Advertisement

Chemometrik pp 337-364 | Cite as

Spektrenauswertung

Chapter
  • 277 Downloads

Zusammenfassung

Spektren entstehen, wenn man die Wechselwirkung einer Probe mit Photonen oder Mikroteilchen (zum Beispiel Elektronen) in Abhängigkeit von deren Energie studiert. Spektren sind als Signale in komplexer Weise vom Eingangssignal (zum Beispiel der Lichtstrahlung), den physikalischen und chemischen Eigenschaften der Probe, vom Messgerät und von der Messprozedur abhängig. Sie werden durch Störungen und Rauschen beeinträchtigt und sind im vollen Umfang Gegenstand der elementaren Signalverarbeitung (Kap. 6). Spektren zeichnen sich in diesem Kontext durch die Besonderheit aus, dass sie (in der Regel) aus einer großen Zahl geordneter Messpunkten bestehen, die einen Datenvektor bilden.

Preview

Unable to display preview. Download preview PDF.

Unable to display preview. Download preview PDF.

Literatur

  1. 1.
    Zupan J (1986) Computer-Supported Spectroscopic Databases, Ellis Horwood, Chichester. Warr WA (1993) Computer-Assisted Structure Elucidation, Library search and spectral data collections. Anal Chem 65: 1045AGoogle Scholar
  2. 2.
    Neudert R (1998) Spectroscopic Databases. In: von Ragué Schleyer P (ed) Encyclopedia of Computational Chemistry, Wiley, Chichester, p 2632, specinfo.wiley.com oder www.fiz-karlsruhe.de.Google Scholar
  3. 3.
    IR Searchmaster, Bio-Rad Laboratories, SadtlerDivision, www.bio-rad.comGoogle Scholar
  4. 4.
    Chemometrics-Reviews in Anal Chem (1988) 60: 252R–273R, (1990) 62: 84R–101R, (1992) 64: 22R–49R, (1994) 66: 315R–359R, (1996) 68: 21R–61R, (1998) 70: 209R–228R, (2000) 72: 91R–97RGoogle Scholar
  5. 5.
    Wang CP, Isenhour TL (1987) Infrared Library Search on Principal-Component-Analyzed Fourier-Transformed Absorption Spectra. Appl Spectrosc 41:185. Sherman JW, de Haseth JA, Cameron DL (1989) A Window Fourier-Domain Infrared Search System. Appl Spectrosc 43: 1311Google Scholar
  6. 6.
    Zupan J, Munk ME (1986) Feedback Search of Hierarchical Trees. Anal Chem 58: 3219. Bjerga JM, Small GW (1990) Automated Selection of Library Subsets for Infrared Spectral Searching. Anal Chem 62: 226Google Scholar
  7. 7.
    Jung-Pin Yu, Friedrich HB (1987) Odd Moments of the Cross-Correlation Foundation for Library Searching of Infrared Spectra. Appl Spectrosc 41: 869. Domokos L, Henneberg D, Weidmann B (1983) Optimization of Search Algorithms for a Mass Spectra Library. Analyt Chim A 150: 37. Domokos L, Henneberg D, (1984) A Correlation Method in Library Search. Analyt Chimica A 165: 75. Ebel S, Mück W (1987) Algorithmen zum automatischen Vergleich von Spektren in der HPLC/UV-Kopplung. Fresenius Z Anal Chem 327: 794, (1988) 331: 351, 359. Brown CW, Donahue SM (1988) Searching a UV-Visible Spectral Library. Appl Spectrosc 42: 347Google Scholar
  8. 8.
    Zürcher M, Clerc TJ (1988) General Theory of Similarity Measures for Library Search Systems. Analyt Chim A 206:161CrossRefGoogle Scholar
  9. 9.
    Jurs PC, Sutton GP, Ranc ML (1989) Carbon-13 NMR Spectral Simulation. Anal Chem 61: 1115A. Warr WA (1993) Computer-Assisted Structure Elucidation, 2. Indirect database approaches and established Systems. Anal Chem 65: 1087A. Bremser W (1988) Strukturaufklärung und künstliche Intelligenz. Angew Chem 100 : 252. Hippe Z (1991) Artificial Intelligence in Chemistry: Structure Elucidation and Simulation of Chemical Reactions. Elsevier, Amsterdam. Pretsch E, Clerc JT (1997) Spectra Interpretation of Organic Compounds. Wiley VCH, WeinheimGoogle Scholar
  10. 10.
    Günzler H, Heise H (1998) IR-Spektroskopie, Eine Einführung. Wiley VCH WeinheimGoogle Scholar
  11. 11.
    McLafferty FW, Tureček F (1993) Interpretation von Massenspektren. Spektrum Akademischer Verlag, HeidelbergGoogle Scholar
  12. 12.
    Bremser W, Ernst L, Fachinger W, Gerhards R, Hardt A, Levis PME (1987) Carbon-13 NMR Spectral Data, 4. Aufl, VCH WeinheimGoogle Scholar
  13. 13.
    Elyashberg ME, Martirosian ER, Karasev YuZ, Thiele H, Somberg H(1997) X-PERT: a user-friendly expert system for molecular structure elucidation by spectral methods. Anal Chim Acta 337: 265CrossRefGoogle Scholar
  14. 14.
    Jurs PC, Kowalski BR, Isenhour TL (1969), Computerized Learning Machines Applied to Chemical Problems. Anal Chem 41: 21,690,695,1945,1949CrossRefGoogle Scholar
  15. 15.
    Hasenoehrl EJ, Griffith PR (1993) Classification of Condensed-Phase Infrared Spectra by Substructures Using Principal Component Analysis. Appl Spectrosc 47: 643. Brown SD (1995) Chemical Systems under indirect Observation: Latent Properties and Chemometrics. Appl Spectrosc 49: 14AGoogle Scholar
  16. 16.
    Zupan J (1993) Neural Networks for Chemists, An Introduction. Verlag Chemie, Weinheim. Zupan J (1998) Neural Networks in Chemistry, in von Ragué Schleyer P (ed), Encyclopedia of Computational Chemistry, Wiley, Chichester, p 1813Google Scholar
  17. 17.
    Robb EW, Munk ME (1990) A Neural Network Approach to Infrared Spectrum Interpretation. Microchim Acta [Wien] I: 131CrossRefGoogle Scholar
  18. 18.
    Anker LS, Jurs PC (1992) Prediction of Carbon-13 NMR Chemical Shifts by Artificial Neural Networks. Anal Chem 64:1157. Borggaard C, Thodberg HH (1992) Optimal Minimal Neural Interpretation of Spectra. Anal Chem 64: 545. Meisen WJ, Smits JRM, Rolf HG, Kateman G (1993) Two-dimensional mapping of IR spectra using a parallel implemented self-organising feature map. Chemom Intell Lab Syst 18: 195. van Est QC, Schoenmakers PJ, Smits JRM, Nijssen WPM (1993) Practical Implementation of Neural Networks for the Interpretation of Infrared Spectra, Vibrational Spectrosc 4: 263Google Scholar
  19. 19.
    Libnau FO, Toft J, Christy AA, Kvalheim OM (1994) Structure of Liquid Water Determined from Infrared Temperature Profiling and Evolutionary Curve Resolution. J Am Chem Soc 116:8311CrossRefGoogle Scholar
  20. 20.
    Kvalheim OM, Yi-Zeng Liang (1992) Heuristic Evolving Latent Projections: Resolving Two-Way Multicomponent Data, 1. Selectivity, Latent-Projective Graph, Datascope, Local Rank and Unique Resolution. Anal Chem 64: 936,946CrossRefGoogle Scholar
  21. 21.
    Vandeginste B, Essers R, Bosman T, Reijen J, Kateman G (1985) Three-Component Curve Resolution in Liquid Chromatography with Multiwavelength Diode Array Detection. Anal Chem 57: 971. Kawata S, Komeda H, Sasaki K, Minami S (1985) Advanced Algorithm for Determining Component Spectra Based on Principal Component Analysis. Appl Spectrosc 39: 610. Devaus MF, Bertrand D, Robert P, Qannari M (1988) Application of Multidimensional Analyses to the Extraction of Discriminant Spectral Patterns from NIR Spectra. Appl Spectrosc 42: 1015. Saarinen P, Kauppinen J (1991) Multicomponent Analysis of FT-IR Spectra, Appl Spectrosc 45: 953. Friedrich HB, Jung-Pin Yu (1987) Combination of Orthogonal Spectra to Estimate Component Spectra in Multicomponent Mixtures. Appl Spectrosc 41: 227. Yi-Zeng Liang, Kvalheim OM, Manne R (1993) White, gray and black multicomponent systems. A Classification of mixture problems and methods for the quantitative analysis, Chemom Intell Lab Syst 18: 235,22: 229 (1994). Karstang TV, Kvalheim OM (1991) Multivariate Prediction and Background Correction Using Local Modeling and Derivative Spectroscopy. Anal Chem 63: 767. Windig W (1992) Self-modelling mixture analysis of spectral data with continuous concentration profiles. Chemom Intell Lab Syst 16: 1. Donahue SM, Brown CW (1991) Successive Average Orthogonalization of Spectral Data. Anal Chem 63: 980. Malinowski ER (1992) Window Factor Analysis: Theoretical Derivation and Application to Flow Injection Data. J Chemom 6: 29Google Scholar
  22. 22.
    Gans P (1992) Data Fitting in the Chemical Sciences, Wiley. Allen GC, McMeeking RF (1978) Deconvolution of Spectra by Least-Squares Fitting. Analyt Chim A 103: 73. Gans P (1976) Numerical Methods for Data-Fitting Problems. Coord Chem Rev. 19: 99Google Scholar
  23. 23.
    De Weijer AP, Lucasius CB, Buydens L, Kateman G, Heuvel HM, Mannee H (1994) Curve Fitting Using Natural Computation. Anal Chem 66: 23. Ferry A, Jacobsson P (1995) Curve Fitting and Deconvolution of Instrumental Broadening: A Simulated Annealing Approach. Appl Spectrosc 49: 273Google Scholar
  24. 24.
    Efimow AM (1995) Optical Constants of Inorganic Glasses. CRC Press, Boca RatonGoogle Scholar
  25. 25.
    Programm SCOUT 98, www.mtheiss.comGoogle Scholar
  26. 26.
    Kelly JJ, Barlow CL, Jinguji TM, Callis JB (1989) Prediction of Gasoline Octane Numbers from Near-Infrared Spectral Features in the Range 660–1215 nm. Anal Chem 61:313CrossRefGoogle Scholar

Copyright information

© Springer-Verlag Berlin Heidelberg 2001

Authors and Affiliations

  1. 1.Institut für Anorganische und Analytische ChemieFriedrich-Schiller-Universität JenaJena
  2. 2.Analytik Jena AGJena
  3. 3.Institut für Physikalische ChemieFriedrich-Schiller-Universität JenaJena
  4. 4.Brooks Automation GmbHJena

Personalised recommendations