Jump to main content
Jump to site search
Access to RSC content Close the message box

Continue to access RSC content when you are not at your institution. Follow our step-by-step guide.



Deep learning networks for the recognition and quantitation of surface-enhanced Raman spectroscopy

Abstract

Surface-enhanced Raman spectroscopy (SERS) based on machine learning methods has been applied in material analysis, biological detection, food safety, and intelligent analysis. However, machine learning methods generally require extra preprocessing or feature engineering, and handling large-scale data using these methods is challenging. In this study, deep learning networks were used as fully connected network, convolution neural network (CNN), fully convolutional network (FCN), and principal component analysis network (PCANet) to determine their abilities to recognise drugs in human urine and measure pirimiphos-methyl in wheat extract in the two input forms of one-dimensional vector or two-dimensional matrix. The best recognition result for drugs in urine with an accuracy of 98.05% in the prediction set was obtained using CNN with spectra as input in the matrix form. The optimal quantitation for pirimiphos-methyl was obtained using FCN with spectra in matrix form, which realised the analysis with a determination coefficient of 0.9997 and a root mean square error of 0.1574 in the prediction set. These networks performed better than the common machine learning methods. Overall, the deep learning networks provide feasible alternatives for the recognition and quantitation of SERS.

Back to tab navigation

Supplementary files

Article information


Submitted
12 Mar 2020
Accepted
15 May 2020
First published
18 May 2020

Analyst, 2020, Accepted Manuscript
Article type
Paper

Deep learning networks for the recognition and quantitation of surface-enhanced Raman spectroscopy

S. Weng, H. Yuan, X. Zhang, P. Li, L. Zheng, J. Zhao and L. Huang, Analyst, 2020, Accepted Manuscript , DOI: 10.1039/D0AN00492H

Social activity

Search articles by author

Spotlight

Advertisements