A Systematic Review of the Use of Electronic Nose and Tongue Technologies for Detecting Food Contaminants
Sensor operations in the food industry are faced with several major challenges, including in sensitivity, selectivity, accuracy and rapid detection. Among emerging technologies, e-nose and e-tongue systems have attracted much attention from researchers. This review examines 112 studies published fro...
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| Main Authors: | , , , , |
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| Format: | Article |
| Language: | English |
| Published: |
MDPI AG
2025-07-01
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| Series: | Chemosensors |
| Subjects: | |
| Online Access: | https://www.mdpi.com/2227-9040/13/7/262 |
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| Summary: | Sensor operations in the food industry are faced with several major challenges, including in sensitivity, selectivity, accuracy and rapid detection. Among emerging technologies, e-nose and e-tongue systems have attracted much attention from researchers. This review examines 112 studies published from 2004 to 2025, and examines the functionalities and performance in detecting various food product-associated analytes. The sensitivity of e-nose and e-tongue systems was analyzed using various data processing techniques. Recent research and development in leading countries (i.e., China, United Kingdom, Columbia, India, Portugal, Spain, Hungary, Ireland) was examined. The findings indicate that principal component analysis (PCA) was the most widely used technique, while more articles were published in 2021. Worldwide research contributions showed China at the forefront of e-nose studies (26.7%) and Spain leading in e-tongue research (30%). The highest sensitivity values were 99.0% for the e-nose in 2015 and 100% for the e-tongue in 2012. In specific applications, the e-nose achieved a maximum average sensitivity of 15% in apple analysis, while the e-tongue achieved a maximum average sensitivity of 40.5% in water samples. Furthermore, the review presents an in-depth discussion of key parameters, including food sample types, citation rates, analysis techniques, accuracy, and sensitivity, with graphical representations for enhanced clarity. |
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| ISSN: | 2227-9040 |