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Open AccessArticle

Estimation of Olfactory Sensitivity Using a Bayesian Adaptive Method

1
Institute of Neuroscience and Medicine INM-3, Research Center Jülich, 52428 Jülich, Germany
2
Psychophysiology of Food Perception, German Institute of Human Nutrition Potsdam-Rehbrücke, 14558 Nuthetal, Germany
*
Author to whom correspondence should be addressed.
Nutrients 2019, 11(6), 1278; https://doi.org/10.3390/nu11061278
Received: 24 April 2019 / Revised: 15 May 2019 / Accepted: 28 May 2019 / Published: 5 June 2019
(This article belongs to the Special Issue Taste, Nutrition and Health)
The ability to smell is crucial for most species as it enables the detection of environmental threats like smoke, fosters social interactions, and contributes to the sensory evaluation of food and eating behavior. The high prevalence of smell disturbances throughout the life span calls for a continuous effort to improve tools for quick and reliable assessment of olfactory function. Odor-dispensing pens, called Sniffin’ Sticks, are an established method to deliver olfactory stimuli during diagnostic evaluation. We tested the suitability of a Bayesian adaptive algorithm (QUEST) to estimate olfactory sensitivity using Sniffin’ Sticks by comparing QUEST sensitivity thresholds with those obtained using a procedure based on an established standard staircase protocol. Thresholds were measured twice with both procedures in two sessions (Test and Retest). Overall, both procedures exhibited considerable overlap, with QUEST displaying slightly higher test-retest correlations, less variability between measurements, and reduced testing duration. Notably, participants were more frequently presented with the highest concentration during QUEST, which may foster adaptation and habituation effects. We conclude that further research is required to better understand and optimize the procedure for assessment of olfactory performance. View Full-Text
Keywords: smell sensitivity; olfaction; threshold; staircase; QUEST smell sensitivity; olfaction; threshold; staircase; QUEST
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Figure 1

  • Externally hosted supplementary file 1
    Doi: 10.5281/zenodo.2548620
    Link: https://doi.org/10.5281/zenodo.2548620
    Description: Measurement data and visualizations of individual experimental runs.
MDPI and ACS Style

Höchenberger, R.; Ohla, K. Estimation of Olfactory Sensitivity Using a Bayesian Adaptive Method. Nutrients 2019, 11, 1278.

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