A critical review of recent trends in sample classification using Laser-Induced Breakdown Spectroscopy (LIBS)
Lukas Brunnbauer, Zuzana Gajarska, Hans Lohninger, Andreas Limbeck
Abstract
LIBS-based classification has experienced an ever-increasing interest in the last few years. LIBS is a well-suited technique for classification tasks based on elemental fingerprinting, providing fast simultaneous multi-element analysis with stand-off, online, and portable capabilities. The topic of classification gained even more momentum due to the current hype on machine learning, big data, and chemometrics. Nevertheless, with many LIBS users not being data scientists by training, classification algorithms are often used and considered “black boxes,” hindering the adequate application of these tools. This review provides a comprehensive introduction and overview of the steps necessary (e.g., normalization, background correction, feature selection) to go from recorded data to a well-performing classifier. Additionally, the basic principles, advantages, and limitations of the most used machine learning algorithms reported in LIBS-classification literature are discussed. Finally, the review offers an overview of the literature published in the field, highlighting the great diversity of applications.