AI integration into wavelength-based SPR biosensing: Advancements in spectroscopic analysis and detection.

Journal: Analytica chimica acta
PMID: 39880496

Abstract

BACKGROUND: In recent years, employing deep learning methods in the biosensing area has significantly reduced data analysis time and enhanced data interpretation and prediction accuracy. In some SPR fields, research teams have further enhanced detection capabilities using deep learning techniques. However, the application of deep learning to spectroscopic surface plasmon resonance (SPR) biosensors has not been reported. This study addresses the integration of AI methods to improve the signal-to-noise ratio (SNR) and detection accuracy of wavelength-based portable SPR biosensors.

Authors

  • Ying-Feng Chang
    Artificial Intelligence Research Center, Chang Gung University, Kweishan District, Taoyuan City 33302, Taiwan.
  • Yu-Chung Wang
    Artificial Intelligence Research Center, Chang Gung University, Taoyuan, 333323, Taiwan; Department of Artificial Intelligence, College of Intelligent Computing, Chang Gung University, Taoyuan, 333323, Taiwan.
  • Tsung-Yu Huang
    Department of Materials Engineering, Ming Chi University of Technology, New Taipei City, 243303, Taiwan.
  • Meng-Chi Li
    General Education Center, Ming Chi University of Technology, New Taipei City, 243303, Taiwan; Organic Electronics Research Center, Ming Chi University of Technology, New Taipei City, 243303, Taiwan.
  • Sin-You Chen
    Artificial Intelligence Research Center, Chang Gung University, Kweishan District, Taoyuan City 33302, Taiwan.
  • Yu-Xen Lin
    TeraOptics Corporation, Taoyuan, 320317, Taiwan.
  • Li-Chen Su
    General Education Center, Ming Chi University of Technology, New Taipei City, 243303, Taiwan; Organic Electronics Research Center, Ming Chi University of Technology, New Taipei City, 243303, Taiwan. Electronic address: sulichen@o365.mcut.edu.tw.
  • Kwei-Jay Lin
    University of California Irvine, Irvine, 92697, USA; Chang Gung University, Taoyuan, 33302, China. Electronic address: klin@cgu.edu.tw.

Keywords

External Resources

Popular Topics
Recent Journals