AI-driven 5G IoT e-nose for whiskey classification

Abstract

The main contribution is the design, implementation and validation of a complete AI-driven electronic nose architecture to perform the classification of whiskey and acetones. This classification is of paramount important in the distillery production line of whiskey in order to predict the quality of the final product. In this work, we investigate the application of an e-nose (based on arrays of single-walled carbon nanotubes) to the distinction of two different substances, such as whiskey and acetone (as a subproduct of the distillation process), and discrimination of three different types of the same substance, such as three types of whiskies. We investigated different strategies to classify the odor data and provided a suitable approach based on random forest with accuracy of 99% and with inference times under 1.8 seconds. In the case of clearly different substances, as subproducts of the whiskey distillation process, the procedure presented achieves a high accuracy in the classification process, with an accuracy around 96%.

Publication DOI: https://doi.org/10.1007/s10489-025-06425-1
Divisions: College of Engineering & Physical Sciences
College of Engineering & Physical Sciences > School of Computer Science and Digital Technologies
Aston University (General)
Funding Information: This research was funded by UKRI EPSRC EP/Y028813/1 “National Edge AI Hub for Real Data: Edge Intelligence for Cyberdisturbances and Data Quality.” Also, thank to the Generalitat Valenciana for the grant CIBEST/2023/101 and the grant CIAEST/2022/91. It ha
Additional Information: Copyright © The Author(s) 2025. This article is licensed under a Creative Commons Attribution 4.0 International License, which permits use, sharing, adaptation, distribution and reproduction in any medium or format, as long as you give appropriate credit to the original author(s) and the source, provide a link to the Creative Commons licence, and indicate if changes were made. The images or other third party material in this article are included in the article’s Creative Commons licence, unless indicated otherwise in a credit line to the material. If material is not included in the article’s Creative Commons licence and your intended use is not permitted by statutory regulation or exceeds the permitted use, you will need to obtain permission directly from the copyright holder. To view a copy of this licence, visit https://creativecommons.org/licenses/by/4.0/
Uncontrolled Keywords: 5G IoT,e-nose,PCA,ML,Odor discrimintation
Publication ISSN: 1573-7497
Last Modified: 01 May 2025 16:01
Date Deposited: 24 Apr 2025 17:01
Full Text Link:
Related URLs: https://link.sp ... 489-025-06425-1 (Publisher URL)
http://www.scop ... tnerID=8YFLogxK (Scopus URL)
PURE Output Type: Article
Published Date: 2025-04-24
Accepted Date: 2025-03-01
Authors: Segura-Garcia, Jaume
Fayos-Jordan, Rafael
Alselek, Mohammad
Maicas, Sergi
Arevalillo-Herraez, Miguel
Navarro-Camba, Enrique A.
Alcaraz Calero, Jose M. (ORCID Profile 0000-0002-2654-7595)

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