DataDriven Modeling of SmartphoneBased Electrochemiluminescence Sensor Data Using Artificial Intelligence Understanding relationships among multimodal data extracted from a smartphonebased electrochemiluminescence (ECL) sensor is crucial for the development of lowcost pointofcare 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)32 luminophore and its experimentally measured ECL and electrochemical data. A smartphonebased ECL sensor with Ru(bpy)32TPrA was developed using disposable screenprinted carbon electrodes. ECL images and amperograms were simultaneously obtained following 1.2V voltage application. These multimodal data were analyzed by RF and FNN algorithms, which allowed the prediction of Ru(bpy)32 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 xc2xb5M and 2.5 xc2xb5M. The AI approaches using RF and FNN were capable of directly inferring the concentration of Ru(bpy)32 using easily observable key features. The results demonstrate that datadriven 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.

Electronic Tongue Recognition with Feature Specificity Enhancement As a kind of intelligent instrument, an electronic tongue (Etongue) realizes liquid analysis with an electrodesensor array and certain machine learning methods. The large amplitude pulse voltammetry (LAPV) is a regular Etongue type that prefers to collect a large amount of response data at a high sampling frequency within a short time. Therefore, a fast and effective feature extraction method is necessary for machine learning methods. Considering the fact that massive commonmode components (high correlated signals) in the sensorarray responses would depress the recognition performance of the machine learning models, we have proposed an alternative feature extraction method named feature specificity enhancement (FSE) for feature specificity enhancement and feature dimension reduction. The proposed FSE method highlights the specificity signals by eliminating the common mode signals on paired sensor responses. Meanwhile, the radial basis function is utilized to project the original features into a nonlinear space. Furthermore, we selected the kernel extreme learning machine (KELM) as the recognition part owing to its fast speed and excellent flexibility. Two datasets from LAPV Etongues have been adopted for the evaluation of the machinelearning models. One is collected by a designed Etongue for beverage identification and the other one is a public benchmark. For performance comparison, we introduced several machinelearning models consisting of different combinations of feature extraction and recognition methods. The experimental results show that the proposed FSE coupled with KELM demonstrates obvious superiority to other models in accuracy, time consumption and memory cost. Additionally, low parameter sensitivity of the proposed model has been demonstrated as well.