Deep learning networks for the recognition and quantitation of surface-enhanced Raman spectroscopy-Reference-Cited by-同舟云学术

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

Published:2020 Issue:14 Volume:145 Page:4827-4835
ISSN:0003-2654
Container-title:The Analyst
language:en
Short-container-title:Analyst

Author:

Weng Shizhuang¹²³⁴^ORCID,Yuan Hecai¹²³⁴,Zhang Xueyan¹²³⁴,Li Pan⁵⁶⁷⁸⁴^ORCID,Zheng Ling¹²³⁴,Zhao Jinling¹²³⁴,Huang Linsheng¹²³⁴

Affiliation:

1. National Engineering Research Center for Agro-Ecological Big Data Analysis & Application

2. Anhui University

3. Hefei 230601

4. China

5. Center of Medical Physics and Technology

6. Hefei Institutes of Physical Science

7. CAS

8. Hefei 230021

Abstract

Surface-enhanced Raman spectroscopy (SERS) based on machine learning methods has been applied in material analysis, biological detection, food safety, and intelligent analysis.

Funder

National Natural Science Foundation of China

National Basic Research Program of China

Ministry of Agriculture of the People's Republic of China

Publisher

Royal Society of Chemistry (RSC)

Subject

Electrochemistry,Spectroscopy,Environmental Chemistry,Biochemistry,Analytical Chemistry

Link

http://pubs.rsc.org/en/content/articlepdf/2020/AN/D0AN00492H

Reference36 articles.

1. Raman fingerprint of chromate, aluminate and ferrite spinels

2. SERS: Materials, applications, and the future

3. Rapid Discrimination of Malaria- and Dengue-Infected Patients Sera Using Raman Spectroscopy

4. Determination of thiabendazole in orange juice using an MISPE-SERS chemosensor

5. Concentration, detection and discrimination of Bacillus anthracis spores in orange juice using aptamer based surface enhanced Raman spectroscopy

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