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Advances in Sensors Development for Environmental and Food Quality Assessment
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Advances in Sensors Development for Environmental and Food Quality Assessment

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

Deadline for manuscript submissions: 15 June 2024 | Viewed by 704

Special Issue Editors

Prof. Dr. Florentina-Daniela Munteanu

E-Mail Website
Guest Editor
Faculty of Food Engineering, Tourism and Environmental Protection, “Aurel Vlaicu” University of Arad, Arad, Romania
Interests: environmental engineering; electrochemistry; biosensors; biotechnologies in environmental protection
Special Issues, Collections and Topics in MDPI journals
Dr. Claudiu-Ștefan Ursachi

E-Mail Website
Guest Editor
Faculty of Food Engineering, Tourism and Environmental Protection, “Aurel Vlaicu” University of Arad, 310330 Arad, Romania
Interests: food quality; food safety; biosensors
Dr. Simona Perța-Crișan

E-Mail Website
Guest Editor
Faculty of Food Engineering, Tourism and Environmental Protection, “Aurel Vlaicu” University of Arad, 310330 Arad, Romania
Interests: sensory analysis; food quality and safety; food processing

Special Issue Information

Dear Colleagues,

In recent decades, environmental health, food safety and security for human health have gained much attention. Therefore, a growing interest has been registered in the development of various sensors that have good accuracy and efficiency, as these are viable alternatives to traditional methods. Moreover, the sensors have recently profited from emerging trends including sensor arrays, artificial intelligence, the Internet of Things, smart packaging, smartphones, and nanomaterials. This Special Issue aims to gather together original articles and reviews on advances in the development of physical and chemical sensors and biosensors for the detection of environmental pollutants, food safety and food quality monitoring.

Potential topics include but are not limited to:

  • Sensors or biosensors for environmental monitoring
  • Sensors or biosensors for food safety and food quality monitoring
  • Novel sensing principles used for the development of the sensors
  • Development of arrays of sensors for environmental and food quality assessment
  • 3D printed technology for the fabrication of sensors
  • Detection of food pathogens, additives, pesticides, drug residues, etc.
  • Detection of environmental pollutants
  • Continuous monitoring using sensors

Prof. Dr. Florentina-Daniela Munteanu
Dr. Claudiu-Ștefan Ursachi
Dr. Simona Perța-Crișan
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. 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.

Published Papers (1 paper)

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Research

20 pages, 1916 KiB  
Article
Missing Value Imputation of Wireless Sensor Data for Environmental Monitoring
by Thomas Decorte, Steven Mortier, Jonas J. Lembrechts, Filip J. R. Meysman, Steven Latré, Erik Mannens and Tim Verdonck
Sensors 2024, 24(8), 2416; https://doi.org/10.3390/s24082416 - 10 Apr 2024
Viewed by 446
Abstract
Over the past few years, the scale of sensor networks has greatly expanded. This generates extended spatiotemporal datasets, which form a crucial information resource in numerous fields, ranging from sports and healthcare to environmental science and surveillance. Unfortunately, these datasets often contain missing [...] Read more.
Over the past few years, the scale of sensor networks has greatly expanded. This generates extended spatiotemporal datasets, which form a crucial information resource in numerous fields, ranging from sports and healthcare to environmental science and surveillance. Unfortunately, these datasets often contain missing values due to systematic or inadvertent sensor misoperation. This incompleteness hampers the subsequent data analysis, yet addressing these missing observations forms a challenging problem. This is especially the case when both the temporal correlation of timestamps within a single sensor and the spatial correlation between sensors are important. Here, we apply and evaluate 12 imputation methods to complete the missing values in a dataset originating from large-scale environmental monitoring. As part of a large citizen science project, IoT-based microclimate sensors were deployed for six months in 4400 gardens across the region of Flanders, generating 15-min recordings of temperature and soil moisture. Methods based on spatial recovery as well as time-based imputation were evaluated, including Spline Interpolation, MissForest, MICE, MCMC, M-RNN, BRITS, and others. The performance of these imputation methods was evaluated for different proportions of missing data (ranging from 10% to 50%), as well as a realistic missing value scenario. Techniques leveraging the spatial features of the data tend to outperform the time-based methods, with matrix completion techniques providing the best performance. Our results therefore provide a tool to maximize the benefit from costly, large-scale environmental monitoring efforts. Full article
(This article belongs to the Special Issue Advances in Sensors Development for Environmental and Food Quality Assessment)
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