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SmartSPR sensor: Machine learning approaches to create intelligent surface plasmon based sensors.

Julio Cartier M Gomes1, Leandro Carlos Souza2, Leiva Casemiro Oliveira1

  • 1Universidade Federal Rural do Semi-Árido, Departament of Computer Science, Rua Francisco Mota Bairro, 572 - Pres. Costa e Silva, Mossoró - RN, Brazil.

Biosensors & Bioelectronics
|November 16, 2020
PubMed
Summary

Machine learning techniques enhance the reliability of surface plasmon resonance (SPR) sensors by analyzing real-time molecular interaction data (sensorgrams). This leads to intelligent biosensors for accurate substance detection and diagnostics.

Keywords:
Automated diagnosis testingMachine learningOptical sensorSPR intelligent sensorSmart biosensorSurface plasmon resonance

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Area of Science:

  • Biotechnology and Biosensing
  • Machine Learning Applications in Diagnostics
  • Analytical Chemistry

Background:

  • Surface plasmon resonance (SPR) sensors provide real-time molecular interaction data crucial for diagnostics and substance detection.
  • Ensuring the reliability of SPR sensor responses (sensorgrams) is critical to prevent misinterpretations due to sample handling, noise, or tampering.
  • Current SPR sensor data analysis may lack robustness for routine, auditable applications.

Purpose of the Study:

  • To investigate machine learning (ML) techniques for improving the quality and reliability of real-time SPR sensorgrams.
  • To develop a novel strategy for describing and analyzing SPR sensorgrams.
  • To create intelligent SPR sensors capable of safe, reliable, and auditable data analysis.

Main Methods:

  • Application of various machine learning (ML) algorithms to analyze SPR sensorgram data.
  • Development of a new method for characterizing SPR sensorgrams.
  • Integration of ML algorithms into an 'Intelligence Module' for sensorgram classification and substance identification.

Main Results:

  • The proposed ML approach successfully enhances and validates the quality of real-time SPR sensorgrams.
  • The developed Intelligence Module can classify sensorgrams, identify substances, and analyze specific data regions.
  • The system standardizes data and supports audit procedures, enabling automated testing capabilities for SPR biosensors.

Conclusions:

  • Machine learning significantly improves the reliability and interpretability of SPR sensor data.
  • The developed ML-based Intelligence Module enables the creation of intelligent SPR biosensors for automated and auditable analyses.
  • The approach was successfully validated using a protocol for Leishmaniasis diagnosis.