Automatic Identification of Individual Nanoplastics by Raman Spectroscopy Based on Machine Learning

Lifang Xie, Siheng Luo, Yangyang Liu, Xuejun Ruan, Kedong Gong, Qiuyue Ge, Kejian Li, Ventsislav Kolev Valev, Guokun Liu, Liwu Zhang

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The increasing prevalence of nanoplastics in the environment underscores the need for effective detection and monitoring techniques. Current methods mainly focus on microplastics, while accurate identification of nanoplastics is challenging due to their small size and complex composition. In this work, we combined highly reflective substrates and machine learning to accurately identify nanoplastics using Raman spectroscopy. Our approach established Raman spectroscopy data sets of nanoplastics, incorporated peak extraction and retention data processing, and constructed a random forest model that achieved an average accuracy of 98.8% in identifying nanoplastics. We validated our method with tap water spiked samples, achieving over 97% identification accuracy, and demonstrated the applicability of our algorithm to real-world environmental samples through experiments on rainwater, detecting nanoscale polystyrene (PS) and polyvinyl chloride (PVC). Despite the challenges of processing low-quality nanoplastic Raman spectra and complex environmental samples, our study demonstrated the potential of using random forests to identify and distinguish nanoplastics from other environmental particles. Our results suggest that the combination of Raman spectroscopy and machine learning holds promise for developing effective nanoplastic particle detection and monitoring strategies.

Original languageEnglish
Pages (from-to)18203-18214
Number of pages12
JournalEnvironmental Science and Technology
Issue number46
Early online date3 Jul 2023
Publication statusPublished - 21 Nov 2023

Bibliographical note

Funding Information:
The authors gratefully acknowledge financial support from National Natural Science Foundation of China (no. 22176036, no. 21976030, and no. 22006020), the Natural Science Foundation of Shanghai (no. 19ZR1471200). V.K.V. acknowledges support from the Royal Society through the University Research Fellowships and the Royal Society grants PEF1\170015 and RGF\EA\180228, as well as the EPSRC grant EP/T001046/1. V.K.V. and L.Z. acknowledge the International Collaboration Awards 2020 of the Royal Society (No. ICA\R1\201088).


  • Machine Learning
  • Microplastics
  • Nanoplastics
  • Raman Spectroscopy
  • Random Forest

ASJC Scopus subject areas

  • General Chemistry
  • Environmental Chemistry


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