Machine Learning Techniques for Chemical Identification Using Cyclic Square Wave Voltammetry.

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. Wh

SUBMITTER: Dean SN 

PROVIDER: S-EPMC6567068 | biostudies-literature | 2019 May

REPOSITORIES: biostudies-literature