Modeling and correction of sensitivity thresholds determined by best EstimateThreshold (BET)

IF 4.9 1区农林科学 Q1 FOOD SCIENCE & TECHNOLOGY

Food Quality and Preference Pub Date : 2025-09-10 DOI:10.1016/j.foodqual.2025.105690

Caroline Peltier , Alix Rollinat , Christophe Martin

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引用次数: 0

Abstract

The r-Alternative Forced Choice (r-AFC) test is a test of discrimination in which the subject is presented with three samples, one of which is a test sample containing a nominated stimulus (test sample), the other being references. The subject is instructed to indicate the test sample. Taste and odor sensitivity thresholds are frequently determined using successive r-AFC tests with stimuli in increasing concentrations. The Best Estimate Threshold (BET) method consists in using successive 3-AFC with increasing concentrations to estimate sensitivity threshold. Then, the threshold is estimated using the geometrical mean of the highest concentration that caused an error and the concentration directly below it. However, a subject who feels no difference between the samples may give a correct answer by chance. It leads to consequent potential bias in the determination of the sensitivity thresholds.

This paper aims to formalize and model the thresholds obtained in successive r-AFC in order to quantify the errors inherent in such protocols. It establishes that, when you assumed that the distribution of the true sensitivity threshold is known in the population, the threshold obtained by r-AFC can be modelled with a variable following a specific probability law.

This paper presents the theory of this model, then illustrate it with simulations and application on a real dataset. An R package dedicated to these analyses, AFCR, was also created and is available on github (https://github.com/ChemoSens/AFCR). Therefore, sensory scientists could use the package as a help to set up their sensory protocol or-to analyze their own data.

查看原文本刊更多论文

best EstimateThreshold （BET）确定的灵敏度阈值的建模与校正

r-Alternative Forced Choice （r-AFC）测试是一种歧视测试，其中向受试者提供三个样本，其中一个是包含指定刺激（测试样本）的测试样本，另一个是参考资料。受试者被指示指出测试样本。味觉和气味敏感性阈值通常是通过连续的r-AFC试验来确定的，刺激浓度不断增加。最佳估计阈值（BET）方法是使用连续的3-AFC浓度增加来估计灵敏度阈值。然后，使用引起误差的最高浓度的几何平均值和直接低于它的浓度来估计阈值。然而，一个感觉不到样本之间差异的受试者可能会偶然给出一个正确的答案。在确定灵敏度阈值时，它会导致潜在的偏差。本文旨在对连续r-AFC中获得的阈值进行形式化和建模，以便量化此类协议中固有的误差。它表明，当您假设真实灵敏度阈值在总体中的分布是已知的时，r-AFC获得的阈值可以用遵循特定概率律的变量建模。本文首先介绍了该模型的原理，然后通过仿真和在实际数据集上的应用进行了说明。专门用于这些分析的R包AFCR也被创建，并可在github （https://github.com/ChemoSens/AFCR）上获得。因此，感官科学家可以使用这个包来帮助建立他们的感官协议或分析他们自己的数据。

本文章由计算机程序翻译，如有差异，请以英文原文为准。

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来源期刊

Food Quality and Preference 工程技术-食品科技

CiteScore

10.40

自引率

15.10%

发文量

263

审稿时长

38 days

期刊介绍： Food Quality and Preference is a journal devoted to sensory, consumer and behavioural research in food and non-food products. It publishes original research, critical reviews, and short communications in sensory and consumer science, and sensometrics. In addition, the journal publishes special invited issues on important timely topics and from relevant conferences. These are aimed at bridging the gap between research and application, bringing together authors and readers in consumer and market research, sensory science, sensometrics and sensory evaluation, nutrition and food choice, as well as food research, product development and sensory quality assurance. Submissions to Food Quality and Preference are limited to papers that include some form of human measurement; papers that are limited to physical/chemical measures or the routine application of sensory, consumer or econometric analysis will not be considered unless they specifically make a novel scientific contribution in line with the journal''s coverage as outlined below.