Skip to main content

A Dynamic Method of Experiment Design of Computer Aided Sensory Evaluation

  • Chapter

Part of the book series: Advances in Soft Computing ((AINSC,volume 41))

Abstract

It plays an important role in sensory evaluation to optimize experiment design, i.e. using less number of tests to obtain available data as possible by intelligent technologies. This paper presents one method for optimizing experiment design based on learning automaton, and this method is applied in computer aided sensory evaluation (abbr. CASE). The validity of this method is showed by the result of experiment from CASE.

This is a preview of subscription content, log in via an institution.

Buying options

Chapter
USD   29.95
Price excludes VAT (USA)
  • Available as PDF
  • Read on any device
  • Instant download
  • Own it forever
eBook
USD   169.00
Price excludes VAT (USA)
  • Available as PDF
  • Read on any device
  • Instant download
  • Own it forever
Softcover Book
USD   219.99
Price excludes VAT (USA)
  • Compact, lightweight edition
  • Dispatched in 3 to 5 business days
  • Free shipping worldwide - see info

Tax calculation will be finalised at checkout

Purchases are for personal use only

Learn about institutional subscriptions

Preview

Unable to display preview. Download preview PDF.

Unable to display preview. Download preview PDF.

References

  1. Dijksterhuis, G.B.: Multivariate data analysis in sensory and consumer science. Food & nutrition press, Trumbull (1997)

    Google Scholar 

  2. Zeng, X.Y., Ding, Y.S.: An introduction to intelligent evaluation. Journal of Donghua university 3, 1–4 (2004)

    Google Scholar 

  3. Goer, J.C.: Generalized Procrustes Analysis. Psychometrika 40, 33–51 (1975)

    Article  MathSciNet  Google Scholar 

  4. Van der Burg, E.: Nonlinear canonical correlation and some related techniques. DSWO press, Leiden (1988)

    Google Scholar 

  5. Van der Burg, E., Dijksterhuis, D.B.: Nonlinear canonical Correlation Analysis of Multiway Data. In: Coppi, R., Bolasco, S. (eds.) Multiway Data Analysis, pp. 245–255. Elsevier Science Publisher B.V, Amsterdam (1989)

    Google Scholar 

  6. Stone, H., Sidel, J.L.: Sensory Evaluation Practices, 3rd edn. Academic Press, San Diego (2004)

    Google Scholar 

  7. Lewandowsky, S., Murdock, B.B.: Memory for serial order. Psychological Review 96, 25–57 (1989)

    Article  Google Scholar 

  8. Lakshmivarahan, S.: Learning algorithms theory and applications. Springer, New York (1981)

    MATH  Google Scholar 

  9. Zeng, X.Y., Liu, Z.Y.: A learning automata based algorithm for optimization of Continuous Complex Functions. Information science 174(Issue 3-4), 165–175 (2005)

    Article  MATH  MathSciNet  Google Scholar 

  10. Liu, X.H., et al.: A method for optimizing dichotomy in sensory evaluation. In: 2006 International Conference on Intelligent Systems & Knowledge Engineering (ISKE2006), Shanghai, China, April 6-7 (2006)

    Google Scholar 

Download references

Author information

Authors and Affiliations

Authors

Editor information

Patricia Melin Oscar Castillo Eduardo Gomez Ramírez Janusz Kacprzyk Witold Pedrycz

Rights and permissions

Reprints and permissions

Copyright information

© 2007 Springer-Verlag Berlin Heidelberg

About this chapter

Cite this chapter

Luo, B. (2007). A Dynamic Method of Experiment Design of Computer Aided Sensory Evaluation. In: Melin, P., Castillo, O., Ramírez, E.G., Kacprzyk, J., Pedrycz, W. (eds) Analysis and Design of Intelligent Systems using Soft Computing Techniques. Advances in Soft Computing, vol 41. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-540-72432-2_51

Download citation

  • DOI: https://doi.org/10.1007/978-3-540-72432-2_51

  • Publisher Name: Springer, Berlin, Heidelberg

  • Print ISBN: 978-3-540-72431-5

  • Online ISBN: 978-3-540-72432-2

  • eBook Packages: EngineeringEngineering (R0)

Publish with us

Policies and ethics