Abstract
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.
Highlights
There is demand for automated, accurate, and fast identification of chemical threats and hazardous materials such as heavy metals, explosives, and herbicides/pesticides in the field
Using the prototype instrument described by Erickson et al [8], two libraries of explosives and seawater samples were compiled from CSW voltammograms from which four datasets were created for training and testing of the classifiers: 4-class chemicals in seawater (4-SW), 11-class chemicals in seawater (11-SW), 3-class explosives (3-EXP), and 11-class explosives (11-EXP)
In addition to a principal component analysis (PCA)-SVM benchmark for classification of cyclic square wave voltammetry (CSWV) samples, we examined some common algorithms used for classification of time series including linear discriminant analysis (LDA) which has been utilized in both cheminformatics [23] and time series classification more generally, with 1 Nearest Neighbor and Dynamic Time Warping (1NN-DTW), a benchmark algorithm for time series classification [9,22], as well as numerous deep learning techniques
Summary
There is demand for automated, accurate, and fast identification of chemical threats and hazardous materials such as heavy metals, explosives, and herbicides/pesticides in the field. For this purpose, electrochemical detection provides a number of advantages including low-detection limits, amenability to miniaturization, and use of small analyte volumes [1]. One example is square-wave voltammetry (SWV), which is a powerful electrochemical technique practical for analytical and detection applications. SWV (CSWV), the very promising results of which have demonstrated improved peak resolution and increased sensitivity over traditional SWV [2]. The square-wave cathodic, anodic, and adsorptive stripping voltammetry are seen as modes that facilitate increasing pre-analysis concentration enrichment
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