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Applications of Electronic Nose (E-Nose) and Electronic Tongue (E-Tongue) in Food Quality

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A special issue of Chemosensors (ISSN 2227-9040). This special issue belongs to the section "Applied Chemical Sensors".

Deadline for manuscript submissions: 15 February 2026 | Viewed by 14638

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Dr. Celia García-Hernández

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BioEcoUVa, Universidad de Valladolid, 47002 Valladolid, Spain
Interests: fabrication of electrochemical sensors and biosensors inspired in nanomaterials; (bio)electronic tongues applied in food analysis; thin films and nanotechnology: langmuir, layer-by-layer, spincoating; electrodeposition of coatings; corrosion and mechanical properties of materials of industrial interest

Special Issue Information

Dear Colleagues,

The concepts of electronic tongues (e-tongues) and electronic noses (e-noses) have developed rapidly in recent years due to their vast potential. They are based on electrochemical sensors combined with multivariate data analysis. The development of new analytical methods to characterize food is of vital importance for improving current quality and safety control systems. E-tongues and e-noses are holistic systems that provide global and qualitative information about samples. However, if the data matrix obtained by such multisensor systems is analyzed with adequate chemometric processing tools, descriptive or predictive information about specific parameters can be also extracted. Moreover, biosensors have been successfully implemented in these systems to develop bioelectronic devices. The electrochemical sensors used in these systems must incorporate appropriate electroactive and/or sensing materials that can interact with compounds of interest in the food industry. Some candidates for this task include conducting polymers, metal nanoparticles, metal oxide nanoparticles, porphyrins, phthalocyanines, and/or enzymes. In this context, nanotechnology can play an important role in manufacturing nanostructured sensors through various surface modification techniques.

This Special Issue focuses on recent research activities in the field of electronic tongues and noses for food analysis. Authors are encouraged to submit suitable articles/reviews addressing innovations in the field of electrochemical sensors/biosensors; novel electronic devices for food quality control; lab-on-chip devices; microsystems for food analysis; new electrocatalytic materials for sensing units; advanced fabrication processes based on nanotechnology; and in situ systems for food quality control, among other applications in foodstuff analysis.

Dr. Celia García-Hernández
Guest Editor

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Keywords

electronic tongues
electronic noses
food analysis
food quality and safety
electrochemical sensors
electrochemical biosensors
nanostructured sensors for food analysis

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Published Papers (9 papers)

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Research

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16 pages, 1719 KiB

Open AccessArticle

Geographical Origin Classification of Oolong Tea Using an Electronic Nose: Application of Machine Learning and Gray Relational Analysis

by Sushant Kaushal, Priya Rana, Chao-Chin Chung and Ho-Hsien Chen

Chemosensors 2025, 13(8), 295; https://doi.org/10.3390/chemosensors13080295 - 8 Aug 2025

Viewed by 249

Abstract

Taiwan accounts for 90% of the total oolong tea production and enjoys a good global reputation for its quality. In recent years, oolong tea from neighboring countries has been imported into Taiwan and sold as Taiwanese oolong at high prices. This study aimed to rapidly classify oolong tea from four geographical origins (Taiwan, Vietnam, China, and Indonesia) using an electronic nose (E-nose) combined with machine learning. Color measurements were also conducted to support the classification. The electronic nose (E-nose) was utilized to analyze the aroma profiles of tea samples. To classify the samples, five machine learning models—linear discriminant analysis (LDA), support vector machine (SVM), K-nearest neighbor (KNN), artificial neural network (ANN), and random forest (RF)—were developed using 70% of the dataset for training and tested on the remaining 30%. Gray relational analysis (GRA) was applied to measure the relationship between sensor responses and reference tea origins. Multivariate analysis of variance (MANOVA) indicated a statistically significant effect of tea origin on color parameters, as confirmed by both Pillai’s trace and Wilks’ Lambda (Λ) tests (p = 0.000 < 0.05). Among the tested models, LDA and ANN achieved the highest overall classification accuracy (98.33%), with ANN outperforming in the discrimination of Taiwanese oolong tea, achieving 98.89% accuracy. GRA presented higher gray relational grade (GRG) values for Taiwanese tea samples compared to other origins and identified sensors S4, S6, and S14 as the dominant contributors. In conclusion, the E-nose combined with machine learning provides a rapid, non-destructive, and effective approach for geographical origin classification of oolong tea. Full article

(This article belongs to the Special Issue Applications of Electronic Nose (E-Nose) and Electronic Tongue (E-Tongue) in Food Quality)

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25 pages, 6467 KiB

Open AccessArticle

Integrating Sensor Data, Laboratory Analysis, and Computer Vision in Machine Learning-Driven E-Nose Systems for Predicting Tomato Shelf Life

by Julia Marie Senge, Florian Kaltenecker and Christian Krupitzer

Chemosensors 2025, 13(7), 255; https://doi.org/10.3390/chemosensors13070255 - 12 Jul 2025

Viewed by 454

Abstract

Assessing the quality of fresh produce is essential to ensure a safe and satisfactory product. Methods to monitor the quality of fresh produce exist; however, they are often expensive, time-consuming, and sometimes require the destruction of the sample. Electronic Nose (E-Nose) technology has been established to track the ripeness, spoilage, and quality of fresh produce. Our study developed a freshness monitoring system for tomatoes, combining E-Nose technology with storage condition monitoring, color analysis, and weight-loss tracking. Different post-purchase scenarios were investigated, focusing on the influence of temperature and mechanical damage on shelf life. Support Vector Classifier (SVC) and k-Nearest Neighbor (kNN) were applied to classify storage scenarios and storage days, while Support Vector Regression (SVR) and kNN regression were used for predicting storage days. By using a data fusion approach with Linear Discriminant Analysis (LDA), the SVC achieved an accuracy of 72.91% in predicting storage days and an accuracy of 86.73% in distinguishing between storage scenarios. The kNN yielded the best regression results, with a Mean Absolute Error (MAE) of 0.841 days and a coefficient of determination of 0.867. The results highlight the method’s potential to predict storage scenarios and storage days, providing insight into the product’s remaining shelf life. Full article

(This article belongs to the Special Issue Applications of Electronic Nose (E-Nose) and Electronic Tongue (E-Tongue) in Food Quality)

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18 pages, 3916 KiB

Open AccessArticle

TinyML-Based Real-Time Drift Compensation for Gas Sensors Using Spectral–Temporal Neural Networks

by Adir Krayden, M. Avraham, H. Ashkar, T. Blank, S. Stolyarova and Yael Nemirovsky

Chemosensors 2025, 13(7), 223; https://doi.org/10.3390/chemosensors13070223 - 20 Jun 2025

Viewed by 927

Abstract

The implementation of low-cost sensitive and selective gas sensors for monitoring fruit ripening and quality strongly depends on their long-term stability. Gas sensor drift undermines the long-term reliability of low-cost sensing platforms, particularly in precision agriculture. We present a real-time drift compensation framework based on a lightweight Temporal Convolutional Neural Network (TCNN) combined with a Hadamard spectral transform. The model operates causally on incoming sensor data, achieving a mean absolute error below 1 mV on long-term recordings (equivalent to <1 particle per million (ppm) gas concentration). Through quantization, we compress the model by over 70%, without sacrificing accuracy. Demonstrated on a combustion-type gas sensor system (dubbed GMOS) for ethylene monitoring, our approach enables continuous, drift-corrected operation without the need for recalibration or dependence on cloud-based services, offering a generalizable solution for embedded environmental sensing—in food transportation containers, cold storage facilities, de-greening rooms and directly in the field. Full article

(This article belongs to the Special Issue Applications of Electronic Nose (E-Nose) and Electronic Tongue (E-Tongue) in Food Quality)

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18 pages, 4392 KiB

Open AccessArticle

Trimethylamine Gas Sensor Based on Electrospun In₂O₃ Nanowires with Different Grain Sizes for Fish Freshness Monitoring

by Xiangrui Dong, Bo Zhang, Mengyao Shen, Qi Lu, Hao Shen, Yi Ni, Yuechen Liu and Haitao Song

Chemosensors 2025, 13(6), 218; https://doi.org/10.3390/chemosensors13060218 - 14 Jun 2025

Viewed by 3101

Abstract

Seafood, especially marine fish, is highly prone to spoilage during processing, transportation, and storage. It releases pungent trimethylamine (TMA) gas, which severely affects food quality and safety. Metal–oxide–semiconductor (MOS) gas sensors for TMA detection offer a rapid, convenient, and accurate method for assessing fish freshness. Indium oxide (In₂O₃) has shown potential as an effective sensing material for the detection of TMA. In this work, one-dimensional In₂O₃ nanowires with different grain sizes and levels of crystallinity were synthetized using the electrospinning technique and underwent different thermal calcination processes. Gas-sensing tests showed that the In₂O₃–3 °C/min–500 °C gas sensor exhibited an outstanding performance, including a high response (R_a/R_g = 47.0) to 100 ppm TMA, a short response time (6 s), a low limit of detection (LOD, 0.0392 ppm), and an excellent long-term stability. Furthermore, the sensor showed promising experimental results in monitoring the freshness of Larimichthys crocea (L. crocea). By analyzing the relationship between the grain size and crystallinity of the In₂O₃ samples, a mechanism for the enhanced gas-sensing performance was proposed. This work provides a novel strategy for designing and fabricating gas sensors for TMA detection and highlights their potential for broad applications in real-time fish freshness monitoring. Full article

(This article belongs to the Special Issue Applications of Electronic Nose (E-Nose) and Electronic Tongue (E-Tongue) in Food Quality)

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14 pages, 4334 KiB

Open AccessArticle

Study on the Geographic Traceability and Growth Age of Panax ginseng C. A. Meyer Base on an Electronic Nose and Fourier Infrared Spectroscopy

by Jinying Li, Jianlei Qiao, Chang Liu, Zhigang Zhou, Cheng Kong, Zhiyong Chang, Xiaohui Weng and Shujun Zhang

Chemosensors 2025, 13(5), 176; https://doi.org/10.3390/chemosensors13050176 - 10 May 2025

Cited by 1 | Viewed by 718

Abstract

During ginseng selection, marketing promotion, and sales, it is imperative to expeditiously differentiate the overall quality grades, identify the geographic traces and determine the growth ages. This facilitates the selection of the most appropriate quality grade for each product, thereby ensuring the most efficacious marketing strategy. In this study, a new method is proposed and developed for the classification of ginsengs with diverse geographical traceability and with various growth ages by combining an electronic nose (E-nose) system and machine learning with Fourier-transform infrared spectroscopy (FTIR) as a calibration technology. An investigation has been carried out to discover the differences in the secondary metabolites and odor of three types of ginseng with different geographic traceability and three growth ages of ginseng from the same geographic traceability site. In the proposed method, five types of ginseng samples have been successfully tested. The optimal Mean-SVM model combined with an E-nose system classified ginseng samples with different geographic traceability and different growth years with accuracies of 100% and 82% in the training and test sets, respectively. These results have significant implications for ginseng’s geographic traceability, growth age determination, and overall quality control. It is believed that the future implementation of the proposed method would significantly protect the health and economic interests of consumers as well as promoting the use of an E-nose in the market surveillance of consumable products such as ginseng and other foods. Full article

(This article belongs to the Special Issue Applications of Electronic Nose (E-Nose) and Electronic Tongue (E-Tongue) in Food Quality)

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Review

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31 pages, 952 KiB

Open AccessReview

Electronic Tongue Technology Applied to the Analysis of Grapes and Wines: A Comprehensive Review from Its Origins

by Celia Garcia-Hernandez, Cristina Garcia-Cabezon, Maria Luz Rodriguez-Mendez and Fernando Martin-Pedrosa

Chemosensors 2025, 13(5), 188; https://doi.org/10.3390/chemosensors13050188 - 17 May 2025

Cited by 1 | Viewed by 1698

Abstract

The electronic tongue (ET) and bioelectronic tongue (bioET) technologies have emerged as innovative and promising tools for the characterization and quality control of complex liquid matrices such as grape musts and wines. These multisensor systems, based on electrochemical detection and chemometric analysis, provide global and rapid information about taste-related attributes, antioxidant content, and other critical parameters, offering an alternative or complement to traditional analytical methods. This review explores the principles, development, and applications of ET and bioET in the wine industry, highlighting their capacity to assess grape ripeness, monitor fermentation, determine wine aging, detect adulterations, and support geographical and varietal authentication. Special attention is paid to advances in sensing materials—such as conducting polymers, metal nanoparticles, and enzymes—and the construction techniques of sensors and biosensors, which have improved ET performance. Finally, the potential of these technologies as cost-effective, portable, and on-site tools aligns with the demands of Industry 4.0 and next-generation smart agriculture and food production systems. Full article

(This article belongs to the Special Issue Applications of Electronic Nose (E-Nose) and Electronic Tongue (E-Tongue) in Food Quality)

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37 pages, 4556 KiB

Open AccessReview

Current Opportunities and Trends in the Gas Sensor Market: A Focus on e-Noses and Their Applications in Food Industry

by Selene Mor, Buse Gunay, Michele Zanotti, Michele Galvani, Stefania Pagliara and Luigi Sangaletti

Chemosensors 2025, 13(5), 181; https://doi.org/10.3390/chemosensors13050181 - 12 May 2025

Viewed by 1751

Abstract

Electronic noses (e-noses) are devices developed to recognize/classify odors and used in many fields, matching the current societal needs and concerns, such as food integrity and quality control, environmental monitoring, medical diagnostics, safety, and security in urban and industrial settlements. In this study, we review the application fields of e-noses based on a market analysis of currently available devices. A total of 44 companies active up to 2024, as well as 265 products, have been identified by considering the web pages of companies that feature e-noses among their products. These devices have been classified according to (i) the sensing mechanisms underlying the device performances and (ii) the application fields. The most diffused sensing devices/systems are chemiresistors (12.8%), electrochemical sensors (13.0%), catalytic beads (12.4%), and those based on optical detection techniques (16.0%). Commercial e-noses find large application in the industrial (21.0%) and chemical and petrochemical (21.0%) fields. A focus is made on the food and beverage application field, which is still a minor part of the overall share (6.0%) but is rapidly increasing and plays a relevant role in future applications where safety, sustainability, and quality issues are strictly intertwined. From this study, a rather complex picture emerges, and a proper taxonomy is expected to correctly classify the different kinds of e-noses. Full article

(This article belongs to the Special Issue Applications of Electronic Nose (E-Nose) and Electronic Tongue (E-Tongue) in Food Quality)

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23 pages, 3229 KiB

Open AccessReview

A Systematic Review of the Applications of Electronic Nose and Electronic Tongue in Food Quality Assessment and Safety

by Ramkumar Vanaraj, Bincy I.P, Gopiraman Mayakrishnan, Ick Soo Kim and Seong-Cheol Kim

Chemosensors 2025, 13(5), 161; https://doi.org/10.3390/chemosensors13050161 - 1 May 2025

Cited by 6 | Viewed by 3892

Abstract

Food quality assessment is a critical aspect of food production and safety, ensuring that products meet both regulatory and consumer standards. Traditional methods such as sensory evaluation, chromatography, and spectrophotometry are widely used but often suffer from limitations, including subjectivity, high costs, and time-consuming procedures. In recent years, the development of electronic nose (e-nose) and electronic tongue (e-tongue) technologies has provided rapid, objective, and reliable alternatives for food quality monitoring. These bio-inspired sensing systems mimic human olfactory and gustatory functions through sensor arrays and advanced data processing techniques, including artificial intelligence and pattern recognition algorithms. The e-nose is primarily used for detecting volatile organic compounds (VOCs) in food, making it effective for freshness evaluation, spoilage detection, aroma profiling, and adulteration identification. Meanwhile, the e-tongue analyzes liquid-phase components and is widely applied in taste assessment, beverage authentication, fermentation monitoring, and contaminant detection. Both technologies are extensively used in the quality control of dairy products, meat, seafood, fruits, beverages, and processed foods. Their ability to provide real-time, non-destructive, and high-throughput analysis makes them valuable tools in the food industry. This review explores the principles, advantages, and applications of e-nose and e-tongue systems in food quality assessment. Additionally, it discusses emerging trends, including IoT-based smart sensing, advances in nanotechnology, and AI-driven data analysis, which are expected to further enhance their efficiency and accuracy. With continuous innovation, these technologies are poised to revolutionize food safety and quality control, ensuring consumer satisfaction and compliance with global standards. Full article

(This article belongs to the Special Issue Applications of Electronic Nose (E-Nose) and Electronic Tongue (E-Tongue) in Food Quality)

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Other

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32 pages, 1555 KiB

Open AccessSystematic Review

A Systematic Review of the Use of Electronic Nose and Tongue Technologies for Detecting Food Contaminants

by Muhammad Zia Ul Haq, Baljit Singh, Xolile Fuku, Ahmed Barhoum and Furong Tian

Chemosensors 2025, 13(7), 262; https://doi.org/10.3390/chemosensors13070262 - 19 Jul 2025

Viewed by 455

Abstract

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. Full article

(This article belongs to the Special Issue Applications of Electronic Nose (E-Nose) and Electronic Tongue (E-Tongue) in Food Quality)

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