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Electronic Nose and Artificial Olfaction
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Electronic Nose and Artificial Olfaction

A special issue of Sensors (ISSN 1424-8220). This special issue belongs to the section "Chemical Sensors".

Deadline for manuscript submissions: 15 June 2025 | Viewed by 3669

Special Issue Editors

Prof. Dr. Jesus Lozano

E-Mail Website
Guest Editor
Industrial Engineering School, University of Extremadura, 06006 Badajoz, Spain
Interests: gas sensors; patter recognition; machine olfaction; electronic nose; intelligent control
Special Issues, Collections and Topics in MDPI journals
Dr. Antonio Ruiz-Canales

E-Mail Website
Guest Editor
Center for Agri-Food and Agro-Environmental Research and Innovation (CIAGRO), Miguel Hernández University of Elche, 03312 Orihuela, Spain
Interests: electronic nose; sensors for agrifood applications, disease detection using electronic nose, sensor platform
Special Issues, Collections and Topics in MDPI journals
Dr. Patricia Arroyo

E-Mail Website
Guest Editor
Industrial Engineering School, University of Extremadura, 06006 Badajoz, Spain
Interests: gas sensors; materials; pattern recognition; machine olfaction; electronic nose

Special Issue Information

Dear Colleagues,

The olfactory system, a complex and intricate sensory mechanism, plays a pivotal role in our daily lives, influencing our perceptions, behaviors, and well-being. Over the years, the development of Electronic Nose (eNose) technology has been driven by the aspiration to replicate and enhance the olfactory capabilities of humans for various applications spanning industries. It is based on the use of gas sensors combined with pattern recognition methods. Both topics have made great advances in recent years and are worth reviewing in this Special Issue. Chemical sensors have improved their metrological parameters such as the limit of detection, the linearity of the response signal, sensitivity, selectivity, size, consumption, response time and repeatability. The second involved the development of advanced embedded or remote signal and data analysis techniques, including big data and cloud computing.

Prof. Dr. Jesus Lozano
Dr. Antonio Ruiz-Canales
Dr. Patricia Arroyo
Guest Editors

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Keywords

  • sensor development, novel materials and integration for eNose systems
  • signal processing techniques for odor recognition and classification
  • machine learning and artificial intelligence approaches in olfactory data analysis
  • miniaturization and wearable eNose devices
  • applications of eNose in food quality control, environmental monitoring, healthcare and beyond
  • human–machine interaction and olfactory interfaces
  • challenges and future directions in the field of artificial olfaction

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Published Papers (1 paper)

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Research

14 pages, 3226 KiB  
Article
Identification of Beef Odors under Different Storage Day and Processing Temperature Conditions Using an Odor Sensing System
by Yuanchang Liu, Nan Peng, Jinlong Kang, Takeshi Onodera and Rui Yatabe
Sensors 2024, 24(17), 5590; https://doi.org/10.3390/s24175590 - 29 Aug 2024
Viewed by 3306
Abstract
This study used an odor sensing system with a 16-channel electrochemical sensor array to measure beef odors, aiming to distinguish odors under different storage days and processing temperatures for quality monitoring. Six storage days ranged from purchase (D0) to eight days (D8), with [...] Read more.
This study used an odor sensing system with a 16-channel electrochemical sensor array to measure beef odors, aiming to distinguish odors under different storage days and processing temperatures for quality monitoring. Six storage days ranged from purchase (D0) to eight days (D8), with three temperature conditions: no heat (RT), boiling (100 °C), and frying (180 °C). Gas chromatography–mass spectrometry (GC-MS) analysis showed that odorants in the beef varied under different conditions. Compounds like acetoin and 1-hexanol changed significantly with the storage days, while pyrazines and furans were more detectable at higher temperatures. The odor sensing system data were visualized using principal component analysis (PCA) and uniform manifold approximation and projection (UMAP). PCA and unsupervised UMAP clustered beef odors by storage days but struggled with the processing temperatures. Supervised UMAP accurately clustered different temperatures and dates. Machine learning analysis using six classifiers, including support vector machine, achieved 57% accuracy for PCA-reduced data, while unsupervised UMAP reached 49.1% accuracy. Supervised UMAP significantly enhanced the classification accuracy, achieving over 99.5% with the dimensionality reduced to three or above. Results suggest that the odor sensing system can sufficiently enhance non-destructive beef quality and safety monitoring. This research advances electronic nose applications and explores data downscaling techniques, providing valuable insights for future studies. Full article
(This article belongs to the Special Issue Electronic Nose and Artificial Olfaction)
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