Bayesian D- and I-optimal designs for choice experiments involving mixtures and process variables

Becerra, Mario; Goos, Peter

doi:10.1016/j.foodqual.2023.104928

Cited by 2 publications

(1 citation statement)

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“…Moreover, in the specific literature about CEs, D-optimal designs, starting from the simple fractional factorial designs, and by considering more complex designs [19,30], were defined through specific algorithms for the trial-point selections, and then developed by using Bayesian optimal designs [41]. Furthermore, the use of alternative optimality criteria had a continuous improvement over time, considering, among others, Burgess and Street [32], up to the most recent developments [42], where optimal designs, and specifically the criteria D and I, are expounded and discussed for CEs involving mixture variables, e.g., ingredient proportions and process variables, as cooking features.…”

Section: Literature Reviewmentioning

confidence: 99%

An Optimal Design through a Compound Criterion for Integrating Extra Preference Information in a Choice Experiment: A Case Study on Moka Ground Coffee

Berni,

Nikiforova,

Pinelli

2024

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In this manuscript, we propose an innovative approach to studying consumers’ preferences for coffee, which integrates a choice experiment with consumer sensory tests and chemical analyses (caffeine contents obtained through a High-Performance Liquid Chromatography (HPLC) method). The same choice experiment is administered on two consecutive occasions, i.e., before and after the guided tasting session, to analyze the role of tasting and awareness about coffee composition in the consumers’ preferences. To this end, a Bayesian optimal design, based on a compound design criterion, is applied in order to build the choice experiment; the compound criterion allows for addressing two main issues related to the efficient estimation of the attributes and the evaluation of the sensorial part, e.g., the HPLC effects and the scores obtained through the consumer sensory test. All these elements, e.g., the attributes involved in the choice experiment, the scores obtained for each coffee through the sensory tests, and the HPLC quantitative evaluation of caffeine, are analyzed through suitable Random Utility Models. The initial results are promising, confirming the validity of the proposed approach.

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