Malodour classification with low-cost flexible electronics

Emre Ozer (), Jedrzej Kufel, John Biggs, Anjit Rana, Francisco J. Rodriguez, Thomas Lee-Clark, Antony Sou, Catherine Ramsdale, Scott White, Suresh Kumar Garlapati, Palaniappan Valliappan, Aiman Rahmanudin, Venuskrishnan Komanduri, Glenn Sunley Saez, Sankara Gollu, Gavin Brown, Piotr Dudek, Krishna C. Persaud, Michael L. Turner, Stephanie Murray, Susan Bates, Robert Treloar, Brian Newby and Jane Ford
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Emre Ozer: Pragmatic Semiconductor
Jedrzej Kufel: Pragmatic Semiconductor
John Biggs: Pragmatic Semiconductor
Anjit Rana: Pragmatic Semiconductor
Francisco J. Rodriguez: Pragmatic Semiconductor
Thomas Lee-Clark: Pragmatic Semiconductor
Antony Sou: Pragmatic Semiconductor
Catherine Ramsdale: Pragmatic Semiconductor
Scott White: Pragmatic Semiconductor
Suresh Kumar Garlapati: Indian Institute of Technology Hyderabad
Palaniappan Valliappan: University of Manchester, University of Manchester
Aiman Rahmanudin: University of Manchester, University of Manchester
Venuskrishnan Komanduri: University of Manchester, University of Manchester
Glenn Sunley Saez: University of Manchester, University of Manchester
Sankara Gollu: University of Manchester, University of Manchester
Gavin Brown: University of Manchester, University of Manchester
Piotr Dudek: University of Manchester, University of Manchester
Krishna C. Persaud: University of Manchester, University of Manchester
Michael L. Turner: University of Manchester, University of Manchester
Stephanie Murray: Unilever, Port Sunlight Lab
Susan Bates: Unilever, Port Sunlight Lab
Robert Treloar: Unilever, Port Sunlight Lab
Brian Newby: Unilever, Port Sunlight Lab
Jane Ford: Unilever, Port Sunlight Lab

Nature Communications, 2023, vol. 14, issue 1, 1-9

Abstract: Abstract Understanding body malodour in a measurable manner is essential for developing personal care products. Body malodour is the result of bodily secretion of a highly complex mixture of volatile organic compounds. Current body malodour measurement methods are manual, time consuming and costly, requiring an expert panel of assessors to assign a malodour score to each human test subject. This article proposes a technology-based solution to automate this task by developing a custom-designed malodour score classification system comprising an electronic nose sensor array, a sensor readout interface and a machine learning hardware fabricated on low-cost flexible substrates. The proposed flexible integrated smart system is to augment the expert panel by acting like a panel assessor but could ultimately replace the panel to reduce the test and measurement costs. We demonstrate that it can classify malodour scores as good as or even better than half of the assessors on the expert panel.

Date: 2023
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DOI: 10.1038/s41467-023-36104-z

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