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Trends and Directions of Preference Elicitation and Assessment in Food Science: Single-, Pair-, and Multi-Criteria
László Sipos1,2, Zsófia Galambosi1, Péter Biró2,3
1Institute of Food Science and Technology, Department of Postharvest, Commercial and Sensory Science Hungarian University of Agriculture and Life Sciences Budapest Hungary.
Food Science & Nutrition
|August 1, 2025
Summary
Standard sensory testing ranking methods are limited. This study explores advanced techniques like Analytic Hierarchy Process (AHP) and multi-criteria decision making (MCDM) for better preference modeling in sensory science.
Area of Science:
- Sensory Science
- Decision Science
- Preference Modeling
Background:
- Traditional ranking methods in sensory testing offer simplicity and wide application.
- Emerging research and industrial challenges necessitate advanced methodologies beyond standard approaches.
- Existing methods struggle with complex consumer choices and unbiased preference quantification.
Purpose of the Study:
- To discuss international sensory standardization ranking methods (single-, pair-ranking, BIBD).
- To evaluate the application of advanced techniques from other disciplines in sensory science.
- To explore preference modeling and quantification for complex consumer choices.
Main Methods:
- Overview of international sensory standardization ranking methods.
- Evaluation of pair ranking, Analytic Hierarchy Process (AHP), multi-criteria decision making (MCDM), and graph theory.
- Adaptation of methods from decision theory, social choice, operations research, and economics.
Main Results:
- Identified limitations of standard sensory ranking methods for complex problems.
- Summarized principles, applications, effectiveness, and limitations of advanced methodologies.
- Demonstrated the potential of interdisciplinary methods for preference modeling and decision-making in sensory competitions.
Conclusions:
- Advanced ranking methods from other disciplines offer solutions for complex sensory evaluation.
- Pairwise comparisons, AHP, MCDM, and graph theory can enhance preference modeling and consumer choice analysis.
- Further research is needed to adapt and disseminate these methods within sensory sciences.
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