This paper presents a software package that allows chemists to analyze spectroscopy data using innovative machine learning (ML) techniques. The package, designed for use in conjunction with lab-based spectroscopic instruments, includes features to encourage its adoption by analytical chemists, such as having an intuitive graphical user interface with a step-by-step ‘wizard’ for building new ML models, supporting standard file types and data preprocessing, and incorporating well-known standard chemometric analysis techniques as well as new ML techniques for analysis of spectra, so that users can compare their performance. The ML techniques that were developed for this application have been designed based on considerations of the defining characteristics of this problem domain, and combine high accuracy with visualization, so that users are provided with some insight into the basis for classification decisions.


Support Vector Machine Artificial Neural Network Partial Little Square Model Library Kernel Support Vector Machine 
These keywords were added by machine and not by the authors. This process is experimental and the keywords may be updated as the learning algorithm improves.


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Copyright information

© Springer-Verlag London Limited 2009

Authors and Affiliations

  • Michael G. Madden
    • 1
  • Tom Howley
    • 2
  1. 1.College of Engineering & InformaticsNational University of IrelandGalwayIreland
  2. 2.Analyze IQ LimitedAthenry, GalwayIreland

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