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Intelligent Sensing Technologies for Monitoring Food Quality and Safety

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

Deadline for manuscript submissions: 30 December 2024 | Viewed by 1209

Special Issue Editor


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Guest Editor
Instituto Interuniversitario de Investigación de Reconocimiento Molecular y Desarrollo Tecnológico (IDM), Unidad Mixta Universitat Politècnica de València—Universitat de València, Camí de Vera s/n, 46022 Valencia, Spain
Interests: environmental control; food quality control; microelectronics-based sensors

Special Issue Information

Dear Colleagues,

We are now close to completing the first quarter of the 21st century, and humanity is facing several crucial challenges that are yet to be solved. One of these challenges is food. According to the UN databases, the world is experiencing a growing population that is expected to reach 9.8 billion inhabitants in 2050. Food inequality is also increasing worldwide, with nearly one in three people lacking regular access to adequate food in 2020. Thus, the struggle to meet the UN’s second Sustainable Development Goal for 2030 (zero hunger); improving nutrition, food quality and security; and contributing to the sustainability of the agrifood sector are fundamental tasks for humankind and to which we as scientists can contribute.

In this sense, food science and technology have come a long way in recent decades. New and better methods applicable to food technology, production and process management and food quality control have emerged and are now consolidated and implemented.

This Special Issue invites authors to contribute to the search for solutions by submitting new research results in the field of Intelligent Sensing Technologies for Monitoring Food Quality and Safety, particularly those referring (but not limited) to the following topics:

Sensing of food quality

  • Freshness
  • Ripeness/ageing
  • Freezing/thawing
  • Color/smell/texture
  • Sensory aspects
  • Nutritional aspects

Sensing of food safety

  • Detection of compounds
  • Phytosanitary control
  • Waste and spoilage
  • Infections
  • Illnesses
  • Pests

New technologies/applications

  • Chemical analyses
  • Microstructure analyses
  • Light microscopy
  • Confocal laser scanner microscopy
  • Electron microcopy
  • X-ray microscopy
  • Magnetic resonance imaging (MRI)
  • Low-field nuclear magnetic resonance (NMR)
  • Impedance Spectroscopy
  • Near Infrared Spectroscopy
  • Artificial vision techniques
  • Fluorescence
  • Gas chromatography/mass spectroscopy (GC/MS)
  • Electronic noses and tongues
  • Sensors arrays
  • And any others…

Aerial and remote agrifood sensing

  • Drones
  • Satellites
  • Aerial imaging
  • Remote agrifood and food management sensing

Prof. Dr. Nicolás Laguarda-Miró
Guest Editor

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.

Keywords

  • sensors
  • technologies
  • food
  • quality
  • safety

Published Papers (1 paper)

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Research

26 pages, 1270 KiB  
Article
Analysis of Wheat Grain Infection by Fusarium Mycotoxin-Producing Fungi Using an Electronic Nose, GC-MS, and qPCR
by Piotr Borowik, Valentyna Dyshko, Miłosz Tkaczyk, Adam Okorski, Magdalena Polak-Śliwińska, Rafał Tarakowski, Marcin Stocki, Natalia Stocka and Tomasz Oszako
Sensors 2024, 24(2), 326; https://doi.org/10.3390/s24020326 - 05 Jan 2024
Viewed by 760
Abstract
Fusarium graminearum and F. culmorum are considered some of the most dangerous pathogens of plant diseases. They are also considerably dangerous to humans as they contaminate stored grain, causing a reduction in yield and deterioration in grain quality by producing mycotoxins. Detecting Fusarium [...] Read more.
Fusarium graminearum and F. culmorum are considered some of the most dangerous pathogens of plant diseases. They are also considerably dangerous to humans as they contaminate stored grain, causing a reduction in yield and deterioration in grain quality by producing mycotoxins. Detecting Fusarium fungi is possible using various diagnostic methods. In the manuscript, qPCR tests were used to determine the level of wheat grain spoilage by estimating the amount of DNA present. High-performance liquid chromatography was performed to determine the concentration of DON and ZEA mycotoxins produced by the fungi. GC-MS analysis was used to identify volatile organic components produced by two studied species of Fusarium. A custom-made, low-cost, electronic nose was used for measurements of three categories of samples, and Random Forests machine learning models were trained for classification between healthy and infected samples. A detection performance with recall in the range of 88–94%, precision in the range of 90–96%, and accuracy in the range of 85–93% was achieved for various models. Two methods of data collection during electronic nose measurements were tested and compared: sensor response to immersion in the odor and response to sensor temperature modulation. An improvement in the detection performance was achieved when the temperature modulation profile with short rectangular steps of heater voltage change was applied. Full article
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