Absorption Spectra Analysis using Modified Self-Organizing Feature Maps

  • Thomas Bryant Department of Computer Science and Engineering, Oakland University, United States
  • Jessica Koppen Department of Chemistry, Oakland University, United States;
  • Mohamed Zohdy Department of Electrical and Computer Engineering, Oakland University, United States
Keywords: Self-Organizing Feature Map, Unsupervised Learning, Energies, Intensities, Chemical Absorption

Abstract

This research demonstrates an application of a modified self-organizing feature map (SOFM) algorithm to analyze and discover the quality of chemical absorption spectrum data. By forming an NxN neural array from input features and varying the essential parameters of the algorithm, map recognition quality is increased at the expense of more computation. The features of this SOFM are based on absorption intensity variations with excitation wavelength. SOFMs are used to discern pattern similarities and differences between spectral data. A context tree allows individual features, or key numbers in the data, to be input and classifies the vector to the type of data that is most similar. This research also use the self-organizing map to enhance as well as visualize resultant classification efficiency through the use of watershed transformation.

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Published
2016-01-03
How to Cite
Bryant, T., Koppen, J., & Zohdy, M. (2016). Absorption Spectra Analysis using Modified Self-Organizing Feature Maps. Transactions on Machine Learning and Artificial Intelligence, 3(6), 1. https://doi.org/10.14738/tmlai.36.1155