Research Advances of Deep Learning-based Raman Spectroscopy and Their Application in Detection of Microplastics
10.19756/j.issn.0253-3820.241126
- VernacularTitle:基于深度学习的拉曼光谱及其在微塑料检测中的应用研究进展
- Author:
Yong-Hui HAN
1
;
Chun-Bo SHI
;
Wang LIANG
;
Xiao-Yue ZHANG
;
Jian-Sheng CUI
;
Bo YAO
Author Information
1. 河北科技大学环境科学与工程学院,石家庄 050018;河北省固体废弃物资源化技术创新中心,石家庄 050018
- Keywords:
Microplastics;
Raman spectroscopy;
Deep learning;
Environmental monitoring;
Review
- From:
Chinese Journal of Analytical Chemistry
2025;53(2):153-163
- CountryChina
- Language:Chinese
-
Abstract:
Microplastics are widely present in various environments such as water bodies,land,and atmosphere,which pose threats to the ecological environment and human health through transmission and accumulation in the food chain.The existing detection techniques for microplastics face challenges such as complex preparation procedure of samples,low efficiency in processing large batches of samples,and difficulties in handling complex samples.Therefore,there is an urgent need for rapid and efficient detection techniques suitable for complex microplastics samples in the field of environmental monitoring.Raman spectroscopy,known for its advantages such as rapidity,accuracy,high sensitivity,non-destructiveness,and non-contact,demonstrates great application potential in detection of microplastics.Deep learning,an artificial intelligence method known for its large-scale data processing,nonlinear modeling and automatic feature extraction capabilities,is receiving increasing attention in the analysis of Raman spectroscopy signals.The application of deep learning-based Raman spectroscopy has significantly improved performance indicators such as detection efficiency and accuracy.This article introduced the existing Raman enhancement techniques,summarized the deep learning methods applied in Raman spectroscopy signal analysis,reviewed the recent research and application progress of deep learning-based Raman spectroscopy in detection of microplastics,and finally discussed the challenges and future prospects of deep learning-based Raman spectroscopy in detection of microplastics.
