Abstract
In the field of food, as in other fields, the measurement of emotional responses to food and their sensory properties is a major challenge. In the present protocol, we propose a step-by-step procedure that allows a physiological description of odors, aromas, and their hedonic properties. The method rooted in subgroup discovery belongs to the field of data science and especially data mining. It is still little used in the field of food and is based on a descriptive modeling of emotions on the basis of human physiological responses.
Access this chapter
Tax calculation will be finalised at checkout
Purchases are for personal use only
Similar content being viewed by others
References
De Wijk RA, Kooijman V, Verhoeven RHG et al (2012) Autonomic nervous system responses on and facial expressions to the sight, smell, and taste of liked and disliked foods. Food Qual Prefer 26:196–203
De Wijk RA, Kaneko D, Dijksterhuis GB et al (2019) Food perception and emotion measured over time in-lab and in-home. Food Qual Prefer 75:170–178
Verastegui-Tena L, Schulte-Holierhoek A, van Trijp H et al (2017) Beyond expectations: the responses of the autonomic nervous system to visual food cues. Physiol Behav 179:478–486
He, W. (2016). Beyond liking: emotional and physiological responses to food stimuli (Doctoral dissertation, Wageningen University and Research)
Kuoppa P, Pulkkinen K, Tarvainen MP et al (2016) Psychophysiological responses to positive and negative food and nonfood visual stimuli. J Neurosci Psychol Econ 9:78–88
Nardelli M, Lanata A, di Francesco F et al (2020) Investigating complex cardiovascular dynamics during hedonic olfactory elicitation. In: 2020 11th conference of the European Study Group on Cardiovascular Oscillations (ESGCO), pp 1–2
Walsh AM, Duncan SE, Bell MA et al (2017) Integrating implicit and explicit emotional assessment of food quality and safety concerns. Food Qual Prefer 56:212–224
Rousmans S, Robin O, Dittmar A et al (2000) Autonomic nervous system responses associated with primary tastes. Chem Senses 25:709–718
He W, Boesveldt S, de Graaf C et al (2014) Dynamics of autonomic nervous system responses and facial expressions to odors. Front Psychol 5:110
Muroni P, Crnjar R, Tomassini Barbarossa I (2011) Emotional responses to pleasant and unpleasant Oral flavour stimuli. Chem Percept 4:65
Leterme A, Brun L, Dittmar A et al (2008) Autonomic nervous system responses to sweet taste: evidence for habituation rather than pleasure. Physiol Behav 93:994–999
Moranges M, Rouby C, Plantevit M et al (2021) Explicit and implicit measures of emotions: data-science might help to account for data complexity and heterogeneity. Food Qual Prefer 92:104181
Moranges M, Plantevit M, Bensafi M (2022) Using subgroup discovery to relate odor pleasantness and intensity to peripheral nervous system reactions. IEEE Trans Affect Comput:1. https://doi.org/10.1109/TAFFC.2022.3173403
Licon CC, Manesse C, Dantec M et al (2018) Pleasantness and trigeminal sensations as salient dimensions in organizing the semantic and physiological spaces of odors. Sci Rep 8:8444
Pedregosa F, Varoquaux G, Gramfort A et al (2011) Scikit-learn: machine learning in Python. J Mach Learn Res 12:2825–2830
Lemmerich F, Becker M (2019) Pysubgroup: easy-to-use subgroup discovery in Python. In: Brefeld U, Curry E, Daly E et al (eds) Machine learning and knowledge discovery in databases. Springer International Publishing, Cham, pp 658–662
Macqueen J (1967) Some methods for classification and analysis of multivariate observations. 5th Berkeley Symp. Math. Statist. Probability, pp 281–297
Wrobel S (1997) An algorithm for multi-relational discovery of subgroups. In: Komorowski J, Zytkow J (eds) Principles of data mining and knowledge discovery. Springer, Berlin, Heidelberg, pp 78–87
Bosc G, Golebiowski J, Bensafi M et al (2016) Local subgroup discovery for eliciting and understanding new structure-odor relationships. In: Calders T, Ceci M, Malerba D (eds) Discovery science. Springer International Publishing, Cham, pp 19–34
Moranges M, Plantevit M, Fournel A et al (2018) Exceptional attributed subgraph mining to understand the olfactory percept. In: Soldatova L, Vanschoren J, Papadopoulos G et al (eds) Discovery science. Springer International Publishing, Cham, pp 276–291
Licon CC, Bosc G, Sabri M et al (2019) Chemical features mining provides new descriptive structure-odor relationships. PLoS Comput Biol 15:e1006945
Lundberg SM, Lee S-I (2017) A unified approach to interpreting model predictions. In: Advances in neural information processing systems. Curran Associates, Inc. Long Beach, California, USA
Frank E, Hall M, Holmes G et al (2010) Weka-a machine learning workbench for data mining. In: Maimon O, Rokach L (eds) Data mining and knowledge discovery handbook. Springer US, Boston, pp 1269–1277
Schubert E, Zimek A (2019) ELKI: a large open-source library for data analysis – ELKI Release 0.7.5 “Heidelberg,” http://arxiv.org/abs/1902.03616
Berthold MR, Cebron N, Dill F et al (2009) KNIME – the Konstanz information miner: version 2.0 and beyond. SIGKDD Explor Newsl 11:26–31
Demsˇar J, Curk T, Erjavec A et al (2013) Orange: data mining toolbox in Python. 5. J Mach Learn Res 14:2349–2353
Meeng M, Knobbe A (2011) Flexible enrichment with cortana – software demo. 3
Atzmueller M, Lemmerich F (2012) VIKAMINE – open-source subgroup discovery, pattern mining, and analytics. In: Flach PA, De Bie T, Cristianini N (eds) Machine learning and knowledge discovery in databases. Springer, Berlin, Heidelberg, pp 842–845
Author information
Authors and Affiliations
Editor information
Editors and Affiliations
Rights and permissions
Copyright information
© 2023 The Author(s), under exclusive license to Springer Science+Business Media, LLC, part of Springer Nature
About this chapter
Cite this chapter
Moranges, M., Plantevit, M., Bensafi, M. (2023). Peripheral Nervous System Responses to Food Stimuli: Analysis Using Data Science Approaches. In: Bensafi, M. (eds) Basic Protocols on Emotions, Senses, and Foods. Methods and Protocols in Food Science . Springer, New York, NY. https://doi.org/10.1007/978-1-0716-2934-5_18
Download citation
DOI: https://doi.org/10.1007/978-1-0716-2934-5_18
Published:
Publisher Name: Springer, New York, NY
Print ISBN: 978-1-0716-2933-8
Online ISBN: 978-1-0716-2934-5
eBook Packages: Springer Protocols