A Spectrum Matching Algorithm Based on Dynamic Spectral Distance

DOI

10.2991/icmii-15.2015.139How to use a DOI?

Keywords

Spectrum Matching, Hamming Distance, Difference of Spectrum, Similarity Measure

Abstract

Spectral matching algorithms are widely used in various models for spectral analysis and identification. Traditional spectral matching algorithms usually adopt the similarity of their absorbance value as the index to evaluate the similarity between two spectra. The absorbance value is related to concentration of substance and state at the sampling time, hence those algorithms have higher requirements on samples. This thesis proposes an algorithm based on dynamic spectral distance (DSD), considering both the waveform similarity and the absolute difference, taking the segmented hamming distance of derivative spectrum as the waveform difference measure between two spectral curves and the standard deviation of Difference of Spectrum (DS) as the absolute difference measure of spectrum. As the experiment shows, the algorithm can effectively identify not only spectral with similar waveform but also those with different waveform, and thus has higher accuracy and stability than traditional algorithms. The algorithm can be used in classification and matching system for UV-visible spectrum, infrared spectrum, hyper-spectrum, etc.

Copyright

© 2015, the Authors. Published by Atlantis Press.

Open Access

This is an open access article distributed under the CC BY-NC license (http://creativecommons.org/licenses/by-nc/4.0/).

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TY  - CONF
AU  - Qi Jia
AU  - Shouhong Cao
PY  - 2015/10
DA  - 2015/10
TI  - A Spectrum Matching Algorithm Based on Dynamic Spectral Distance
BT  - Proceedings of the 3rd International Conference on Mechatronics and Industrial Informatics
PB  - Atlantis Press
SP  - 794
EP  - 799
SN  - 2352-538X
UR  - https://doi.org/10.2991/icmii-15.2015.139
DO  - 10.2991/icmii-15.2015.139
ID  - Jia2015/10
ER  -