Next Article in Journal
Epitaxial Graphene Sensors Combined with 3D-Printed Microfluidic Chip for Heavy Metals Detection
Previous Article in Journal
SmartFire: Intelligent Platform for Monitoring Fire Extinguishers and Their Building Environment
Previous Article in Special Issue
Sparse ECG Denoising with Generalized Minimax Concave Penalty
Open AccessArticle

Machine Learning Techniques for Chemical Identification Using Cyclic Square Wave Voltammetry

1
National Research Council Postdoctoral Fellow, Washington, DC 20375, USA
2
U.S. Naval Research Laboratory, Center for Bio/Molecular Science & Engineering (Code 6900), 4555 Overlook Avenue SW, Washington, DC 20375, USA
*
Author to whom correspondence should be addressed.
Sensors 2019, 19(10), 2392; https://doi.org/10.3390/s19102392
Received: 25 April 2019 / Revised: 16 May 2019 / Accepted: 22 May 2019 / Published: 25 May 2019
(This article belongs to the Special Issue Machine Learning for Sensing and Healthcare)
Electroanalytical techniques are useful for detection and identification because the instrumentation is simple and can support a wide variety of assays. One example is cyclic square wave voltammetry (CSWV), a practical detection technique for different classes of compounds including explosives, herbicides/pesticides, industrial compounds, and heavy metals. A key barrier to the widespread application of CSWV for chemical identification is the necessity of a high performance, generalizable classification algorithm. Here, machine and deep learning models were developed for classifying samples based on voltammograms alone. The highest performing models were Long Short-Term Memory (LSTM) and Fully Convolutional Networks (FCNs), depending on the dataset against which performance was assessed. When compared to other algorithms, previously used for classification of CSWV and other similar data, our LSTM and FCN-based neural networks achieve higher sensitivity and specificity with the area under the curve values from receiver operating characteristic (ROC) analyses greater than 0.99 for several datasets. Class activation maps were paired with CSWV scans to assist in understanding the decision-making process of the networks, and their ability to utilize this information was examined. The best-performing models were then successfully applied to new or holdout experimental data. An automated method for processing CSWV data, training machine learning models, and evaluating their prediction performance is described, and the tools generated provide support for the identification of compounds using CSWV from samples in the field. View Full-Text
Keywords: electrochemical detection; cyclic square wave voltammetry; machine learning techniques electrochemical detection; cyclic square wave voltammetry; machine learning techniques
Show Figures

Graphical abstract

MDPI and ACS Style

Dean, S.N.; Shriver-Lake, L.C.; Stenger, D.A.; Erickson, J.S.; Golden, J.P.; Trammell, S.A. Machine Learning Techniques for Chemical Identification Using Cyclic Square Wave Voltammetry. Sensors 2019, 19, 2392.

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