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Chemosensors
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Artificial Intelligence (AI)/Machine Learning (ML)-Assisted Chemical Sensors
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Artificial Intelligence (AI)/Machine Learning (ML)-Assisted Chemical Sensors

A special issue of Chemosensors (ISSN 2227-9040). This special issue belongs to the section "Electrochemical Devices and Sensors".

Deadline for manuscript submissions: 15 December 2025 | Viewed by 9739

Special Issue Editors

Dr. Caizhi Liao

E-Mail Website
Guest Editor
Department of Engineering, University of Cambridge, Cambridge, UK
Interests: chemical sensors; bioactive materials; artificial intelligence (AI); biomedical technology
Dr. Yanyu Xiong

E-Mail Website
Guest Editor
Department of Electrical and Computer Engineering, University of Illinois Urbana-Champaign, Champaign, IL, USA
Interests: biosensors; nanophotonics; quantum dots; DNA origami; machine learning (ML); microscopy

Special Issue Information

Dear Colleagues,

The integration of Artificial Intelligence (AI) and Machine Learning (ML) techniques with chemical sensors has revolutionized the field of chemical sensing. This Special Issue explores the innovative applications and advancements in AI/ML-assisted chemical sensors. By harnessing the power of AI/ML algorithms, these sensors can achieve unparalleled levels of accuracy, sensitivity, and selectivity in detecting and identifying target molecules across various domains, including environmental monitoring, healthcare diagnostics, and industrial processes. This Special Issue highlights the fundamental principles and methodologies in AI/ML-assisted chemical sensing, including feature extraction, pattern recognition, and data fusion techniques. Furthermore, it discusses the benefits of employing AI/ML in enhancing sensor performance, such as real-time data analysis, adaptive learning, and predictive modelling. Through case studies and examples, this Special Issue demonstrates the transformative potential of AI/ML-assisted chemical sensors in addressing critical challenges and advancing scientific research and technological innovation in diverse applications.

Dr. Caizhi Liao
Dr. Yanyu Xiong
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 100 words) can be sent to the Editorial Office for announcement on this website.

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. Chemosensors is an international peer-reviewed open access monthly 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 2700 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

  • artificial intelligence (AI)
  • machine learning (ML)
  • chemical sensing
  • environmental monitoring
  • healthcare diagnostics
  • industrial processes
  • data processing
  • sensor performance

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Further information on MDPI's Special Issue policies can be found here.

Published Papers (4 papers)

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Research

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22 pages, 17549 KiB  
Article
Monitoring of Indoor Air Quality in a Classroom Combining a Low-Cost Sensor System and Machine Learning
by Ioannis D. Apostolopoulos, Eleni Dovrou, Silas Androulakis, Katerina Seitanidi, Maria P. Georgopoulou, Angeliki Matrali, Georgia Argyropoulou, Christos Kaltsonoudis, George Fouskas and Spyros N. Pandis
Chemosensors 2025, 13(4), 148; https://doi.org/10.3390/chemosensors13040148 - 18 Apr 2025
Viewed by 325
Abstract
Monitoring indoor air quality in schools is essential, particularly as children are highly vulnerable to air pollution. This study evaluates the performance of the low-cost sensor-based air quality monitoring system ENSENSIA, during a 3-week campaign in an elementary school classroom in Athens, Greece. [...] Read more.
Monitoring indoor air quality in schools is essential, particularly as children are highly vulnerable to air pollution. This study evaluates the performance of the low-cost sensor-based air quality monitoring system ENSENSIA, during a 3-week campaign in an elementary school classroom in Athens, Greece. The system measured PM2.5, CO, NO, NO2, O3, and CO2. High-end instrumentation provided the reference concentrations. The aim was to assess the sensors’ performance in estimating the average day-to-day exposure, capturing temporal variations and the degree of agreement among different sensor units, with particular attention to the impact of machine learning (ML) calibration. Using the factory calibration settings, the CO2 and PM2.5 sensors showed strong inter-unit consistency for hourly averaged values. The other sensors, however, exhibited inter-unit variability, with differences in the reported average day-to-day concentrations ranging from 20% to 160%. ML-based calibration was investigated for the CO, NO, NO2, and O3 sensors using measurements by reference instruments for training and evaluation. Among the eleven ML algorithms tested, the Support Vector Regression performed better for the calibration of the CO, NO2, and O3 sensors. The NO sensor was better calibrated using the Elastic Net algorithm. The inter-unit variability was reduced by a factor of two after the ML calibration. The daily average error compared to the reference measured was also reduced by approximately 15–50% depending upon the sensor. Full article
(This article belongs to the Special Issue Artificial Intelligence (AI)/Machine Learning (ML)-Assisted Chemical Sensors)
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14 pages, 2785 KiB  
Article
Machine Learning-Assisted 3D Flexible Organic Transistor for High-Accuracy Metabolites Analysis and Other Clinical Applications
by Caizhi Liao, Huaxing Wu and Luigi G. Occhipinti
Chemosensors 2024, 12(9), 174; https://doi.org/10.3390/chemosensors12090174 - 1 Sep 2024
Cited by 1 | Viewed by 1709
Abstract
The integration of advanced diagnostic technologies in healthcare is crucial for enhancing the accuracy and efficiency of disease detection and management. This paper presents an innovative approach combining machine learning-assisted 3D flexible fiber-based organic transistor (FOT) sensors for high-accuracy metabolite analysis and potential [...] Read more.
The integration of advanced diagnostic technologies in healthcare is crucial for enhancing the accuracy and efficiency of disease detection and management. This paper presents an innovative approach combining machine learning-assisted 3D flexible fiber-based organic transistor (FOT) sensors for high-accuracy metabolite analysis and potential diagnostic applications. Machine learning algorithms further enhance the analytical capabilities of FOT sensors by effectively processing complex data, identifying patterns, and predicting diagnostic outcomes with 100% high accuracy. We explore the fabrication and operational mechanisms of these transistors, the role of machine learning in metabolite analysis, and their potential clinical applications by analyzing practical human blood samples for hypernatremia syndrome. This synergy not only improves diagnostic precision but also holds potential for the development of personalized diagnostics, tailoring treatments for individual metabolic profiles. Full article
(This article belongs to the Special Issue Artificial Intelligence (AI)/Machine Learning (ML)-Assisted Chemical Sensors)
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Review

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54 pages, 7881 KiB  
Review
Spectroscopy-Based Methods and Supervised Machine Learning Applications for Milk Chemical Analysis in Dairy Ruminants
by Aikaterini-Artemis Agiomavriti, Maria P. Nikolopoulou, Thomas Bartzanas, Nikos Chorianopoulos, Konstantinos Demestichas and Athanasios I. Gelasakis
Chemosensors 2024, 12(12), 263; https://doi.org/10.3390/chemosensors12120263 - 13 Dec 2024
Viewed by 1560
Abstract
Milk analysis is critical to determine its intrinsic quality, as well as its nutritional and economic value. Currently, the advancements and utilization of spectroscopy-based techniques combined with machine learning algorithms have made the development of analytical tools and real-time monitoring and prediction systems [...] Read more.
Milk analysis is critical to determine its intrinsic quality, as well as its nutritional and economic value. Currently, the advancements and utilization of spectroscopy-based techniques combined with machine learning algorithms have made the development of analytical tools and real-time monitoring and prediction systems in the dairy ruminant sector feasible. The objectives of the current review were (i) to describe the most widely applied spectroscopy-based and supervised machine learning methods utilized for the evaluation of milk components, origin, technological properties, adulterants, and drug residues, (ii) to present and compare the performance and adaptability of these methods and their most efficient combinations, providing insights into the strengths, weaknesses, opportunities, and challenges of the most promising ones regarding the capacity to be applied in milk quality monitoring systems both at the point-of-care and beyond, and (iii) to discuss their applicability and future perspectives for the integration of these methods in milk data analysis and decision support systems across the milk value-chain. Full article
(This article belongs to the Special Issue Artificial Intelligence (AI)/Machine Learning (ML)-Assisted Chemical Sensors)
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37 pages, 22751 KiB  
Review
Machine Learning-Assisted Raman Spectroscopy and SERS for Bacterial Pathogen Detection: Clinical, Food Safety, and Environmental Applications
by Md Hasan-Ur Rahman, Rabbi Sikder, Manoj Tripathi, Mahzuzah Zahan, Tao Ye, Etienne Gnimpieba Z., Bharat K. Jasthi, Alan B. Dalton and Venkataramana Gadhamshetty
Chemosensors 2024, 12(7), 140; https://doi.org/10.3390/chemosensors12070140 - 15 Jul 2024
Cited by 11 | Viewed by 5400
Abstract
Detecting pathogenic bacteria and their phenotypes including microbial resistance is crucial for preventing infection, ensuring food safety, and promoting environmental protection. Raman spectroscopy offers rapid, seamless, and label-free identification, rendering it superior to gold-standard detection techniques such as culture-based assays and polymerase chain [...] Read more.
Detecting pathogenic bacteria and their phenotypes including microbial resistance is crucial for preventing infection, ensuring food safety, and promoting environmental protection. Raman spectroscopy offers rapid, seamless, and label-free identification, rendering it superior to gold-standard detection techniques such as culture-based assays and polymerase chain reactions. However, its practical adoption is hindered by issues related to weak signals, complex spectra, limited datasets, and a lack of adaptability for detection and characterization of bacterial pathogens. This review focuses on addressing these issues with recent Raman spectroscopy breakthroughs enabled by machine learning (ML), particularly deep learning methods. Given the regulatory requirements, consumer demand for safe food products, and growing awareness of risks with environmental pathogens, this study emphasizes addressing pathogen detection in clinical, food safety, and environmental settings. Here, we highlight the use of convolutional neural networks for analyzing complex clinical data and surface enhanced Raman spectroscopy for sensitizing early and rapid detection of pathogens and analyzing food safety and potential environmental risks. Deep learning methods can tackle issues with the lack of adequate Raman datasets and adaptability across diverse bacterial samples. We highlight pending issues and future research directions needed for accelerating real-world impacts of ML-enabled Raman diagnostics for rapid and accurate diagnosis and surveillance of pathogens across critical fields. Full article
(This article belongs to the Special Issue Artificial Intelligence (AI)/Machine Learning (ML)-Assisted Chemical Sensors)
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