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    Artificial neural networks as a tool for identity confirmation of infrared spectra

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    Authors
    Visser T
    Luinge HJ
    Type
    Report
    Language
    en
    
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    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
    Publiekssamenvatting
    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.
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    RIVM
    URI
    http://hdl.handle.net/10029/261354
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