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Chemosensors
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Advanced Real-Time On-Site Sensing Technologies for Food and Environment Analysis:...
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Advanced Real-Time On-Site Sensing Technologies for Food and Environment Analysis: 2nd Edition

A special issue of Chemosensors (ISSN 2227-9040). This special issue belongs to the section "Analytical Methods, Instrumentation and Miniaturization".

Deadline for manuscript submissions: 31 May 2025 | Viewed by 2062

Special Issue Editors

Prof. Dr. Zhenbo Wei

E-Mail Website
Guest Editor
Department of Biosystems Engineering, Zhejiang University, Hangzhou 310058, China
Interests: nanocomposites; modified electrode; pattern recognition; intelligent flavor sensing system; food quality detection
Special Issues, Collections and Topics in MDPI journals
Dr. Shanshan Qiu

E-Mail Website
Guest Editor
College of Materials and Environmental Engineering, Hangzhou Dianzi University, Hangzhou, China
Interests: gas sensors; data mining
Special Issues, Collections and Topics in MDPI journals

Special Issue Information

Dear Colleagues,

Real-time detection devices and sensors are key in object detection, with a fast inference while maintaining simple operation and a base-level accuracy. The number of different types of sensors that focus on object detection quantitatively or qualitatively is continuously growing, although their applications in practical utilization are more limited. Compared to laboratory-scale devices, real-time on-site detection devices based on gas sensors, microwave sensors or spectroscopy sensors are extremely attractive due to their low cost, easy operation and simplified sample pretreatment.

This Special Issue will provide a forum for the latest research activities in the field of chemical/physical sensors, relevant data mining and their application. Both review articles and original research papers are solicited in areas including, but not limited to, the following:

  • Gas sensors, microwave sensors or spectroscopy sensors;
  • Online analysis system design based on micro sensors or sensor arrays;
  • The application of sensors for food detection or environment monitoring;
  • Data mining for sensor signal feature extraction, data reduction, classification, prediction, etc.

Prof. Dr. Zhenbo Wei
Dr. Shanshan Qiu
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

  • gas sensors
  • microwave sensors
  • spectroscopy sensors
  • food detection
  • environment monitoring

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

Published Papers (2 papers)

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17 pages, 6065 KiB  
Article
AIPE-Active Neutral Ir(III) Complexes as Bi-Responsive Luminescent Chemosensors for Sensing Picric Acid and Fe3+ in Aqueous Media
by Qinglong Zhang, Jiangchao Xu, Qiang Xu and Chun Liu
Chemosensors 2025, 13(1), 10; https://doi.org/10.3390/chemosensors13010010 - 8 Jan 2025
Viewed by 791
Abstract
Three neutral iridium complexes Ir1–Ir3 were synthesized using diphenylphosphoryl-substituted 2-phenylpyridine derivatives as the cyclometalating ligand and picolinic acid as the auxiliary ligand. They exhibited significant aggregation-induced phosphorescent emission (AIPE) properties in H2O/THF and were successfully used as bi-responsive luminescent [...] Read more.
Three neutral iridium complexes Ir1–Ir3 were synthesized using diphenylphosphoryl-substituted 2-phenylpyridine derivatives as the cyclometalating ligand and picolinic acid as the auxiliary ligand. They exhibited significant aggregation-induced phosphorescent emission (AIPE) properties in H2O/THF and were successfully used as bi-responsive luminescent sensors for the detection of picric acid (PA) and Fe3+ in aqueous media. Ir1–Ir3 possesses high efficiency and high selectivity for detecting PA and Fe3+, with the lowest limit of detection at 59 nM for PA and 390 nM for Fe3+. Additionally, the complexes can achieve naked-eye detection of Fe3+ in aqueous media. Ir1–Ir3 exhibit excellent potential for practical applications in complicated environments. The detection mechanism for PA is attributed to photo-induced electron transfer (PET) and Förster resonance energy transfer (FRET), and the detection mechanism for Fe3+ may be explained by PET and the strong interactions between Fe3+ and the complexes. Full article
(This article belongs to the Special Issue Advanced Real-Time On-Site Sensing Technologies for Food and Environment Analysis: 2nd Edition)
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24 pages, 8219 KiB  
Article
Prediction of Potato Rot Level by Using Electronic Nose Based on Data Augmentation and Channel Attention Conditional Convolutional Neural Networks
by Jiayu Mai, Haonan Lin, Xuezhen Hong and Zhenbo Wei
Chemosensors 2024, 12(12), 275; https://doi.org/10.3390/chemosensors12120275 - 20 Dec 2024
Viewed by 862
Abstract
This study introduces a novel approach for predicting the decay levels of potato by integrating an electronic nose system combined with feature-optimized deep learning models. The electronic nose system was utilized to collect volatile gas data from potatoes at different decay stages, offering [...] Read more.
This study introduces a novel approach for predicting the decay levels of potato by integrating an electronic nose system combined with feature-optimized deep learning models. The electronic nose system was utilized to collect volatile gas data from potatoes at different decay stages, offering a non-invasive method to classify decay levels. To mitigate data scarcity and improve training model robustness, a Gaussian Mixture Embedded Generative Adversarial Network (GMEGAN) was used to generate synthetic data, resulting in augmented datasets that increased diversity and improved model performance. Several machine learning and deep learning models, including traditional classifiers (SVM, LR, RF, ANN) and advanced neural networks (CNN, ECA-CNN, CAM-CNN, Conditional CNN), were trained and evaluated. Models incorporating feature-optimized channel attention modules (f-CAM, f-ECA) achieved a classification accuracy of up to 90.28%, significantly outperforming traditional machine learning models (72–77%) and standard CNN models (83.33%). The inclusion of GMEGAN-generated datasets further enhanced classification performance, especially for feature-optimized Conditional CNN models, with an observed increase in accuracy of up to 5.55%. A comprehensive evaluation of the GMEGAN-generated data, including feature mapping consistency, data distribution similarity, and quality metrics, demonstrated that the generated data closely resembled real data, thereby effectively enhancing dataset diversity. The proposed approach shows significant potential in improving classification accuracy and robustness for agricultural quality assessment, particularly in predicting the decay levels of potatoes. Full article
(This article belongs to the Special Issue Advanced Real-Time On-Site Sensing Technologies for Food and Environment Analysis: 2nd Edition)
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