Predicting the colour associated with odours using an electronic nose
Abstract
Predicting olfactory perception with an electronic nose can aid in the design and evaluation of olfactory-based experiences. We investigate whether the human perception of odours can be predicted outside the bounds of perceived pleasantness and semantic descriptors. We tuned an electronic nose to predict an odour's colour in the CIELAB colour space using human judgements. This revealed that the crossmodal associations people have towards colours could be predicted. Our electronic nose system can predict an odour's colour with a 70 – 81% machine-human similarity rating. These findings suggest a systematic and predictable link exists between the chemical features of odours and the colour associated to them. These findings highlight the possibilities of predicting human olfactory perception using an electronic nose.
Keywords:
Electronic Nose, Odours, Odour Perception, Regression, Predicting Colours, CIELAB
References
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A. Fournel, C. Ferdenzi, C. Sezille, C. Rouby, and M. Bensafi. 2016. Multidimensional representation of odors in the human olfactory cortex. Hum. Brain Mapp. 37, 6 (2016), 2161–2172. DOI:https://doi.org/10.1002/hbm.23164
Rafi Haddad, Abebe Medhanie, Yehudah Roth, David Harel, and Noam Sobel. 2010. Predicting odor pleasantness with an electronic nose. PLoS Comput. Biol. 6, 4 (2010). DOI:https://doi.org/10.1371/journal.pcbi.1000740
Diclehan Karakaya, Oguzhan Ulucan, and Mehmet Turkan. 2020. Electronic Nose and Its Applications: A Survey. Int. J. Autom. Comput. 17, 2 (2020), 179–209. DOI:https://doi.org/10.1007/s11633-019-1212-9
Sarah E. Kemp and Avery N. Gilbert. 1997. Odor intensity and color lightness are correlated sensory dimensions. Am. J. Psychol. 110, 1 (1997), 35–46. DOI:https://doi.org/10.2307/1423699
F. Kermen, A. Chakirian, C. Sezille, P. Joussain, G. Le Goff, A. Ziessel, M. Chastrette, N. Mandairon, A. Didier, C. Rouby, and M. Bensafi. 2011. Molecular complexity determines the number of olfactory notes and the pleasantness of smells. Sci. Rep. 1, (2011), 1–6. DOI:https://doi.org/10.1038/srep00206
Rehan M. Khan, Chung Hay Luk, Adeen Flinker, Amit Aggarwal, Hadas Lapid, Rafi Haddad, and Noam Sobel. 2007. Predicting odor pleasantness from odorant structure: Pleasantness as a reflection of the physical world. J. Neurosci. 27, 37 (2007), 10015– 10023. DOI:https://doi.org/10.1523/JNEUROSCI.1158-07.2007
Wang Li, Hongying Liu, Dandan Xie, Zichun He, and Xititan Pi. 2017. Lung Cancer Screening Based on Type-different Sensor Arrays. Sci. Rep. 7, 1 (2017). DOI:https://doi.org/10.1038/s41598-017-02154-9
Ronnier Luo. 2016. Encyclopedia of color science and technology. Springer Publishing Company, Incorporated.
Charles Spence. 2011. Crossmodal correspondences: A tutorial review. Attention, Perception, Psychophys. 73, 4 (2011), 971–995. DOI:https://doi.org/10.3758/s13414-010-0073-7
Ryan J. Ward, Fred P.M. Jjunju, Elias J. Griffith, Sophie M. Wuerger, and Alan Marshall. 2020. Artificial Odour-Vision Syneasthesia via Olfactory Sensory Argumentation. IEEE Sens. J. 21, 5 (2020), 6784–6792. DOI:https://doi.org/10.1109/JSEN.2020.3040114
Ryan J. Ward, Sophie Wuerger, and Alan Marshall. 2020. Smelling sensations: olfactory crossmodal correspondences. bioRxiv (January 2020), 2020.04.15.042630. DOI:https://doi.org/10.1101/2020.04.15.042630
Danli Wu, Yu Cheng, Dehan Luo, Kin Yeung Wong, Kevin Hung, and Zhijing Yang. 2019. POP-CNN: Predicting odor’s pleasantness with convolutional neural network. IEEE Sensors 19, 23 (2019), 11337–11345.
Published
2021-06-21
How to Cite
WARD, Ryan; RAHMAN, Shammi; WUERGER, Sophie; MARSHALL, Alan.
Predicting the colour associated with odours using an electronic nose . In: WORKSHOP ON MULTISENSORY EXPERIENCES (SENSORYX), 1. , 2021, New York.
Anais [...].
Porto Alegre: Sociedade Brasileira de Computação,
2021
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DOI: https://doi.org/10.5753/sensoryx.2021.15683.
