Laser Spectroscopy for Sensing: Fundamentals, Techniques and Applications
Multivariate analysis, chemometrics, and machine learning in laser spectroscopy
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Torrione, P; Collins, LM; Morton, KD
January 1, 2014
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.
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Torrione, P., Collins, L. M., & Morton, K. D. (2014). Multivariate analysis, chemometrics, and machine learning in laser spectroscopy. In Laser Spectroscopy for Sensing: Fundamentals, Techniques and Applications (pp. 125–164). https://doi.org/10.1533/9780857098733.1.125
Torrione, P., L. M. Collins, and K. D. Morton. “Multivariate analysis, chemometrics, and machine learning in laser spectroscopy.” In Laser Spectroscopy for Sensing: Fundamentals, Techniques and Applications, 125–64, 2014. https://doi.org/10.1533/9780857098733.1.125.
Torrione P, Collins LM, Morton KD. Multivariate analysis, chemometrics, and machine learning in laser spectroscopy. In: Laser Spectroscopy for Sensing: Fundamentals, Techniques and Applications. 2014. p. 125–64.
Torrione, P., et al. “Multivariate analysis, chemometrics, and machine learning in laser spectroscopy.” Laser Spectroscopy for Sensing: Fundamentals, Techniques and Applications, 2014, pp. 125–64. Scopus, doi:10.1533/9780857098733.1.125.
Torrione P, Collins LM, Morton KD. Multivariate analysis, chemometrics, and machine learning in laser spectroscopy. Laser Spectroscopy for Sensing: Fundamentals, Techniques and Applications. 2014. p. 125–164.