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Sensory analysis via multi-block multivariate additive PLS splines

Author

Listed:
  • Rosaria Lombardo
  • Pietro Amenta
  • Myrtille Vivien
  • Robert Sabatier

Abstract

In the last decade, much effort has been spent on modelling dependence between sensory variables and chemical--physical ones, especially when observed at different occasions/spaces/times or if collected from several groups (blocks) of variables. In this paper, we propose a nonlinear generalization of multi-block partial least squares with the inclusion of variable interactions. We show the performance of the method on a known data set.

Suggested Citation

  • Rosaria Lombardo & Pietro Amenta & Myrtille Vivien & Robert Sabatier, 2012. "Sensory analysis via multi-block multivariate additive PLS splines," Journal of Applied Statistics, Taylor & Francis Journals, vol. 39(4), pages 731-743, August.
    • Handle: RePEc:taf:japsta:v:39:y:2012:i:4:p:731-743
    DOI: 10.1080/02664763.2011.611239
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    References listed on IDEAS

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    1. Escofier, B. & Pages, J., 1994. "Multiple factor analysis (AFMULT package)," Computational Statistics & Data Analysis, Elsevier, vol. 18(1), pages 121-140, August.
    2. J. Gower, 1975. "Generalized procrustes analysis," Psychometrika, Springer;The Psychometric Society, vol. 40(1), pages 33-51, March.
    3. R. Lombardo & J.-F. Durand & A. Faraj, 2011. "Iterative Design of Experiments by Non-Linear PLS Models. A Case Study: The Reservoir Simulator Data to Forecast Oil Production," Journal of Classification, Springer;The Classification Society, vol. 28(1), pages 113-125, April.
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