Post hoc support vector machine learning for impedimetric biosensors based on weak protein–ligand interactions-Reference-Cited by-同舟云学术

Post hoc support vector machine learning for impedimetric biosensors based on weak protein–ligand interactions

Published:2018 Issue:9 Volume:143 Page:2066-2075
ISSN:0003-2654
Container-title:The Analyst
language:en
Short-container-title:Analyst

Author:

Rong Y.¹²³⁴,Padron A. V.¹²³⁴,Hagerty K. J.¹²³⁴,Nelson N.⁵⁶⁴,Chi S.⁷⁸⁹¹⁰¹¹,Keyhani N. O.¹²²³⁴,Katz J.¹³³⁴,Datta S. P. A.¹⁴¹⁵¹⁶⁴¹⁷,Gomes C.¹⁵¹⁸⁴^ORCID,McLamore E. S.¹²³⁴^ORCID

Affiliation:

1. Agricultural & Biological Engineering

2. Institute of Food and Agricultural Sciences

3. University of Florida

4. USA

5. Biological & Agricultural Engineering

6. North Carolina State University

7. Institute of Agricultural Resources and Regional Planning

8. Chinese Academy of Agricultural Sciences; Key Laboratory of Microbial Resources

9. Ministry of Agriculture

10. Beijing

11. China

12. Department of Microbiology and Cell Sciences

13. Department of Oral and Maxillofacial Diagnostic Sciences

14. MIT Auto-ID Labs

15. Department of Mechanical Engineering

16. Massachusetts Institute of Technology

17. Biomedical Engineering Program

18. Iowa State University

Abstract

We develop a simple, open source machine learning algorithm for analyzing impedimetric biosensor data using a mobile phone.

Funder

University of Florida

Division of Chemical, Bioengineering, Environmental, and Transport Systems

China Scholarship Council

Publisher

Royal Society of Chemistry (RSC)

Subject

Electrochemistry,Spectroscopy,Environmental Chemistry,Biochemistry,Analytical Chemistry

Link

http://pubs.rsc.org/en/content/articlepdf/2018/AN/C8AN00065D

Reference60 articles.

1. Biosensors: sense and sensibility

2. Human and planetary health: towards a common language

3. Smartphone-based cyclic voltammetry system with graphene modified screen printed electrodes for glucose detection

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5. Smartphone-based portable biosensing system using impedance measurement with printed electrodes for 2,4,6-trinitrotoluene (TNT) detection

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