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Nature Inspired Engineering: Biomimetic Sensors (2nd Edition)
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Nature Inspired Engineering: Biomimetic Sensors (2nd Edition)

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

Deadline for manuscript submissions: 20 September 2026 | Viewed by 5535

Special Issue Editors

Dr. Xiao Wu

E-Mail Website
Guest Editor
Department of Information Electronics, Faculty of Engineering, Fukuoka Institute of Technology, 3-30-1 Wajiro-Higashi, Higashi-ku, Fukuoka 811-0295, Japan
Interests: electronic tongue; electronic nose; gas sensors; multi-array sensors; food analysis; data processing; artificial intelligence
Special Issues, Collections and Topics in MDPI journals
Prof. Dr. Kiyoshi Toko

E-Mail Website
Guest Editor
Division of Taste Sensor, Research and Development Center for Five-Sense Devices, Kyushu University, Fukuoka 819-0395, Japan
Interests: taste sensors; electronic tongues; electronic noses; biosensors
Special Issues, Collections and Topics in MDPI journals

Special Issue Information

Dear Colleagues, 

Nature provides a huge source of inspiration for sensor design. Among them, electronic tongue and electronic nose are analytical devices based on a series of partially selective chemical sensors or biosensors and multivariate data processing tools. Since their design concepts are inspired by biological sensing systems, they are called biomimetic sensors. Over the past three decades, these sensors have been employed in a wide range of applications, including the classification of samples by purpose, taste quantification, and flavor assessment. Biomimetic sensors simulate the human perception system, detect various external stimuli, and surpass the level of human senses in terms of sensitivity, selectivity, and accuracy, helping people to understand the unknown world and facilitating daily life.

In this Special Issue, we will focus on the latest research on biomimetic sensors, from basic theory to application. We welcome both review articles and original research papers on, though not limited to, the following areas:

  • Biomimetic sensing materials;
  • Bioinspired sensors;
  • Electronic tongue;
  • Bioelectronic tongue;
  • Electronic nose;
  • Taste sensor;
  • Odor-sensing arrays;
  • Olfaction proteins;
  • Cell sensors;
  • Biomedical sensors;
  • Data analysis;
  • MEMS;
  • Environmental analysis;
  • Biomedical applications;
  • Gas Sensors;
  • Multi-Array Sensors;
  • Machine Learning;
  • AI (Artificial Intelligence);
  • Food Sensors;
  • Image Sensors;
  • Wearable Sensors.

Dr. Xiao Wu
Prof. Dr. Kiyoshi Toko
Guest Editors

Manuscript Submission Information

Manuscripts should be submitted online at www.mdpi.com by registering and logging in to this website. Once you are registered, click here to go to the submission form. Manuscripts can be submitted until the deadline. All submissions that pass pre-check are peer-reviewed. Accepted papers will be published continuously in the journal (as soon as accepted) and will be listed together on the special issue website. Research articles, review articles as well as short communications are invited. For planned papers, a title and short abstract (about 250 words) can be sent to the Editorial Office for assessment.

Submitted manuscripts should not have been published previously, nor be under consideration for publication elsewhere (except conference proceedings papers). All manuscripts are thoroughly refereed through a single-blind peer-review process. A guide for authors and other relevant information for submission of manuscripts is available on the Instructions for Authors page. Sensors is an international peer-reviewed open access semimonthly journal published by MDPI.

Please visit the Instructions for Authors page before submitting a manuscript. The Article Processing Charge (APC) for publication in this open access journal is 2600 CHF (Swiss Francs). Submitted papers should be well formatted and use good English. Authors may use MDPI's English editing service prior to publication or during author revisions.

Keywords

  • biomimetic sensors/sensing
  • bioinspired sensors/sensing
  • artificial intelligence (AI)

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Related Special Issue

Published Papers (3 papers)

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Research

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17 pages, 3806 KB  
Article
Multivariate Gas Sensor E-Nose System with PARAFAC and Machine Learning Modeling for Quantifying and Classifying the Impact of Fishing Gears
by Vinie Lee Silva-Alvarado and Jaime Lloret
Sensors 2026, 26(1), 6; https://doi.org/10.3390/s26010006 - 19 Dec 2025
Viewed by 465
Abstract
The quality of seafood is intrinsically linked to the accumulated history of stress, feeding, handling, and physical damage imposed by the fishing gear employed. This study proposes an innovative methodology using an E-nose sensor. The study species was Sparus aurata. Eight fishing [...] Read more.
The quality of seafood is intrinsically linked to the accumulated history of stress, feeding, handling, and physical damage imposed by the fishing gear employed. This study proposes an innovative methodology using an E-nose sensor. The study species was Sparus aurata. Eight fishing gears were studied. The methodology integrates Parallel Factor Analysis (PARAFAC) for impact quantification and Machine Learning (ML) for classifying the fishing gear of origin. Longline was established as the method with the lowest deviation. The impact hierarchy, from highest to lowest deviation, is as follows: Aquaculture 50.61% (95% CI: 34%, 68%), Purse seine 37.92% (95% CI: 22%, 54%), Trawl 35.92% (95% CI: 21%, 51%), Gillnet (three panels) 27.69% (95% CI: 14%, 41%), Gillnet (single panel) 24.63% (95% CI: 9%, 40%), Gillnet (two panels) 18.12% (95% CI: 4%, 31%) and Hook and line 1.36% (95% CI: −10%, 13%). For the classification task, 33 ML models were evaluated. Subspace KNN model yielded the best results with an accuracy of 97.14% in the validation and 98.08% in the testing, using 35 variables. Using 10, 15, 20, 25, and 30 variables, an accuracy higher than 85% was achieved. These results demonstrate the high precision in fish traceability by exploiting the sensor response profile left by each fishing gear. Full article
(This article belongs to the Special Issue Nature Inspired Engineering: Biomimetic Sensors (2nd Edition))
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22 pages, 4258 KB  
Article
A Few-Shot SE-Relation Net-Based Electronic Nose for Discriminating COPD
by Zhuoheng Xie, Yao Tian and Pengfei Jia
Sensors 2025, 25(15), 4780; https://doi.org/10.3390/s25154780 - 3 Aug 2025
Cited by 1 | Viewed by 959
Abstract
We propose an advanced electronic nose based on SE-RelationNet for COPD diagnosis with limited breath samples. The model integrates residual blocks, BiGRU layers, and squeeze–excitation attention mechanisms to enhance feature-extraction efficiency. Experimental results demonstrate exceptional performance with minimal samples: in 4-way 1-shot tasks, [...] Read more.
We propose an advanced electronic nose based on SE-RelationNet for COPD diagnosis with limited breath samples. The model integrates residual blocks, BiGRU layers, and squeeze–excitation attention mechanisms to enhance feature-extraction efficiency. Experimental results demonstrate exceptional performance with minimal samples: in 4-way 1-shot tasks, the model achieves 85.8% mean accuracy (F1-score = 0.852), scaling to 93.3% accuracy (F1-score = 0.931) with four samples per class. Ablation studies confirm that the 5-layer residual structure and single-hidden-layer BiGRU optimize stability (h_F1-score ≤ 0.011). Compared to SiameseNet and ProtoNet, SE-RelationNet shows superior accuracy (>15% improvement in 1-shot tasks). This technology enables COPD detection with as few as one breath sample, facilitating early intervention to mitigate lung cancer risks in COPD patients. Full article
(This article belongs to the Special Issue Nature Inspired Engineering: Biomimetic Sensors (2nd Edition))
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Review

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37 pages, 2180 KB  
Review
Recent Advances and Unaddressed Challenges in Biomimetic Olfactory- and Taste-Based Biosensors: Moving Towards Integrated, AI-Powered, and Market-Ready Sensing Systems
by Zunaira Khalid, Yuqi Chen, Xinyi Liu, Beenish Noureen, Yating Chen, Miaomiao Wang, Yao Ma, Liping Du and Chunsheng Wu
Sensors 2025, 25(22), 7000; https://doi.org/10.3390/s25227000 - 16 Nov 2025
Viewed by 1665
Abstract
Biomimetic olfactory and taste biosensors replicate human sensory functions by coupling selective biological recognition elements (such as receptors, binding proteins, or synthetic mimics) with highly sensitive transducers (including electrochemical, transistor, optical, and mechanical types). This review summarizes recent progress in olfactory and taste [...] Read more.
Biomimetic olfactory and taste biosensors replicate human sensory functions by coupling selective biological recognition elements (such as receptors, binding proteins, or synthetic mimics) with highly sensitive transducers (including electrochemical, transistor, optical, and mechanical types). This review summarizes recent progress in olfactory and taste biosensors focusing on three key areas: (i) materials and device design, (ii) artificial intelligence (AI) and data fusion for real-time decision-making, and (iii) pathways for practical application, including hybrid platforms, Internet of Things (IoT) connectivity, and regulatory considerations. We provide a comparative analysis of smell and taste sensing methods, emphasizing cases where integrating both modalities enhances sensitivity, selectivity, detection limits, and reliability in complex environments like food, environmental monitoring, healthcare, and security. Ongoing challenges are addressed with emerging solutions such as antifouling/self-healing interfaces, modular cartridges, machine learning (ML)-assisted calibration, and manufacturing-friendly approaches using scalable microfabrication and sustainable materials. The review concludes with a practical roadmap advocating for the joint development of receptors, materials, and algorithms; establishment of open standards for long-term stability; implementation of explainable/edge AI with privacy-focused analytics; and proactive collaboration with regulatory bodies. Collectively, these strategies aim to advance biomimetic smell and taste biosensors from experimental prototypes to dependable, commercially viable tools for continuous chemical sensing in real-world applications. Full article
(This article belongs to the Special Issue Nature Inspired Engineering: Biomimetic Sensors (2nd Edition))
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