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Difference variance dispersion graphs for comparing response surface designs with applications in food technology

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  • L. A. Trinca
  • S. G. Gilmour

Abstract

Variance dispersion graphs have become a popular tool in aiding the choice of a response surface design. Often differences in response from some particular point, such as the expected position of the optimum or standard operating conditions, are more important than the response itself. We describe two examples from food technology. In the first, an experiment was conducted to find the levels of three factors which optimized the yield of valuable products enzymatically synthesized from sugars and to discover how the yield changed as the levels of the factors were changed from the optimum. In the second example, an experiment was conducted on a mixing process for pastry dough to discover how three factors affected a number of properties of the pastry, with a view to using these factors to control the process. We introduce the difference variance dispersion graph (DVDG) to help in the choice of a design in these circumstances. The DVDG for blocked designs is developed and the examples are used to show how the DVDG can be used in practice. In both examples a design was chosen by using the DVDG, as well as other properties, and the experiments were conducted and produced results that were useful to the experimenters. In both cases the conclusions were drawn partly by comparing responses at different points on the response surface.

Suggested Citation

  • L. A. Trinca & S. G. Gilmour, 1999. "Difference variance dispersion graphs for comparing response surface designs with applications in food technology," Journal of the Royal Statistical Society Series C, Royal Statistical Society, vol. 48(4), pages 441-455.
  • Handle: RePEc:bla:jorssc:v:48:y:1999:i:4:p:441-455
    DOI: 10.1111/1467-9876.00164
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    Cited by:

    1. Palhazi Cuervo, Daniel & Goos, Peter & Sörensen, Kenneth, 2017. "An algorithmic framework for generating optimal two-stratum experimental designs," Computational Statistics & Data Analysis, Elsevier, vol. 115(C), pages 224-249.

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