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Explanatory analysis of Spectroscopic data using machine learning of simple, interpretable rules. Vib Spectrosc 2003
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@MISC{Goodacre_explanatoryanalysis,
author = {Royston Goodacre},
title = {Explanatory analysis of Spectroscopic data using machine learning of simple, interpretable rules. Vib Spectrosc 2003},
year = {}
}
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Abstract
Whole organism or tissue profiling by vibrational spectroscopy produces vast amounts of seemingly unintelligible data. However, the characterisation of the biological system under scrutiny is generally possible only in combination with modern supervised machine learning techniques, such as artificial neural networks (ANNs). Nevertheless, the interpretation of the calibration models from ANNs is often very difficult, and the information in terms of which vibrational modes in the infrared or Raman spectra are important is not readily available. ANNs are often perceived as ‘black box ’ approaches to modelling spectra, and to allow the deconvolution of complex hyperspectral data it is necessary to develop a system that itself produces ‘rules ’ that are readily comprehensible. Evolutionary computation, and in particular genetic programming (GP), is an ideal method to achieve this. An example of how GP can be used for Fourier transform infrared (FT-IR) image analysis is presented, and is compared with images produced by principal components analysis (PCA), discriminant function analysis (DFA) and partial least squares (PLS) regression.
Keyphrases
spectroscopic data interpretable rule vib spectrosc machine learning explanatory analysis whole organism vibrational mode vibrational spectroscopy particular genetic programming principal component analysis raman spectrum partial least square black box approach vast amount evolutionary computation unintelligible data biological system tissue profiling artificial neural network complex hyperspectral data fourier transform image analysis calibration model discriminant function analysis ideal method
