In Electrophoresis ; h5-index 0.0
Raman spectral detection has emerged as a powerful analytical technique due to the advantages of fast acquisition, non-invasion and low cost. The on-site application is highly dependent on Raman automatic analysis algorithm. However, current Raman algorithm research mainly focuses on small sample Raman spectroscopy identification with defects of low accuracy and detection rate. It is also difficult to realize rapid Raman spectroscopy measurement under big data. In this paper, rapid recognition of mixtures in complex environments was realized by establishing a fast Raman analysis model based on deep learning through data training, self-learning, and parameter optimization. The cloud network architecture was proposed to apply deep learning to real-time detection using Smartphone-based Raman devices. This research solves the technical problems about mixture recognition under big data and thus could be used as a new method for fast and field RS detection in complex environments. This article is protected by copyright. All rights reserved.
Liang Jie, Mu Taotao
Cloud platform, Deep learning, Mixture recognition, Raman spectrometer, Recognition algorithm