Multivariate analysis, chemometrics, and machine learning in laser spectroscopy

Abstract: 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.

[1]  Anil K. Jain,et al.  Statistical Pattern Recognition: A Review , 2000, IEEE Trans. Pattern Anal. Mach. Intell..

[2]  S. J. Rehse,et al.  Towards the clinical application of laser-induced breakdown spectroscopy for rapid pathogen diagnosis: the effect of mixed cultures and sample dilution on bacterial identification , 2010 .

[3]  S. Wold,et al.  PLS-regression: a basic tool of chemometrics , 2001 .

[4]  S. J. Rehse,et al.  Pathogenic Escherichia coli strain discrimination using laser-induced breakdown spectroscopy , 2007 .

[5]  James A. Dodd,et al.  Identification of vapor-phase chemical warfare agent simulants and rocket fuels using laser-induced breakdown spectroscopy , 2010 .

[6]  Weidou Ni,et al.  Study to reduce laser-induced breakdown spectroscopy measurement uncertainty using plasma characteristic parameters , 2010 .

[7]  Anil K. Jain,et al.  Artificial Neural Networks: A Tutorial , 1996, Computer.

[8]  Michel Verleysen,et al.  Nonlinear Dimensionality Reduction , 2021, Computer Vision.

[9]  Jeremiah J Remus,et al.  Can the provenance of the conflict minerals columbite and tantalite be ascertained by laser-induced breakdown spectroscopy? , 2011, Analytical and bioanalytical chemistry.

[10]  Qing-Song Xu,et al.  Uncover the path from PCR to PLS via elastic component regression , 2010 .

[11]  Chase A. Munson,et al.  Laser-induced breakdown spectroscopy for detection of explosives residues: a review of recent advances, challenges, and future prospects , 2009, Analytical and bioanalytical chemistry.

[12]  Pedro Larrañaga,et al.  A review of feature selection techniques in bioinformatics , 2007, Bioinform..

[13]  Robert Tibshirani,et al.  The Elements of Statistical Learning: Data Mining, Inference, and Prediction, 2nd Edition , 2001, Springer Series in Statistics.

[14]  Leo Breiman,et al.  Bagging Predictors , 1996, Machine Learning.

[15]  David G. Stork,et al.  Pattern Classification , 1973 .

[16]  Nello Cristianini,et al.  An Introduction to Support Vector Machines and Other Kernel-based Learning Methods , 2000 .

[17]  Pedro M. Domingos,et al.  On the Optimality of the Simple Bayesian Classifier under Zero-One Loss , 1997, Machine Learning.

[18]  Eric O. Postma,et al.  Dimensionality Reduction: A Comparative Review , 2008 .

[19]  Leslie M. Collins,et al.  Signal processing for the detection of explosive residues on varying substrates using laser-induced breakdown spectroscopy , 2011, Defense + Commercial Sensing.

[20]  D. Hosmer,et al.  Applied Logistic Regression , 1991 .

[21]  Jennifer L. Gottfried,et al.  Discrimination of biological and chemical threat simulants in residue mixtures on multiple substrates , 2011, Analytical and bioanalytical chemistry.

[22]  R. Fisher.  THE USE OF MULTIPLE MEASUREMENTS IN TAXONOMIC PROBLEMS , 1936 .

[23]  Bruce R. Kowalski,et al.  Chemometrics: Theory and Application , 1977 .

[24]  Corinna Cortes,et al.  Support-Vector Networks , 1995, Machine Learning.

[25]  Vladimir N. Vapnik,et al.  The Nature of Statistical Learning Theory , 2000, Statistics for Engineering and Information Science.

[26]  Tom Fawcett,et al.  An introduction to ROC analysis , 2006, Pattern Recognit. Lett..

[27]  Yoshua. Bengio,et al.  Learning Deep Architectures for AI , 2007, Found. Trends Mach. Learn..

[28]  Nasser M. Nasrabadi,et al.  Pattern Recognition and Machine Learning , 2006, Technometrics.

[29]  S. Wold,et al.  The Collinearity Problem in Linear Regression. The Partial Least Squares (PLS) Approach to Generalized Inverses , 1984 .

[30]  Isabelle Guyon,et al.  An Introduction to Variable and Feature Selection , 2003, J. Mach. Learn. Res..

[31]  Michael E. Tipping.  Sparse Bayesian Learning and the Relevance Vector Machine , 2001, J. Mach. Learn. Res..

[32]  S. D. Jong.  SIMPLS: an alternative approach to partial least squares regression , 1993 .

[33]  Jeremiah Remus,et al.  Robust validation of pattern classification methods for laser-induced breakdown spectroscopy. , 2012, Applied optics.

[34]  Weidou Ni,et al.  A simplified spectrum standardization method for laser-induced breakdown spectroscopy measurements , 2011 .

[35]  Leslie M. Collins,et al.  Laser-induced breakdown spectroscopy-based geochemical fingerprinting for the rapid analysis and discrimination of minerals: The example of garnet , 2010 .

[36]  Ricardo Vilalta,et al.  A Perspective View and Survey of Meta-Learning , 2002, Artificial Intelligence Review.

[37]  R. Tibshirani,et al.  Model Search by Bootstrap “Bumping” , 1999 .

[38]  Leo Breiman,et al.  Random Forests , 2001, Machine Learning.