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(Reference retrieved automatically from Web of Science through information on FAPESP grant and its corresponding number as mentioned in the publication by the authors.)

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

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Author(s):
Rivera, Elmer Ccopa [1, 2] ; Swerdlow, Jonathan J. [3] ; Summerscales, Rodney L. [3] ; Uppala, Padma P. Tadi [4] ; Maciel Filho, Rubens [1] ; Neto, Mabio R. C. [2] ; Kwon, Hyun J. [2]
Total Authors: 7
Affiliation:
[1] Univ Estadual Campinas, Sch Chem Engn, BR-13083852 Campinas - Brazil
[2] Andrews Univ, Dept Engn, Berrien Springs, MI 49104 - USA
[3] Andrews Univ, Dept Comp, Berrien Springs, MI 49104 - USA
[4] Andrews Univ, Sch Populat Hlth Nutr & Wellness, Berrien Springs, MI 49104 - USA
Total Affiliations: 4
Document type: Journal article
Source: SENSORS; v. 20, n. 3 FEB 2020.
Web of Science Citations: 0
Abstract

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)2 + 3 luminophore and its experimentally measured ECL and electrochemical data. A smartphone-based ECL sensor with Ru(bpy)2 + 3 /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)2 + 3 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 mu M and 2.5 mu M. The AI approaches using RF and FNN were capable of directly inferring the concentration of Ru (bpy)2+ 3 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. (AU)

FAPESP's process: 17/23335-9 - Biorefinery development integrated to a bioethanol sugar cane plant with zero CO2 emission: routes to convert renewable resources to bio-products and bio-electricity
Grantee:Elmer Alberto Ccopa Rivera
Support type: Scholarships in Brazil - Technical Training Program - Technical Training
FAPESP's process: 15/20630-4 - Biorefinery development integrated to a bioethanol sugar cane plant with zero CO2 emission: routes to convert renewable resources to bio-products and bio-electricity
Grantee:Rubens Maciel Filho
Support type: Research Projects - Thematic Grants