Next Article in Journal
Acoustic Inspection of Concrete Structures Using Active Weak Supervision and Visual Information
Previous Article in Journal
Set-Membership Based Hybrid Kalman Filter for Nonlinear State Estimation under Systematic Uncertainty
Open AccessArticle

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

1
Department of Engineering, Andrews University, Berrien Springs, MI 49104, USA
2
School of Chemical Engineering, University of Campinas, Campinas 13083-852, Brazil
3
Department of Computing, Andrews University, Berrien Springs, MI 49104, USA
4
School of Population Health, Nutrition & Wellness, Andrews University, Berrien Springs, MI 49104, USA
*
Author to whom correspondence should be addressed.
Sensors 2020, 20(3), 625; https://doi.org/10.3390/s20030625
Received: 19 December 2019 / Revised: 15 January 2020 / Accepted: 20 January 2020 / Published: 23 January 2020
(This article belongs to the Section Chemical Sensors)
Understanding relationships among multimodal data extracted from a smartphone-based electrochemiluminescence (ECL) sensor is crucial for the development of low-cost point-of-care diagnostic devices. In this work, artificial intelligence (AI) algorithms such as random forest (RF) and feedforward neural network (FNN) are used to quantitatively investigate the relationships between the concentration of   Ru ( bpy ) 3 2 + luminophore and its experimentally measured ECL and electrochemical data. A smartphone-based ECL sensor with   Ru ( bpy ) 3 2 + /TPrA was developed using disposable screen-printed carbon electrodes. ECL images and amperograms were simultaneously obtained following 1.2-V voltage application. These multimodal data were analyzed by RF and FNN algorithms, which allowed the prediction of   Ru ( bpy ) 3 2 + concentration using multiple key features. High correlation (0.99 and 0.96 for RF and FNN, respectively) between actual and predicted values was achieved in the detection range between 0.02 µM and 2.5 µM. The AI approaches using RF and FNN were capable of directly inferring the concentration of   Ru ( bpy ) 3 2 + using easily observable key features. The results demonstrate that data-driven AI algorithms are effective in analyzing the multimodal ECL sensor data. Therefore, these AI algorithms can be an essential part of the modeling arsenal with successful application in ECL sensor data modeling. View Full-Text
Keywords: electrochemiluminescence; artificial intelligence; sensor; mobile phone; modeling electrochemiluminescence; artificial intelligence; sensor; mobile phone; modeling
Show Figures

Figure 1

MDPI and ACS Style

Ccopa Rivera, E.; Swerdlow, J.J.; Summerscales, R.L.; Uppala, P.P.T.; Maciel Filho, R.; Neto, M.R.C.; Kwon, H.J. Data-Driven Modeling of Smartphone-Based Electrochemiluminescence Sensor Data Using Artificial Intelligence. Sensors 2020, 20, 625.

Show more citation formats Show less citations formats
Note that from the first issue of 2016, MDPI journals use article numbers instead of page numbers. See further details here.

Article Access Map by Country/Region

1
Back to TopTop