Using Generated LIBS Data as a Base for Neural Network Architecture Development

Laser-Induced Breakdown Spectroscopy (LIBS) is a rapid atomic spectroscopy technique used to measure the concentration of elements in samples. This research paper uses supervised deep learning with a LIBS database provided by National Institute of Standards and Technology (NIST) to find out what kind of neural network architecture can best predict element concentrations from metal alloy samples. Accuracy of predictions was used to evaluate the network architectures. Because collecting and storing real samples of metal alloys with a wide range of element concentrations requires a lot of resources, the network’s ability to predict outside of training set’s range of concentrations was also tested. The goal of the research was to find out if NIST database together with machine learning can be used to reduce the amount of real alloy samples needed for LIBS calibration.