ReviewFood science & nutrition2025
Trends and Directions of Preference Elicitation and Assessment in Food Science: Single-, Pair-, and Multi-Criteria Ranking Methods.
Review in Food science & nutrition, 2025. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 2 papers.
What it found
Each row is one number read from the abstract, on the scale the paper reported it, with its interval. Left of the dashed line favours the treatment, right favours the comparator. Under each row is the sentence it came from. New to these charts? A ten-minute tutorial.
The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.
The trial behind it
Trials whose registry record cites this paper, or whose number appears in the abstract. A trial that started after this paper was published is citing it as background, not reporting it.
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Who cites it
2 citing papers in PubMed.
- EEG feature extraction and machine learning for consumer preference analysis of whisky.Current research in food science · 2026Article
- Trends and Directions of Preference Elicitation and Assessment in Food Science: Single-, Pair-, and Multi-Criteria Ranking Methods.Food science & nutrition · 2025Review
Corrections and comments
PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.
Authors and funding
5 authors.
Funding
No grant is acknowledged in the PubMed record.
Abstract
Different ranking methods have long been used in sensory testing. Their diffusion was mainly due to their simplicity, their standardized methods, their software support, and their wide range of applications. However, a number of research and industrial problems have emerged recently that cannot be adequately solved by the standard approaches, making it necessary to apply and adapt methods from other disciplines. This article discusses the ranking methods of international sensory standardization (single-, pair-ranking methods, balanced incomplete block design (BIBD) according to the structure of standardization). We overview ranking methods in international sensory competitions. We evaluate some potential applications of ranking methodological developments pair ranking, Analytic Hierarchy Process (AHP), multi-criteria decision making (MCDM), graph theory, developed and applied by other disciplines, which are less used in sensory sciences. The principles and applications of these methodologies are summarized, and factors affecting their effectiveness and limitations are discussed. Preference modeling and quantification are key questions of decision and sensory sciences. Both areas require true, unbiased individual responses as well as their appropriate aggregation. Competitions are typically multi-criteria problems, evaluated by several decision makers. To better understand consumer preferences and to explore complex consumer choices, different methods and software from other disciplines-decision and social choice theory, operations research, economics (e.g., ranking based on pairwise comparisons, AHP, MCDM, graph theory)-are adapted and disseminated. In addition to surveying latest trends, we propose some areas of future research development.
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Registered trials
Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.