Ms. Mireia Castanys Profile

Mireia Castanys

at Univ Politècnica de Catalunya

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Proceedings Article | 12 August 2005 Paper

A fuzzy logic system for Raman spectrum identification

M. Castanys, M. Soneira, R. Perez-Pueyo, S. Ruiz-Moreno

Proceedings Volume 5857, 585714 (2005) https://doi.org/10.1117/12.612608

KEYWORDS: Raman spectroscopy, Fuzzy logic, Fuzzy systems, System identification, Telecommunications, Light scattering, Raman scattering, Chromium, Statistical analysis, Charge-coupled devices

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Raman Spectroscopy is a fast, rugged analytical technique based on the Raman Effect. When monochromatic light encounters matter, most of the scattered light has the same wavelength as the incident light. However, a small fraction of the scattered light is shifted in a different wavelength by the molecular vibrations and rotations in the sample. The representation of this shifted light is called Raman spectrum, and contains many sharp bands characteristics of the sample, allowing its identification without ambiguity. In this communication, a fuzzy logic system to recognize Raman spectra of artistic pigments is presented. The identification is based on the comparison between an unknown spectrum, and pattern spectra. Frequently the comparison is made by the spectrospist by visual inspection, but this is slow and imprecise. In order to mitigate this problematic, a system based on the fuzzy logic technique to identify Raman spectra is presented. The methodology consists on implementing the comparison with the Correlation. However, a Raman spectrum is inevitably affected by noise which introduces ambiguity into the correlation values. Fuzzy Logic provides a simple way to draw conclusions from imprecise data. The fuzzy identification system is based on the following statement: when the correlation between the unidentified and the pattern is enough high, the analysed pigment is recognized as the pigment which corresponds to this pattern. The membership functions, which characterize the fuzzy sets at the input (Correlation) and output (Identified/ Not_Identified) of the system, and the inference mechanism suitable for the problem, are chosen.

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