Multivariate analysis, chemometrics, and machine learning in laser spectroscopy

Published

Book Section

Spectroscopic techniques are only as powerful as the information that can be extracted from the resulting spectral data. Machine learning is the study of techniques for the automated extraction of information from raw data. Proper application of machine learning to spectral data allows users to make decisions as data are collected, without human-in-the-loop processing. This chapter provides an overview of the application of machine-learning techniques to spectroscopic data. Topics such as data pre-processing, feature selection, classifier development, and cross-validation are discussed in light of the high dimensional data typical of laser spectroscopy. © 2014 Woodhead Publishing Limited All rights reserved.

Full Text

Duke Authors

Cited Authors

  • Torrione, P; Collins, LM; Morton, KD

Published Date

  • January 1, 2014

Book Title

  • Laser Spectroscopy for Sensing: Fundamentals, Techniques and Applications

Start / End Page

  • 125 - 164

International Standard Book Number 13 (ISBN-13)

  • 9780857092731

Digital Object Identifier (DOI)

  • 10.1533/9780857098733.1.125

Citation Source

  • Scopus