org/10.1016/j.trac.2009.07.007
  7. Jerry de Vos, Plastic Identification Anywhere Development of open-source tools to simplify plastic sorting, February 2021, Master Thesis.