# Estimation of Olfactory Sensitivity Using a Bayesian Adaptive Method

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## Abstract

**:**

## 1. Introduction

## 2. Materials and Methods

#### 2.1. Participants

#### 2.2. Stimuli

#### 2.3. Procedure

#### 2.3.1. Experimental Sessions

#### 2.3.2. Stimulus Presentation

#### 2.3.3. Staircase

#### 2.3.4. QUEST

#### 2.3.5. Analysis

#### Odor Discrimination and Identification

#### Data Cleaning

#### Test–Restest Reliability

#### Comparison between Procedures

#### Software

## 3. Results

#### 3.1. Odor Discrimination and Identification

#### 3.2. Starting Concentrations

#### 3.3. Test Duration

#### 3.4. Test-Retest Reliability

#### 3.5. Comparison between Procedures

## 4. Discussion

## 5. Conclusions

## 6. Data and Software Availability

## Author Contributions

## Funding

## Acknowledgments

## Conflicts of Interest

## Appendix A

**Figure A1.**Comparison of threshold estimation runs of the same participant during test and retest sessions for QUEST (

**A**) and the staircase (

**B**).

## References

- Boesveldt, S.; Bobowski, N.; McCrickerd, K.; Maître, I.; Sulmont-Rossé, C.; Forde, C.G. The changing role of the senses in food choice and food intake across the lifespan. Food Qual. Prefer.
**2018**, 68, 80–89. [Google Scholar] [CrossRef] - Rasmussen, V.F.; Vestergaard, E.T.; Hejlesen, O.; Andersson, C.U.N.; Cichosz, S.L. Prevalence of taste and smell impairment in adults with diabetes: A cross-sectional analysis of data from the National Health and Nutrition Examination Survey (NHANES). Primary Care Diabetes
**2018**, 12, 453–459. [Google Scholar] [CrossRef] [PubMed] - Sullivan, R.M.; Wilson, D.A.; Ravel, N.; Mouly, A.M. Olfactory memory networks: From emotional learning to social behaviors. Front. Behav. Neurosci.
**2015**, 9. [Google Scholar] [CrossRef] [PubMed] - Li, W. Learning to smell danger: Acquired associative representation of threat in the olfactory cortex. Front. Behav. Neurosci.
**2014**, 8. [Google Scholar] [CrossRef] [PubMed] - Liu, G.; Zong, G.; Doty, R.L.; Sun, Q. Prevalence and risk factors of taste and smell impairment in a nationwide representative sample of the US population: A cross-sectional study. BMJ Open
**2016**, 6, e013246. [Google Scholar] [CrossRef] [PubMed] - Hummel, T.; Sekinger, B.; Wolf, S.; Pauli, E.; Kobal, G. ‘Sniffin’ Sticks’: Olfactory Performance Assessed by the Combined Testing of Odour Identification, Odor Discrimination and Olfactory Threshold. Chem. Senses
**1997**, 22, 39–52. [Google Scholar] [CrossRef] [PubMed] - Hummel, T.; Kobal, G.; Gudziol, H.; Mackay-Sim, A. Normative data for the “Sniffin’ Sticks” including tests of odor identification, odor discrimination, and olfactory thresholds: An upgrade based on a group of more than 3,000 subjects. Eur. Arch. Oto-Rhino-Laryngol.
**2007**, 264, 237–243. [Google Scholar] [CrossRef] [PubMed] - Oleszkiewicz, A.; Schriever, V.A.; Croy, I.; Hähner, A.; Hummel, T. Updated Sniffin’ Sticks normative data based on an extended sample of 9139 subjects. Eur. Arch. Oto-Rhino-Laryngol.
**2019**, 276, 719–728. [Google Scholar] [CrossRef] [PubMed] - Haehner, A.; Mayer, A.M.; Landis, B.N.; Pournaras, I.; Lill, K.; Gudziol, V.; Hummel, T. High Test-Retest Reliability of the Extended Version of the “Sniffin’ Sticks” Test. Chem. Senses
**2009**, 34, 705–711. [Google Scholar] [CrossRef] - Lötsch, J.; Reichmann, H.; Hummel, T. Different Odor Tests Contribute Differently to the Evaluation of Olfactory Loss. Chem. Senses
**2008**, 33, 17–21. [Google Scholar] [CrossRef] - Wetherill, G.B.; Levitt, H. Sequential Estimation of Points on a Psychometric Function. Br. J. Math. Stat. Psychol.
**1965**, 18, 1–10. [Google Scholar] [CrossRef] [PubMed] - Watson, A.B.; Pelli, D.G. Quest: A Bayesian adaptive psychometric method. Percept. Psychophys.
**1983**, 33, 113–120. [Google Scholar] [CrossRef] [PubMed][Green Version] - Höchenberger, R.; Ohla, K. Rapid Estimation of Gustatory Sensitivity Thresholds with SIAM and QUEST. Front. Psychol.
**2017**, 8. [Google Scholar] [CrossRef] [PubMed][Green Version] - Hardikar, S.; Höchenberger, R.; Villringer, A.; Ohla, K. Higher sensitivity to sweet and salty taste in obese compared to lean individuals. Appetite
**2017**, 111, 158–165. [Google Scholar] [CrossRef] [PubMed] - Kobal, G.; Klimek, L.; Wolfensberger, M.; Gudziol, H.; Temmel, A.; Owen, C.M.; Seeber, H.; Pauli, E.; Hummel, T. Multicenter investigation of 1,036 subjects using a standardized method for the assessment of olfactory function combining tests of odor identification, odor discrimination, and olfactory thresholds. Eur. Arch. Oto-Rhino-Laryngol.
**2000**, 257, 205–211. [Google Scholar] [CrossRef] - Rumeau, C.; Nguyen, D.T.; Jankowski, R. How to assess olfactory performance with the Sniffin’ Sticks test
^{®}. Eur. Ann. Otorhinolaryngol. Head Neck Dis.**2016**, 133, 203–206. [Google Scholar] [CrossRef] [PubMed] - García-Pérez, M.A. Forced-choice staircases with fixed step sizes: Asymptotic and small-sample properties. Vis. Res.
**1998**, 38, 1861–1881. [Google Scholar] [CrossRef] - Altman, D.G.; Bland, J.M. Measurement in Medicine: The Analysis of Method Comparison Studies. Statistician
**1983**, 32, 307. [Google Scholar] [CrossRef] - Bland, J.M.; Altman, D. Statistical methods for assessing agreement between two methods of clinical measurement. Lancet
**1986**, 327, 307–310. [Google Scholar] [CrossRef] - Bland, J.M.; Altman, D.G. Measuring agreement in method comparison studies. Stat. Methods Med. Res.
**1999**, 8, 135–160. [Google Scholar] [CrossRef] - Carkeet, A. Exact Parametric Confidence Intervals for Bland-Altman Limits of Agreement. Optometry Vis. Sci.
**2015**, 92, e71–e80. [Google Scholar] [CrossRef] [PubMed][Green Version] - Peirce, J.W. PsychoPy—Psychophysics software in Python. J. Neurosci. Methods
**2007**, 162, 8–13. [Google Scholar] [CrossRef] [PubMed] - Peirce, J.W. Generating stimuli for neuroscience using PsychoPy. Front. Neuroinf.
**2008**, 2. [Google Scholar] [CrossRef] [PubMed] - Vallat, R. Pingouin: Statistics in Python. J. Open Source Softw.
**2018**, 3, 1026. [Google Scholar] [CrossRef] - Oliphant, T.E. Python for Scientific Computing. Comput. Sci. Eng.
**2007**, 9, 10–20. [Google Scholar] [CrossRef] - Millman, K.J.; Aivazis, M. Python for Scientists and Engineers. Comput. Sci. Eng.
**2011**, 13, 9–12. [Google Scholar] [CrossRef] - Seabold, S.; Perktold, J. Statsmodels: Econometric and statistical modeling with Python. In Proceedings of the 9th Python in Science Conference, Austin, TX, USA, 9–15 July 2010; Volume 57, p. 61. [Google Scholar]
- Hunter, J.D. Matplotlib: A 2D Graphics Environment. Comput. Sci. Eng.
**2007**, 9, 90–95. [Google Scholar] [CrossRef] - Croy, I.; Lange, K.; Krone, F.; Negoias, S.; Seo, H.S.; Hummel, T. Comparison between Odor Thresholds for Phenyl Ethyl Alcohol and Butanol. Chem. Senses
**2009**, 34, 523–527. [Google Scholar] [CrossRef] [PubMed] - Running, C.A. High false positive rates in common sensory threshold tests. Atten. Percept. Psychophys.
**2014**, 77, 692–700. [Google Scholar] [CrossRef][Green Version]

**Figure 1.**Threshold estimates for the staircase and QUEST procedures during Test and Retest sessions. Each dot represents one participant. Horizontal lines show the median values, and whisker lengths represent $1.5\phantom{\rule{0.166667em}{0ex}}\times $ inter-quartile range.

**Figure 2.**(

**A**) Correlation between Test and Retest threshold estimates for the staircase and QUEST procedures. (

**B**) Bland–Altman plots showing mean differences between Test and Retest, and limits of agreement corresponding to 95% confidence intervals (CIs) as $\mathrm{mean}\pm 1.96\times \mathrm{SD}$. The shaded areas represent the 95% CIs of the mean and the limits of agreement. Each dot represents one participant.

**Figure 3.**(

**A**) Mean threshold estimates, averaged across Test and Retest sessions for the staircase and QUEST procedures. Horizontal lines show the median values, and whisker lengths represent $1.5\phantom{\rule{0.166667em}{0ex}}\times $ inter-quartile range. (

**B**) Correlation between mean staircase and QUEST threshold estimates. (

**C**) Bland–Altman plot showing mean differences between session means in both procedures, and limits of agreement corresponding to 95% confidence intervals (CIs) as $\mathrm{mean}\pm 1.96\times \mathrm{SD}$. The shaded areas represent the 95% CIs of the mean and the limits of agreement. Each dot represents one participant.

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**MDPI and ACS Style**

Höchenberger, R.; Ohla, K. Estimation of Olfactory Sensitivity Using a Bayesian Adaptive Method. *Nutrients* **2019**, *11*, 1278.
https://doi.org/10.3390/nu11061278

**AMA Style**

Höchenberger R, Ohla K. Estimation of Olfactory Sensitivity Using a Bayesian Adaptive Method. *Nutrients*. 2019; 11(6):1278.
https://doi.org/10.3390/nu11061278

**Chicago/Turabian Style**

Höchenberger, Richard, and Kathrin Ohla. 2019. "Estimation of Olfactory Sensitivity Using a Bayesian Adaptive Method" *Nutrients* 11, no. 6: 1278.
https://doi.org/10.3390/nu11061278