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Data-Driven Modeling of Smartphone-Based Electrochemiluminescence Sensor Data Using Artificial Intelligence.

Elmer Ccopa Rivera1,2, Jonathan J Swerdlow3, Rodney L Summerscales3

  • 1Department of Engineering, Andrews University, Berrien Springs, MI 49104, USA.

Sensors (Basel, Switzerland)
|January 26, 2020
PubMed
Summary
This summary is machine-generated.

Artificial intelligence (AI) algorithms analyze electrochemiluminescence (ECL) sensor data to predict luminophore concentration. This advances low-cost, smartphone-based diagnostic devices.

Keywords:
artificial intelligenceelectrochemiluminescencemobile phonemodelingsensor

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Area of Science:

  • Analytical Chemistry
  • Biosensors
  • Artificial Intelligence

Background:

  • Smartphone-based electrochemiluminescence (ECL) sensors offer potential for low-cost point-of-care diagnostics.
  • Understanding multimodal data relationships is key for sensor development.
  • Ru(bpy)32+ luminophore is a common analyte in ECL sensing.

Purpose of the Study:

  • To quantitatively investigate relationships between luminophore concentration and multimodal ECL/electrochemical data using AI.
  • To develop and validate AI models for predicting Ru(bpy)32+ concentration from sensor data.
  • To assess the effectiveness of AI in analyzing ECL sensor data for diagnostic applications.

Main Methods:

  • Development of a smartphone-based ECL sensor using screen-printed carbon electrodes.
  • Simultaneous acquisition of ECL images and amperograms.
  • Application of Random Forest (RF) and Feedforward Neural Network (FNN) AI algorithms for data analysis.
  • Quantitative investigation of multimodal data for predicting Ru(bpy)32+ concentration.

Main Results:

  • High correlations (0.99 for RF, 0.96 for FNN) between actual and predicted Ru(bpy)32+ concentrations were achieved.
  • Accurate prediction of Ru(bpy)32+ concentration in the range of 0.02 µM to 2.5 µM.
  • AI algorithms successfully inferred concentration using key sensor features.

Conclusions:

  • Data-driven AI algorithms, specifically RF and FNN, are effective for analyzing multimodal ECL sensor data.
  • AI models can directly infer analyte concentration from sensor outputs.
  • These AI approaches are valuable for developing advanced ECL-based diagnostic devices.