Data-Driven Modeling of Smartphone-Based Electrochemiluminescence Sensor Data Using Artificial Intelligence
Data-Driven Modeling of Smartphone-Based Electrochemiluminescence Sensor Data Using Artificial Intelligence
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DOI：
10.3390/s20030625
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发表时间：
2020-02-01
期刊：
影响因子：
3.9
通讯作者：
Kwon, Hyun J.
中科院分区：
文献类型：
--
作者：
Rivera, Elmer Ccopa;Swerdlow, Jonathan J.;Kwon, Hyun J.
了解从基于智能手机的电化学发光(ECL)传感器提取的多模式数据之间的关系对于开发低成本的护理点诊断设备至关重要。本文利用随机森林(RF)和前馈神经网络(FNN)等人工智能算法，定量研究了Ru(Bpy)2+3发光团的浓度与其电化学发光和电化学数据之间的关系。利用一次性丝网印刷碳电极，研制了一种基于智能手机的Ru(Bpy)2+3/TPrA电化学发光传感器。在施加1.2V电压后，同时获得ECL图像和安培图。这些多峰数据通过RF和FNN算法进行分析，从而可以使用多个关键特征来预测Ru(Bpy)2+3的浓度。在0.02~2.5µM的检测范围内，Ru(Bpy)2+3的实测值与预测值具有较高的相关性(Rf和FNN分别为0.99和0.96)。使用Rf和FNN的人工智能方法能够利用易于观察的关键特征直接推断Ru(Bpy)2+3的浓度。结果表明，数据驱动的人工智能算法在分析多模式ECL传感器数据时是有效的。因此，这些人工智能算法可以作为建模武器库的重要组成部分，并成功应用于ECL传感器数据建模。
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.