Autonomic Responses Associated with Olfactory Preferences of Fragrance Consumers: Skin Conductance, Respiration, and Heart Rate

Author:

Tang Bangbei12,Zhu Mingxin23,Wu Yingzhang4,Guo Gang4ORCID,Hu Zhian1,Ding Yongfeng2

Affiliation:

1. Department of Physiology, Army Medical University, Chongqing 400038, China

2. School of Intelligent Manufacturing Engineering, Chongqing University of Arts and Sciences, Chongqing 402160, China

3. School of Mechanical Engineering, Sichuan University of Science & Engineering, Yibin 643000, China

4. College of Mechanical and Vehicle Engineering, Chongqing University, Chongqing 400044, China

Abstract

Assessing the olfactory preferences of consumers is an important aspect of fragrance product development and marketing. With the advancement of wearable device technologies, physiological signals hold great potential for evaluating olfactory preferences. However, there is currently a lack of relevant studies and specific explanatory procedures for preference assessment methods that are based on physiological signals. In response to this gap, a synchronous data acquisition system was established using the ErgoLAB multi-channel physiology instrument and olfactory experience tester. Thirty-three participants were recruited for the olfactory preference experiments, and three types of autonomic response data (skin conductance, respiration, and heart rate) were collected. The results of both individual and overall analyses indicated that olfactory preferences can lead to changes in skin conductance (SC), respiration (RESP), and heart rate (HR). The trends of change in both RESP and HR showed significant differences (with the HR being more easily distinguishable), while the SC did not exhibit significant differences across different olfactory perception preferences. Additionally, gender differences did not result in significant variations. Therefore, HR is more suitable for evaluating olfactory perception preferences, followed by RESP, while SC shows the least effect. Moreover, a logistic regression model with a high accuracy (84.1%) in predicting olfactory perception preferences was developed using the changes in the RESP and HR features. This study has significant implications for advancing the assessment of consumer olfactory preferences.

Funder

National Natural Science Foundation of China

Special Funding for Postdoctoral Research Projects in Chongqing

Science, Technology Research Project of Chongqing Municipal Education Commission

Technology Innovation and Application Development Project of Chongqing Yongchuan District Science and Technology Bureau

Publisher

MDPI AG

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