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Chemical Sensors for Environment and Agri-Food Analysis
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Chemical Sensors for Environment and Agri-Food Analysis

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

Deadline for manuscript submissions: closed (30 September 2022) | Viewed by 7214

Special Issue Editor

Dr. Veronica Sberveglieri

E-Mail Website
Guest Editor
1. National Research Council, Institute of Bioscience and Bioresources (CNR-IBBR), Via J.F. Kennedy, 17/i, 42124 Reggio Emilia, Italy
2. Nano Sensor Systems, NASYS Spin-Off University of Brescia, 25125 Brescia, Italy
Interests: study of the volatiloma; in the agro-food sector; set-up of new nanomaterials acting as a substrate for gas sensors (MOX); study of new biosensors based on biological substrates; gas chromatography with mass spectrometry (GC-MS) for the study of the complexity of the volatiloma; integrated IoT data-base from farm to fork to support traceability and quality in the food chain
Special Issues, Collections and Topics in MDPI journals

Special Issue Information

Dear Colleagues,

In recent years, gas sensors have been widely used in different fields such as human health, environmental monitoring, automotive, and IoT in general.

We will focus our attention on gas sensor applications in two major areas, as follows:

  • Food quality, security and safety, from farm to fork, considering microbial and chemical contaminarion;
  • Environmental aspects considering indoor and outdoor applications, correlated with pollutant evolution in air, water and soil.

Gas sensors will be able to integrate their ability and advantages in each mentioned sector in the next 5 years. Sensors will support, help, and increase the food sector’s abilities, and also aim to become increasingly user-friendly and closer to real needs.

The covered topics will be extended to sensing devices, networks, and an array of gas sensors, and applied to the whole food field, from farm to fork, as well as environmental applications both indoors and outdoors, to increase safety and security.

Potential gas sensor topics include but are not limited to the following:

  • Quality online/at line control from farm to fork;
  • Shelf-life measurement;
  • Risk assessment;
  • IoF—Internet of Food;
  • Monitoring the presence of harmful chemical compounds (neoformation and not) in food and the environment;
  • Following the possible growth of microorganisms, indigenous, altering, and pathogens in the food field, whether they are from the environment/surfaces/human contact or from food;
  • Verify the occurrence of possible known or emerging contaminants;
  • Impact of the use of substances on the environment;
  • Assessment of the impact of the use of new (bio) fertilizers on the surrounding environment;
  • Assessment of the healthiness of indoor environments (offices, schools, universities, public transports) regarding chemical, microbiological contaminants.

Dr. Veronica Sberveglieri
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.

Published Papers (3 papers)

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Research

28 pages, 5884 KiB  
Article
Volatile Olfactory Profiles of Umbrian Extra Virgin Olive Oils and Their Discrimination through MOX Chemical Sensors
by Roberto Mariotti, Estefanía Núñez-Carmona, Dario Genzardi, Saverio Pandolfi, Veronica Sberveglieri and Soraya Mousavi
Sensors 2022, 22(19), 7164; https://doi.org/10.3390/s22197164 - 21 Sep 2022
Cited by 5 | Viewed by 1672
Abstract
Extra virgin olive oil (EVOO) is the best vegetable oil worldwide but, at the same time, is one of the product victims of fraud in the agri-food sector, and the differences about quality within the extra-virgin olive oil category are often missed. Several [...] Read more.
Extra virgin olive oil (EVOO) is the best vegetable oil worldwide but, at the same time, is one of the product victims of fraud in the agri-food sector, and the differences about quality within the extra-virgin olive oil category are often missed. Several scientific techniques were applied in order to guarantee the authenticity and quality of this EVOO. In the present study, the volatile compounds (VOCs) by gas chromatography–mass spectrometry with solid-phase micro-extraction detection (GC–MS SPME), organoleptic analysis by the official Slow Food panel and the detection by a Small Sensor System (S3) were applied. Ten EVOOs from Umbria, a central Italian region, were selected from the 2021 Slow Food Italian extra virgin olive oil official guide, which includes hundreds of high-quality olive oils. The results demonstrated the possibility to discriminate the ten EVOOs, even if they belong to the same Italian region, by all three techniques. The result of GC–MS SPME detection was comparable at the discrimination level to the organoleptic test with few exceptions, while the S3 was able to better separate some EVOOs, which were not discriminated perfectly by the other two methods. The correlation analysis performed among and between the three methodologies allowed us to identify 388 strong associations with a p value less than 0.05. This study has highlighted how much the mix of VOCs was different even among few and localized EVOOs. The correlation with the sensor detection, which is faster and chipper compared to the other two techniques, elucidated the similarities and discrepancies between the applied methods. Full article
(This article belongs to the Special Issue Chemical Sensors for Environment and Agri-Food Analysis)
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11 pages, 1993 KiB  
Article
Nanosensor Based on Thermal Gradient and Machine Learning for the Detection of Methanol Adulteration in Alcoholic Beverages and Methanol Poisoning
by Matteo Tonezzer, Nicola Bazzanella, Flavia Gasperi and Franco Biasioli
Sensors 2022, 22(15), 5554; https://doi.org/10.3390/s22155554 - 25 Jul 2022
Cited by 8 | Viewed by 1771
Abstract
Methanol, naturally present in small quantities in the distillation of alcoholic beverages, can lead to serious health problems. When it exceeds a certain concentration, it causes blindness, organ failure, and even death if not recognized in time. Analytical techniques such as chromatography are [...] Read more.
Methanol, naturally present in small quantities in the distillation of alcoholic beverages, can lead to serious health problems. When it exceeds a certain concentration, it causes blindness, organ failure, and even death if not recognized in time. Analytical techniques such as chromatography are used to detect dangerous concentrations of methanol, which are very accurate but also expensive, cumbersome, and time-consuming. Therefore, a gas sensor that is inexpensive and portable and capable of distinguishing methanol from ethanol would be very useful. Here, we present a resistive gas sensor, based on tin oxide nanowires, that works in a thermal gradient. By combining responses at various temperatures and using machine learning algorithms (PCA, SVM, LDA), the device can distinguish methanol from ethanol in a wide range of concentrations (1–100 ppm) in both dry air and under different humidity conditions (25–75% RH). The proposed sensor, which is small and inexpensive, demonstrates the ability to distinguish methanol from ethanol at different concentrations and could be developed both to detect the adulteration of alcoholic beverages and to quickly recognize methanol poisoning. Full article
(This article belongs to the Special Issue Chemical Sensors for Environment and Agri-Food Analysis)
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34 pages, 2042 KiB  
Article
Internet of Food (IoF), Tailor-Made Metal Oxide Gas Sensors to Support Tea Supply Chain
by Estefanía Núñez-Carmona, Marco Abbatangelo and Veronica Sberveglieri
Sensors 2021, 21(13), 4266; https://doi.org/10.3390/s21134266 - 22 Jun 2021
Cited by 12 | Viewed by 2891
Abstract
Tea is the second most consumed beverage, and its aroma, determined by volatile compounds (VOCs) present in leaves or developed during the processing stages, has a great influence on the final quality. The goal of this study is to determine the volatilome of [...] Read more.
Tea is the second most consumed beverage, and its aroma, determined by volatile compounds (VOCs) present in leaves or developed during the processing stages, has a great influence on the final quality. The goal of this study is to determine the volatilome of different types of tea to provide a competitive tool in terms of time and costs to recognize and enhance the quality of the product in the food chain. Analyzed samples are representative of the three major types of tea: black, green, and white. VOCs were studied in parallel with different technologies and methods: gas chromatography coupled with mass spectrometer and solid phase microextraction (SPME-GC-MS) and a device called small sensor system, (S3). S3 is made up of tailor-made metal oxide gas sensors, whose operating principle is based on the variation of sensor resistance based on volatiloma exposure. The data obtained were processed through multivariate statistics, showing the full file of the pre-established aim. From the results obtained, it is understood how supportive an innovative technology can be, remotely controllable supported by machine learning (IoF), aimed in the future at increasing food safety along the entire production chain, as an early warning system for possible microbiological or chemical contamination. Full article
(This article belongs to the Special Issue Chemical Sensors for Environment and Agri-Food Analysis)
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