Developing Marketing Strategies Based on Taste Analysis of Mineral Water

  • Le Yu
  • Junzo Watada
  • Munenori Shibata
Conference paper
Part of the Smart Innovation, Systems and Technologies book series (SIST, volume 15)


This research concerns with the development of marketing strategy of mineral water based on people’s taste preference by analyzing taste components of mineral water. A two-dimensional analysis has been used in classifying tastes’ data. The characteristics of data are recognized in tastes of mineral water by correlation analysis. A combination of Principal Component Analysis and Self-organizing Map is applied to classify the tastes of mineral water. Some marketing strategies are concluded after the evaluation.


Taste analysis mineral water soft computing model SOM Kansei Engineering 


Unable to display preview. Download preview PDF.

Unable to display preview. Download preview PDF.


  1. 1.
    Abdi, H., Williams, L.J.: Principal Component Analysis, Wile Interdisciplinary Reviews. Computational Statistics 2(4), 433–459 (2010)Google Scholar
  2. 2.
    Astel, A., Tsakovski, S., Barbieri, P., Simeonov, V.: Comparison of Self-organizing Maps Classification Approach with Cluster and Principal Analysis for Large Environmental Data Sets. Water Research 41(19), 4566–4578 (2007)CrossRefGoogle Scholar
  3. 3.
    Beullens, K., Kirsanov, D., Irudayaraj, J., Rudnitskaya, A., Legin, A., Nicolai, B.M., Lammertyn, J.: The electronic tongue and ATR-FTIR for rapid detection of sugers and acids in tomatoes. Sensors and Actuators, 107–115 (2006)Google Scholar
  4. 4.
    Beullens, K., Meszaros, P., Vermeir, S., Kirsanov, D., Legin, A., Buysens, S., Cap, N., Nicolaı, B.M., Lammertyn, J.: Analysis of tomato taste using two types of electronic tongues. Sensors and Actuators, 10–17 (2008)Google Scholar
  5. 5.
    Giraudel, J.L., Lek, S.: A comparison of self-organizing map algorithm and some conventional statistical methods for ecological community ordination. Ecological Modeling 146(1-3), 329–339 (2001)CrossRefGoogle Scholar
  6. 6.
    He, W., Hu, X., Zhao, L., Liao, X., Zhang, Y., Zhang, M., Wu, J.: Evaluation of Chinese tea by the electronic tongue: Correlation with sensory properties and classification according to geographical origin and grade level. Food Research International, 1462–1467 (2009)Google Scholar
  7. 7.
    Kara, D.: Evaluation of trace metal concentrations in some herbs and herbal teas by principal component analysis. Food Chemistry, 347–354 (2009)Google Scholar
  8. 8.
    Knox, S., de Chernatory, L.: The application of multi-attribute modeling techniques to the mineral water market. The Quarterly Review of Marketing, School Working Paper SWP 35/ 89Google Scholar
  9. 9.
    Kohonen, T.: Self-organized Formation of Topologically Correct Feature Maps. Biological Cybemetics 44, 135–140 (1982)MathSciNetMATHCrossRefGoogle Scholar
  10. 10.
    Ultsch, A.: U*-Matrix: a Tool to visualize Clusters in high dimensional Data. Technical Report 36, CS Department, Philipps-University Marburg, Germany (2004)Google Scholar
  11. 11.
    Vesanto, J., Alhoniemi, E.: Clustering of the Self-organizing Map. IEEE Transactions on Neural Networks 11(3), 586–600 (2000)CrossRefGoogle Scholar
  12. 12.
    Watada, J., Le, Y., Ogura, M., Shibata, M., Fukuda, T.: Building the Marketing Strategies Based on Kansei of Tastes. In: Proceedings of Kansei Engineering Conference at Tokyo, September 3-5 (2011) (in Japanese)Google Scholar
  13. 13.
    Taste & Aroma Strategic Research Institute (December 1, 2011),
  14. 14.
    The Mineral Water Association of Japan (January 26, 2012),

Copyright information

© Springer-Verlag Berlin Heidelberg 2012

Authors and Affiliations

  • Le Yu
    • 1
  • Junzo Watada
    • 1
  • Munenori Shibata
    • 2
  1. 1.Graduate School of IPSWaseda UniversityKitakyuusyuu-shiJapan
  2. 2.Taste & Aroma Strategic Research InstituteChuoukuJapan

Personalised recommendations