An Affective Approach to Developing Marketing Strategies of Mineral Water
Junzo Watada*, Le Yu*, Munenori Shibata**, and Marzuki Khalid***
*Graduate School of IPS, Waseda University, 2-7 hibikino, Wakamatsu, Kitakyuusyuu-shi, Fukuoka 808-0135, Japan
**Taste & Aroma Strategic Research Institute, Shinkawa Chuou, Bldg. 8F, 1-17-24 Shinkawa, Chuouku, Tokyo 104-0033, Japan
***Malaysia University of Tectnology, Malaysia
This study is concerned with the development of marketing strategies for mineral water based on consumers’ taste preferences, by analyzing the taste components of mineral water. In this study, we used a twodimensional analysis to classify taste data. We conducted a correlation analysis to identify the characteristics of taste data. We applied a combination of principal component analysis and self-organizing map to classify mineral water tastes. Based on this evaluation, we identified some marketing strategies in the conclusion. According to this study, the taste of mineral water is not determined by the origin and is not influenced by the hardness of the water.
-  J. Watada, M. Takagi, N. Yubazaki, and H. Hirano, “Realization of the Comfortable Space using Brainwave-Signals,” J. of Systems and Control Engineering (JSCE), Vol.220, No.8, pp. 667-673, Dec. 2006.
-  J. Watada, K. Aoki, M. Kawano, and M. S. Hitam, “Dual Scaling Approach to Data Mining from Text Data Base,” J. of Advanced Computational Intelligence Intelligent Informatics (JACIII), Vol.10, No.4, pp. 441-447, July 2006.
-  Y.-C. Hsiao and J. Watada, “Systematic Construction of Shape Grammars for Form Design of Products,” Japan society of Kansei Engineering, 2009.
-  L.-C. Lin and J. Watada, “Building a Decision Support System for Urban Design Based on the Creative City Concept,” C. P. Lim and L. C. Jain (Eds.), New Directions in Decision Support Systems: Methodologies and Applications, Springer-Verlag, Germany, pp. 317-346, 2010.
-  J. Watada, L. Yu, M. Ogura, M. Shibata, and T. Fukuda, “Building the Marketing Strategies Based on Kansei of Tastes,” Proc., Kansei Engineering Conf., at Tokyo, Sept. 3-5, 2011. (in Japanese)
-  Taste & Aroma Strategic Research Institute,
http://www.mikaku.jp, on 2011.12.1.
-  The Mineral Water Association of Japan,
http://minekyo.net/index.php, on 2012.1.26.
-  S. Knox and L. de Chernatory, “The application of multi-attribute modeling techniques to the mineral water market,” the Quarterly Review of Marketing, School Working Paper SWP 35/89, 1989.
-  J.Watada, L.-C. Lin, L. Ding,M. I. Shapiai, L. C. Chew, Z. Ibrahim, L. W. Jau, and M. Khalid, “A Rough-Set-Based Two-Class Classifier for Large Imbalanced Dataset, Smart Innovation, Systems and Technologies, 1,” G. Phillips-Wren et al. (Eds.), Vol.4, Advances in Intelligent Decision Technologies, XIII., Springer-Verlag Berlin Heidelberg, pp. 641-651, 2010.
-  K. Beullens, D. Kirsanov, J. Irudayaraj, A. Rudnitskaya, A. Legin, B. M. Nicolai, and J. Lammertyn, “The electronic tongue and ATRFTIR for rapid detection of sugers and acids in tomatoes,” Sensors and Actuators, pp. 107-115, 2006.
-  H. Abdi and L. J. Williams, “Principal Component Analysis, Wile Interdisciplinary Reviews,” Computational Statistics, Vol.2, Issue 4, pp. 433-459, July/August 2010.
-  K. Beullens, P. Meszaros, S. Vermeir, D. Kirsanov, A. Legin, S. Buysens, N. Cap, B. M. Nicola, and J. Lammertyn, “Analysis of tomato taste using two types of electronic tongues,” Sensors and Actuators, pp. 10-17, 2008.
-  W. He, X. Hu, L. Zhao, X. Liao, Y. Zhang, M. Zhang, and J. Wu, “Evaluation of Chinese tea by the electronic tongue: Correlation with sensory properties and classification according to geographical origin and grade level,” Food Research Int., pp. 1462-1467, 2009.
-  D. Kara, “Evaluation of trace metal concentrations in some herbs and herbal teas by principal component analysis,” Food Chemistry, pp. 347-354, 2009.
-  T. Kohonen, “Self-organized Formation of Topologically Correct Feature Maps, Biological Cybemetics,” Vol.44, pp. 135-140, 1982.
-  A. Astel, S. Tsakovski, P. Barbieri, and V. Simeonov, “Comparison of Self-organizing Maps Classification Approach with Cluster and Principal Analysis for Large Environmental Data Sets,” Water Research, Vol.41, No.19, pp. 4566-4578, 2007.
-  J. L. Giraudel and S. Lek, “A comparison of self-organizing map algorithm and some conventional statistical methods for ecological community ordination,” Ecological Modeling, Vol.146, Nos.1-3, pp. 329-339, 2001.
-  A. Ultsch, “U*-Matrix: a Tool to visualize Clusters in high dimensional Data,” Technical Report 36, CS Department, Philipps-University Marburg, Germany, 2004.
-  J. Vesanto and E. Alhoniemi, “Clustering of the Self-organizing Map,” IEEE Trans. on Neural Networks, Vol.11, No.3, pp. 586-600, May 2000.
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