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Innovative Sensors and Embedded Sensor Systems for Food Analysis

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

Deadline for manuscript submissions: 20 December 2024 | Viewed by 11748

Special Issue Editor


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Guest Editor
Dipartimento di Ingegneria dell'Energia Elettrica e dell'Informazione "Guglielmo Marconi", Viale del Risorgimento 2, Bologna, Italy
Interests: sensors; electrical impedance spectroscopy; optical spectroscopy; food analysis; portable sensor systems
Special Issues, Collections and Topics in MDPI journals

Special Issue Information

Dear Colleagues,

Food products are routinely screened for quality assurance and to guarantee the absence of contaminants that can seriously endanger consumer health. Such screening procedures are usually carried out in laboratories by trained personnel, resulting in long response times and high analysis costs.

Recent advances in sensor technology form the basis of the development of embedded sensor systems, based on microcontrollers and FPGAs, as well as modern smartphones, allowing the quick and in-the-field analysis of food products by operators without particular skills. These embedded systems exploit different sensing techniques, such as electrical impedance spectroscopy (EIS), optical absorbance spectroscopy, and computer vision, and are equipped with wireless communication technologies to transfer the measured data to remote hosts.

The editors welcome the submission of high-quality research papers (not previously published in other journals) as well as review articles discussing recent advancements in the development of embedded sensor systems for food analysis and innovative techniques associated with food analysis that can be easily implemented in the form of an electronic embedded system.

Dr. Marco Grossi
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
  • food analysis
  • embedded sensor systems
  • biosensors
  • sensor networks
  • sensing techniques (electrical impedance spectroscopy, absorbance spectroscopy, fluorescence spectroscopy, and computer vision)

Published Papers (7 papers)

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Research

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19 pages, 4487 KiB  
Article
A Simple Microwave Imaging System for Food Product Inspection through a Symmetry-Based Microwave Imaging Approach
by Gennaro Bellizzi, Alessio Buzzin, Lorenzo Crocco, Antonio Mastrandrea, Noemi Zeni, Sabrina Zumbo and Marta Cavagnaro
Sensors 2024, 24(1), 99; https://doi.org/10.3390/s24010099 - 24 Dec 2023
Viewed by 972
Abstract
In the food industry, there is a growing demand for cost-effective methods for the inline inspection of food items able to non-invasively detect small foreign bodies that may have contaminated the product during the production process. Microwave imaging may be a valid alternative [...] Read more.
In the food industry, there is a growing demand for cost-effective methods for the inline inspection of food items able to non-invasively detect small foreign bodies that may have contaminated the product during the production process. Microwave imaging may be a valid alternative to the existing technologies, thanks to its inherently low-cost and its capability of sensing low-density contaminants. In this paper, a simple microwave imaging system specifically designed to enable the inspection of a large variety of food products is presented. The system consists of two circularly loaded antipodal Vivaldi antennas with a very large operative band, from 1 to 15 GHz, thus allowing a suitable spatial resolution for different food products, from mostly fatty to high water-content foods. The antennas are arranged in such a way as to collect a signal that can be used to exploit a recently proposed real-time microwave imaging strategy, leveraging the inherent symmetries that usually characterize food items. The system is experimentally characterized, and the achieved results compare favorably with the design specifications and numerical simulations. Relying on these positive results, the first experimental proof of the effectiveness of the entire system is presented confirming its efficacy. Full article
(This article belongs to the Special Issue Innovative Sensors and Embedded Sensor Systems for Food Analysis)
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17 pages, 6542 KiB  
Article
3D Printed Fused Deposition Modeling (FDM) Capillaries for Chemiresistive Gas Sensors
by Martin Adamek, Jiri Mlcek, Nela Skowronkova, Magdalena Zvonkova, Miroslav Jasso, Anna Adamkova, Josef Skacel, Iva Buresova, Romana Sebestikova, Martina Cernekova and Martina Buckova
Sensors 2023, 23(15), 6817; https://doi.org/10.3390/s23156817 - 31 Jul 2023
Cited by 1 | Viewed by 970
Abstract
This paper discusses the possible use of 3D fused deposition modeling (FDM) to fabricate capillaries for low-cost chemiresistive gas sensors that are often used in various applications. The disadvantage of these sensors is low selectivity, but 3D printed FDM capillaries have the potential [...] Read more.
This paper discusses the possible use of 3D fused deposition modeling (FDM) to fabricate capillaries for low-cost chemiresistive gas sensors that are often used in various applications. The disadvantage of these sensors is low selectivity, but 3D printed FDM capillaries have the potential to increase their selectivity. Capillaries with 1, 2 and 3 tiers with a length of 1.5 m, 3.1 m and 4.7 m were designed and manufactured. Food and goods available in the general trade network were used as samples (alcohol, seafood, chicken thigh meat, acetone-free nail polish remover and gas from a gas lighter) were also tested. The “Vodka” sample was used as a standard for determining the effect of capillary parameters on the output signal of the MiCS6814 sensor. The results show the shift of individual parts of the signal in time depending on the parameters of the capillary and the carrier air flow. A three-tier capillary was chosen for the comparison of gas samples with each other. The graphs show the differences between individual samples, not only in the height of the output signal but also in its time characteristic. The tested 3D printed FDM capillaries thus made it possible to characterize the output response by also using an inexpensive chemiresistive gas sensor in the time domain. Full article
(This article belongs to the Special Issue Innovative Sensors and Embedded Sensor Systems for Food Analysis)
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11 pages, 1414 KiB  
Article
A Novel Equipment-Free Paper-Based Fluorometric Method for the Analytical Determination of Quinine in Soft Drink Samples
by Vasiliki C. Tsaftari, Maria Tarara, Paraskevas D. Tzanavaras and George Z. Tsogas
Sensors 2023, 23(11), 5153; https://doi.org/10.3390/s23115153 - 28 May 2023
Cited by 3 | Viewed by 1578
Abstract
A simple, equipment-free, direct fluorometric method, employing paper-based analytical devices (PADs) as sensors, for the selective determination of quinine (QN) is described herein. The suggested analytical method exploits the fluorescence emission of QN without any chemical reaction after the appropriate pH adjustment with [...] Read more.
A simple, equipment-free, direct fluorometric method, employing paper-based analytical devices (PADs) as sensors, for the selective determination of quinine (QN) is described herein. The suggested analytical method exploits the fluorescence emission of QN without any chemical reaction after the appropriate pH adjustment with nitric acid, at room temperature, on the surface of a paper device with the application of a UV lamp at 365 nm. The devices crafted had a low cost and were manufactured with chromatographic paper and wax barriers, and the analytical protocol followed was extremely easy for the analyst and required no laboratory instrumentation. According to the methodology, the user must place the sample on the detection area of the paper and read with a smartphone the fluorescence emitted by the QN molecules. Many chemical parameters were optimized, and a study of interfering ions present in soft drink samples was carried out. Additionally, the chemical stability of these paper devices was considered in various maintenance conditions with good results. The detection limit calculated as 3.3 S/N was 3.6 mg L−1, and the precision of the method was satisfactory, being from 3.1% (intra-day) to 8.8% (inter-day). Soft drink samples were successfully analyzed and compared with a fluorescence method. Full article
(This article belongs to the Special Issue Innovative Sensors and Embedded Sensor Systems for Food Analysis)
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12 pages, 1728 KiB  
Article
Field-Deployable Determinations of Peroxide Index and Total Phenolic Content in Olive Oil Using a Promising Portable Sensor System
by Marco Grossi, Alessandra Bendini, Enrico Valli and Tullia Gallina Toschi
Sensors 2023, 23(11), 5002; https://doi.org/10.3390/s23115002 - 23 May 2023
Cited by 1 | Viewed by 927
Abstract
Useful information about the oxidative stability of a virgin olive oil in terms of oxidation products and antioxidant compounds can be obtained by analyzing the peroxide index (PI) and total phenolic content (TPC), respectively. These quality parameters are usually determined in a chemical [...] Read more.
Useful information about the oxidative stability of a virgin olive oil in terms of oxidation products and antioxidant compounds can be obtained by analyzing the peroxide index (PI) and total phenolic content (TPC), respectively. These quality parameters are usually determined in a chemical laboratory using expensive equipment, toxic solvents, and well-trained personnel. This paper presents a novel portable sensor system for in the field and rapid determination of PI and TPC that is particularly suited in the case of small production environments that cannot afford an internal laboratory for quality control analysis. The system is small, can be powered by both USB ports and batteries, is easy to operate, and integrates a Bluetooth module for wireless data transmission. It estimates the PI and TPC in olive oil from the measurement of the optical attenuation of an emulsion between a reagent and the sample under test. The system has been tested on a set of 12 olive oil samples (eight for calibration and four for validation), and the results have shown how the considered parameters can be estimated with good accuracy. The maximum deviation from the results obtained with the reference analytical techniques is 4.7 meq O2/kg in the case of PI and 45.3 ppm in the case of TPC for the calibration set, while it is 14.8 meq O2/kg in the case of PI and 55 ppm in the case of TPC for the validation set. Full article
(This article belongs to the Special Issue Innovative Sensors and Embedded Sensor Systems for Food Analysis)
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17 pages, 5586 KiB  
Article
Microbiological Quality Estimation of Meat Using Deep CNNs on Embedded Hardware Systems
by Dimitrios Kolosov, Lemonia-Christina Fengou, Jens Michael Carstensen, Nette Schultz, George-John Nychas and Iosif Mporas
Sensors 2023, 23(9), 4233; https://doi.org/10.3390/s23094233 - 24 Apr 2023
Cited by 1 | Viewed by 1811
Abstract
Spectroscopic sensor imaging of food samples meta-processed by deep machine learning models can be used to assess the quality of the sample. This article presents an architecture for estimating microbial populations in meat samples using multispectral imaging and deep convolutional neural networks. The [...] Read more.
Spectroscopic sensor imaging of food samples meta-processed by deep machine learning models can be used to assess the quality of the sample. This article presents an architecture for estimating microbial populations in meat samples using multispectral imaging and deep convolutional neural networks. The deep learning models operate on embedded platforms and not offline on a separate computer or a cloud server. Different storage conditions of the meat samples were used, and various deep learning models and embedded platforms were evaluated. In addition, the hardware boards were evaluated in terms of latency, throughput, efficiency and value on different data pre-processing and imaging-type setups. The experimental results showed the advantage of the XavierNX platform in terms of latency and throughput and the advantage of Nano and RP4 in terms of efficiency and value, respectively. Full article
(This article belongs to the Special Issue Innovative Sensors and Embedded Sensor Systems for Food Analysis)
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19 pages, 4815 KiB  
Article
An Affordable NIR Spectroscopic System for Fraud Detection in Olive Oil
by Candela Melendreras, Ana Soldado, José M. Costa-Fernández, Alberto López, Marta Valledor, Juan Carlos Campo and Francisco Ferrero
Sensors 2023, 23(3), 1728; https://doi.org/10.3390/s23031728 - 03 Feb 2023
Cited by 7 | Viewed by 2334
Abstract
Adulterations of olive oil are performed by adding seed oils to this high-quality product, which are cheaper than olive oils. Food safety controls have been established by the European Union to avoid these episodes. Most of these methodologies require expensive equipment, time-consuming procedures, [...] Read more.
Adulterations of olive oil are performed by adding seed oils to this high-quality product, which are cheaper than olive oils. Food safety controls have been established by the European Union to avoid these episodes. Most of these methodologies require expensive equipment, time-consuming procedures, and expert personnel to execute. Near-infrared spectroscopy (NIRS) technology has many applications in the food processing industry. It analyzes food safety and quality parameters along the food chain. Using principal component analysis (PCA), the differences and similarities between olive oil and seed oils (sesame, sunflower, and flax oil) have been evaluated. To quantify the percentage of adulterated seed oil in olive oils, partial least squares (PLS) have been employed. A total of 96 samples of olive oil adulterated with seed oils were prepared. These samples were used to build a spectra library covering various mixtures containing seed oils and olive oil contents. Eighteen chemometric models were developed by combining the first and second derivatives with Standard Normal Variable (SNV) for scatter correction to classify and quantify seed oil adulteration and percentage. The results obtained for all seed oils show excellent coefficients of determination for calibration higher than 0.80. Because the instrumental aspects are not generally sufficiently addressed in the articles, we include a specific section on some key aspects of developing a high-performance and cost-effective NIR spectroscopy solution for fraud detection in olive oil. First, spectroscopy architectures are introduced, especially the Texas Instruments Digital Light Processing (DLP) technology for spectroscopy that has been used in this work. These results demonstrate that the portable prototype can be used as an effective tool to detect food fraud in liquid samples. Full article
(This article belongs to the Special Issue Innovative Sensors and Embedded Sensor Systems for Food Analysis)
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Review

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24 pages, 369 KiB  
Review
The Need for Machines for the Nondestructive Quality Assessment of Potatoes with the Use of Artificial Intelligence Methods and Imaging Techniques
by Marek Danielak, Krzysztof Przybył and Krzysztof Koszela
Sensors 2023, 23(4), 1787; https://doi.org/10.3390/s23041787 - 05 Feb 2023
Cited by 2 | Viewed by 2290
Abstract
This article describes chemical and physical parameters, including their role in the storage, trade, and processing of potatoes, as well as their nutritional properties and health benefits resulting from their consumption. An analysis of the share of losses occurring during the production process [...] Read more.
This article describes chemical and physical parameters, including their role in the storage, trade, and processing of potatoes, as well as their nutritional properties and health benefits resulting from their consumption. An analysis of the share of losses occurring during the production process is presented. The methods and applications used in recent years to estimate the physical and chemical parameters of potatoes during their storage and processing, which determine the quality of potatoes, are presented. The potential of the technologies used to classify the quality of potatoes, mechanical and ultrasonic, and image processing and analysis using vision systems, as well as their use in applications with artificial intelligence, are discussed. Full article
(This article belongs to the Special Issue Innovative Sensors and Embedded Sensor Systems for Food Analysis)
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