Recipe Prediction

  • Georg Klein
Part of the Springer Series in Optical Sciences book series (SSOS, volume 154)


The great progress in industrial color physics in the field of recipe prediction over the past few decades is due, in no small part, to the development of the sophisticated architecture and large memory of modern computers as well as user-friendly human interface design of software. A method of recipe prediction has clear utility. If one has such a method, this means that a given coloration of unknown colorant composition can be reproduced only from available colorants; this is accomplished by calculating, in advance, the necessary concentrations of the components. In literature this method is sometimes also called color-match prediction; here, we use the shorter term recipe prediction.


Color Difference Optical Constant Absorption Pigment Effect Colorant Color Constancy 
These keywords were added by machine and not by the authors. This process is experimental and the keywords may be updated as the learning algorithm improves.


  1. 1.
    Park, RH, Stearns, EI: “Spectrometric formulation”, J Opt Soc Am 34 (1944) 112ADSCrossRefGoogle Scholar
  2. 2.
    Allen, E: “Basic equations used in computer color matching”, J Opt Soc Am 56 (1966) 1256ADSCrossRefGoogle Scholar
  3. 3.
    McGinnis, PH, Jr: “Spectrophotometric color matching with the least squares technique”, Color Engg 5 (1967) 22Google Scholar
  4. 4.
    Gall, L: “Computer Colour Matching”, Colour 73, Adam Hilger, London (1973) 153Google Scholar
  5. 5.
    Allen, E: “Basic equations used in computer color matching, II, tristimulus match, two constant theory”, J Opt Soc Am 64 (1974) 991ADSCrossRefGoogle Scholar
  6. 6.
    Kuehni, RG: “Computer Colorant Formulation”, Lexington Books, Lexington, MA (1975)Google Scholar
  7. 7.
    Eitle, D, Pauli, H: “Rezeptvorausberechnung unter Beruecksichtigung des Deckver moegens”, XIV FATIPEC-Congress Budapest (1978) 209Google Scholar
  8. 8.
    Allen, E: “Colorant Formulation and Shading”, in: Grum, F, Bartelson, CJ, eds: “Optical Radiation Measurement”, Vol 2, “Color Measurement”, Academic Press, New York (1980) 290Google Scholar
  9. 9.
    McLaren, K: “The Colour Science of Dyes and Pigments”, 2nd ed, Hilger, Bristol (1986)Google Scholar
  10. 10.
    McDonald, R: “Recipe prediction for textiles”, in: McDonald, R, ed: “Colour Physics for Industry”, 2nd ed, Soc of Dyers and Colourists, Bradford (1997)Google Scholar
  11. 11.
    Lawson, CL, Hanson, RJ: “Solving least squares problems”, SIAM, Philadelphia (1995)CrossRefMATHGoogle Scholar
  12. 12.
    Wolberg, J: “Data Analysis Using the Method of Least Squares”, Springer, Berlin (2006)Google Scholar
  13. 13.
    Grossmann, W: “Grundzuege der Ausgleichsrechnung”, 3rd ed, Springer, Berlin (1969)CrossRefGoogle Scholar
  14. 14.
    Press, WH, Teukolsky, SA, Vetterling, WT, Flannery, BP: “Fortran numerical Recipes”, Cambridge Univ Press, Cambridge UK (2008)MATHGoogle Scholar
  15. 15.
    Klein, GA: “Farbenphysik fuer industrielle Anwendungen”, Springer, Berlin, Heidelberg (2004)CrossRefGoogle Scholar
  16. 16.
    Sluban, B: “Comparison of Colorimetric and Spectrometric Algorithms for Computer Match Prediction”, Col Res Appl 18 (1993) 74CrossRefGoogle Scholar
  17. 17.
    Marquardt, DW: “An algorithm for least squares estimation of nonlinear parameters”, J Soc Ind Appl Math 11 (1963) 431MathSciNetCrossRefMATHGoogle Scholar
  18. 18.
    Nobbs, JH: “Colour-match prediction for pigmented materials”, in: McDonald, R, ed: “Colour Physics for Industry”, 2nd ed, Soc of Dyers and Colourists, Bradford (1997)Google Scholar
  19. 19.
    Sluban, B, Nobbs, JH: “Colour Correctibility of a Colour Matching Recipe”, Col Res Appl 22 (1997) 88CrossRefGoogle Scholar
  20. 20.
    Sluban, B, Sauperl, O: “A sensitivity model and repeatability of the recipe colour”, Croatic Chem Acta 74 (2001) 315Google Scholar
  21. 21.
    Won, K: “Introduction to object-oriented databases”, MIT Press, Cambridge, MA (1991)Google Scholar
  22. 22.
    Lausen, G: “Datenbanken”, Elsevier, Spektrum Akademischer Verlag, Muenchen, Heidelberg (2005)Google Scholar
  23. 23.
    Zadeh, LA, ed: “Fuzzy logic for the management of uncertainty”, Wiley, New York (1992)Google Scholar
  24. 24.
    Nedjah, N, Mourelle, L: “Fuzzy systems engineering: theory and practice”, Springer, Berlin, Heidelberg (2005)CrossRefMATHGoogle Scholar
  25. 25.
    Spitzer, D, Gottenbos, R, van Hensbergen, P, Lucassen, M: “A novel Approach to Color Matching of Automotive Coatings”, Prog Org Coat 29 (1996) 235CrossRefGoogle Scholar
  26. 26.
    Westland, S: “Advances in artificial intelligence for the colour Industry”, J Soc Dyers Col 110 (1994) 370CrossRefGoogle Scholar
  27. 27.
    Priddy, KL, Leller, PE: “Artificial neural networks”, SPIE Press, Bellingham, WA (2005)CrossRefGoogle Scholar

Copyright information

© Springer Science+Business Media, LLC 2010

Authors and Affiliations

  • Georg Klein
    • 1
  1. 1.HerrenbergGermany

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