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Series / Report no.
Open Access
Type
Report
Language
en
Date
1995-05-31
Research Projects
Organizational Units
Journal Issue
Title
Artificial neural networks as a tool for identity
confirmation of infrared spectra
Translated Title
Bruikbaarheid van kunstmatige neurale netwerken
voor de identititeitsbevestiging van
infraroodspectra
Published in
Abstract
Onderzoek is uitgevoerd naar de bruikbaarheid van
kunstmatige neurale netwerken als criterium voor de identiteitsbevestiging
van infrarood-(IR) spectra. Doel van het onderzoek is de identificatie van
spectra met hoge ruisniveaus, verkregen met behulp van gecombineeerde
gaschromatografie (GC)IR spectrometrie. Neurale netwerken zijn getraind op
GC/IR spectra van Clenbuterol, Fluoranthene en Perylene. De resultaten zijn
vergeleken met classificatie door middel van "peak matching-" en
bibliotheekzoekprocedures. Peak matching bleek de meest betrouwbare methode
voor de identificatie van sterk gelijkende spectra. Voorwaarde is echter
een laag ruisniveau en een hoge spectrale resolutie. Dit geldt eveneens
voor bibliotheekzoekmethoden. Neurale netwerken bleken minder gevoelig voor
ruis en daarom meer geschikt voor bevestiging van de identiteit van
IR-spectra van sporenhoeveelheden.
The utility of artificial neural networks (ANN) as a tool for confirmation of the identity of infrared (IR) spectra has been investigated. The main goal of the study is the identification of spectra with relatively high noise levels, obtained from gas chromatography combined with IR spectrometric detection. Networks were trained for GC/IR spectra of Clenbuterol, Fluoranthene and Perylene as representatives of compounds for which identification in real world samples is demanded occasionally. Results have been compared with classification by peak matching and library search methods. Peak matching appears to be the most discriminative method to distinguish between closely resembling spectra, but only in case of high signal-to-noise ratio and resolution. Similar conclusions are drawn for library search identification. ANN-models are less sensitive to spectral noise and hence most suited to be used for confirmation and identification of spectra obtained intrace analysis.
The utility of artificial neural networks (ANN) as a tool for confirmation of the identity of infrared (IR) spectra has been investigated. The main goal of the study is the identification of spectra with relatively high noise levels, obtained from gas chromatography combined with IR spectrometric detection. Networks were trained for GC/IR spectra of Clenbuterol, Fluoranthene and Perylene as representatives of compounds for which identification in real world samples is demanded occasionally. Results have been compared with classification by peak matching and library search methods. Peak matching appears to be the most discriminative method to distinguish between closely resembling spectra, but only in case of high signal-to-noise ratio and resolution. Similar conclusions are drawn for library search identification. ANN-models are less sensitive to spectral noise and hence most suited to be used for confirmation and identification of spectra obtained intrace analysis.
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RIVM