Spectroscopic identification and quantitative analysis of binary mixtures using artificial neural networks
M L Ganadu1, G Lubinu, A Tilocca
1Dipartimento di Chimica, Università di Sassari, Via Vienna 2, I-07100 Sassari, Italy.
Talanta
|October 31, 2008
Summary
Artificial neural networks effectively identify distorted UV-visible spectra and quantify components in mixtures of similar organic indicators. These advanced networks demonstrate powerful capabilities in complex spectroscopic analysis.
Area of Science:
- Analytical Chemistry
- Computational Chemistry
- Spectroscopy
Background:
- UV-visible spectroscopy is crucial for analyzing organic compounds.
- Distorted spectra and complex mixtures pose challenges in accurate analysis.
- Artificial neural networks offer advanced computational solutions for spectroscopic data.
Purpose of the Study:
- To apply artificial neural networks to identify distorted UV-visible spectra.
- To utilize artificial neural networks for quantitative determination of components in binary mixtures.
- To evaluate the efficacy of trained neural networks on challenging spectroscopic data.
Main Methods:
- Development and training of artificial neural networks.
- Application of networks to UV-visible spectral data of six organic indicators.
- Testing network performance in spectral identification and quantitative analysis.
Main Results:
- Trained artificial neural networks accurately identified distorted UV-visible spectra.
- Networks successfully performed quantitative determination of single components in binary mixtures.
- High performance was achieved even with spectrally similar organic indicators.
Conclusions:
- Artificial neural networks are powerful tools for analyzing complex UV-visible spectra.
- The developed networks provide robust solutions for both qualitative and quantitative spectroscopic challenges.
- This approach enhances the analysis of organic compounds, particularly those with similar spectral characteristics.
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