Derivation of substructures from infrared band shapes by fuzzy logic and partial cross correlation functions

Ehrentreich, F.

doi:10.1007/s002160050535

Cited by 10 publications

(7 citation statements)

References 4 publications

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“…Recently, a simplified MCS concept has been applied to results obtained by a new type of library search for IR spectra. 24 The aim of this work was to investigate whether a MCS approach can be successfully applied to hitlists obtained from library searches with IR spectra. The final purpose was to generate structural restrictions for a systematic structure elucidation applicable to compounds that are not contained in the library.…”

mentioning

confidence: 99%

Maximum Common Substructures of Organic Compounds Exhibiting Similar Infrared Spectra

Вармуза

Penchev

Scsibrany

1998

J. Chem. Inf. Comput. Sci.

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Information about the unknown chemical structure of an organic compound can be obtained by comparing the infrared spectrum with the spectra of a spectral library. The resulting hitlist contains compounds exhibiting the most similar spectra. A method based on the maximum common substructure concept has been developed for an automatic extraction of common structural features from the hitlist structures. A set of substructures is derived that are characteristic for the query structure. Results can be used as structural restrictions in isomer generation.

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mentioning

confidence: 99%

Maximum Common Substructures of Organic Compounds Exhibiting Similar Infrared Spectra

Вармуза

Penchev

Scsibrany

1998

J. Chem. Inf. Comput. Sci.

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“…Following the same philosophy, this result is better expressed by textual rather than numerical phrases. So the results from the intersections are defuzzified 22,23 using three categories, ''present'', ''possibly present'', or ''not present''. The first two categories indicate that the pigment is present and the third one indicates that it is clearly not present.…”

Section: Algorithm Pigment Identificationmentioning

confidence: 99%

Fuzzy Logic for Identifying Pigments Studied by Raman Spectroscopy

et al. 2004

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Fuzzy logic and linguistic variables are used for the automatic interpretation of Raman spectra obtained from pigments found in cultural heritage art objects. Featured bands are extracted from a Raman spectrum of a reference pigment and the methodology for constructing the library is illustrated. An unknown spectrum is then interpreted automatically and a process for identifying the corresponding pigment is described. A reference library consisting of 32 pigments was built and the effectiveness of the algorithm was tested by the Raman spectroscopic analysis of 10 pigments that are known to have been extensively used in Byzantine hagiography. Binary mixtures of these pigments were also tested. The algorithm's level of identification was good even though extra peaks, noise, and background signals were encountered in the spectra.

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“…13 The algorithm has been tested by the present authors with the use of atmospheric infrared spectra and library data recorded at 0.2 cm Ϫ1 spectral resolution. For digitized, equidistantly sampled spectra, a cross-correlation function (ccf) of two spectral vectors a and b with N data points can be defined by…”

Section: Chemometrics For Component Identificationmentioning

confidence: 99%

“…Both absorbance spectra within the interval considered are intensity normalized to produce a function maximum of Յ1. 13 Slight deviations between the wave-number scales of the sample and reference spectrum produce a shift of the cross-correlation-function maximum.…”

Section: Chemometrics For Component Identificationmentioning

confidence: 99%

Reliable component identification in atmospheric open‐path FTIR spectroscopy by a cross‐correlation method

Müller

Heise²

2001

Field Analytical Chem & Tech

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Continuous environmental air monitoring is possible with open-path FTIR spectroscopy. Efficient chemometric tools are needed for the identification of unknown compounds that are observable in remote sensing of volatile emissions into the atmosphere. A qualitative analysis using spectra recorded at a resolution of 0.2 cm Ϫ1 is proposed, based on the cross-correlation function and calculated with data from component-specific intervals from library absorbance spectra. The characteristic wave-number intervals were selected on the grounds of the most intense component absorption bands for which negligible overlap was present with atmospheric absorption lines. The algorithm has been implemented for 36 different gaseous substances into an expert system for the evaluation of atmospheric FTIR spectra, recorded for routine work in active open-path monitoring. Detection limits for substance identification from mixture spectra are discussed. The influence from overlapping spectral features of co-existing atmospheric compounds can be reduced by preprocessing, including such techniques as scaled absorbance subtraction with appropriate spectra of the known gases. A discussion of identification strategies previously presented in the literature is included.

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Derivation of substructures from infrared band shapes by fuzzy logic and partial cross correlation functions

Cited by 10 publications

References 4 publications

Maximum Common Substructures of Organic Compounds Exhibiting Similar Infrared Spectra

Maximum Common Substructures of Organic Compounds Exhibiting Similar Infrared Spectra

Fuzzy Logic for Identifying Pigments Studied by Raman Spectroscopy

Reliable component identification in atmospheric open‐path FTIR spectroscopy by a cross‐correlation method

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