ENBIS Spring Meeting 2018
4 – 6 June 2018; Florence, Italy
Abstract submission: 17 November 2017 – 20 April 2018
Consumers’ Preferences About Coffee: A Choice Experiment Integrated With A Guided Tasting
5 June 2018, 11:55 – 12:20
- Submitted by
- Nedka Dechkova Nikiforova
- Nedka Dechkova Nikiforova (Department of Statistics, Computer Science, Applications ”G.Parenti”, University of Florence), Patrizia Pinelli (Department of Statistics, Computer Science, Applications ”G.Parenti”, University of Florence)
- This talk suggests an innovative approach to analyze the consumers’ preferences for coffee consumption by integrating a choice experiment with consumers’ sensory test and chemical analysis (caffeine and antioxidants evaluated by a High Performance Liquid Chromatography-HPLC method). Firstly, two types of coffee (blend Arabica-Robusta and 100% Arabica) are chosen with different organoleptic characteristics, and a guided tasting session is planned through the development of two scorecards for the organoleptic evaluation. Moreover, a Choice Experiment based on optimal design theory is also planned in order to build choice-sets aiming to: i) an efficient estimation of the attributes for the choice experiment, and ii) detection of the effect of the sensory assessment scores obtained through the guided tasting. For this purpose, a compound design criterion (Wynn, 1970; Atkinson and Bogacka, 1997; Atkinson et al., 2007) is applied for addressing the issues described above. The same choice experiment is administered in two consecutive time occasions, e.g. before and after the guided tasting session, in order to assess the role of tasting in determining the consumers’ preferences. All these elements, e.g. the attributes involved in the choice experiment, the scores obtained for each coffee through the consumers’ sensory test and the HPLC results, are analyzed through Random Utility Models. The obtained results clearly indicate that the guided tasting jointly with the information provided on the two coffees, have a relevant impact on the consumers’ preferences, and contributes to unequivocally define them, by also allowing us to better understand the consumers’ behavior.
1) Wynn H. P. (1970). The sequential generation of D-optimal experimental designs. The Annals of Mathematical Statistics, 41:1055-1064.
2) Atkinson, A. C. and Bogacka, B. (1997). Compound D- and D_S- Optimum Designs for Determining the Order of a Chemical Reaction. Technometrics, 39(4):347-356.
3) Atkinson, A. C., Donev, A.N. and Tobias R.D. (2007). Optimum Experimental Designs, with SAS. Oxford: Oxford University Press.
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