Determination of the Composition of Foodstuffs Using Microwave Dielectric Spectra

  • Frank Daschner
  • Reinhard Knöchel


Hide Layer Salt Content Partial Little Square Regression Calculation Effort Validation Group 
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  1. 1.
    Kent M, Anderson D (1996) Dielectric studies of added water in poultry meat and scallops, J Food Eng 28: 239–259CrossRefGoogle Scholar
  2. 2.
    Kent M (1999) Simultaneous determination of composition and other material properties by using microwave sensors. In: Sensors update, Wiley-VCH, Weinheim, vol 7, pp 4–25Google Scholar
  3. 3.
    Gajda G, Stuchly S (1983) An equivalent circuit of an open-ended coaxial line. IEEE Trans Instrum Meas IM-32(4): 506–508Google Scholar
  4. 4.
    Stuchly M, Brady M, Stuchly S, Gajda G (1982) Equivalent circuit of an open-ended coaxial Line in a lossy dielectric. IEEE Trans Instrum Meas IM-31(2): 116–119Google Scholar
  5. 5.
    Marsland TP, Evans S (1987) Dielectric measurement with an open-ended coaxial probe, IEE Proc 134(4): 341–349Google Scholar
  6. 6.
    Grant EH, Sheppard RJ, South GP (1978) Dielectric behaviour of biological molecules in solution. Oxford University Press, OxfordGoogle Scholar
  7. 7.
    Hartung J, Epelt B (1995) Multivariate Statistik, Lehr-und Handbuch der angewandten Statistik, 5. Auflage, R. Oldenbourg Verlag, MunichGoogle Scholar
  8. 8.
    Martens H, Naes T (1989): Multivariate Calibration. Wiley, ChichesterGoogle Scholar
  9. 9.
    Kent M, Knöchel R, Daschner F, Berger UK (2000): Composition of foods using microwave dielectric spectra. Eur Food Res Technol no 210: 359–366CrossRefGoogle Scholar
  10. 10.
    Archibald DD, Trabelsi S, Kraszewski AW, Nelson SO (1998): Regression analysis of microwave spectra for temperature-compensated and density-independent determination of wheat moisture content. Appl Spectrosc 52(11): 1435–1446CrossRefGoogle Scholar
  11. 11.
    Bartley PG, Nelson SO, McClendon RW, Trabelsi S (1999): Determination of moisture content in wheat using an artificial neural network. In: 3rd workshop on electromagnetic interaction with water and moist substances, Athens, GA, pp 74–78Google Scholar
  12. 12.
    Patterson D (1997) Künstliche neuronale Netze, Prentince Hall Verlag, HaarGoogle Scholar
  13. 13.
    Neural Network Toolbox, User’s Guide, Version 4, The Mathworks Inc., 2000Google Scholar
  14. 14.
    Kreinovich VY (1991): Arbitrary nonlinearity is sufficient to represent all functions by neural networks: a theorem. Neural Networks 4: 381–383CrossRefGoogle Scholar

Copyright information

© Springer-Verlag Berlin Heidelberg 2005

Authors and Affiliations

  • Frank Daschner
  • Reinhard Knöchel
    • 1
  1. 1.Christian Albrechts University of KielKielGermany

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