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A special issue of Biosensors (ISSN 2079-6374). This special issue belongs to the section "Biosensors and Healthcare".

Deadline for manuscript submissions: closed (28 February 2022) | Viewed by 37827

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Special Issue Editors

Dr. Xiaonan Lu

E-Mail Website
Guest Editor

Department of Food Science, The University of British Columbia, Vancouver, BC, Canada
Interests: food safety; food microbiology; food authentication; biosensor; instrumentation; molecular microbiology
Special Issues, Collections and Topics in MDPI journals

Dr. Yaxi Hu

E-Mail Website
Guest Editor

Food Research Division, Health Canada, Ottawa, ON K1A 0K9, Canada
Interests: food safety; food authenticity; sensors; instrumentation; metabolomics

Special Issue Information

Dear Colleagues,

Agri-food is a highly complicated system that challenges the determination of trace levels of chemical and microbial contaminants. Meanwhile, a large number of food commodities are transported among different countries due to globalization. These contaminants can eventually cause various human diseases. Innovative methods to rapidly and accurately detect these contaminants are therefore urgently required. Sensors can be good candidates for such determinations in a high-throughput manner. In this Special Issue, we encourage the submission of original research articles, research notes, and review articles about recent advancements in developing sensors for rapid detection of food contamination so as to improve the safety of our agri-food systems. The technology may include but not be limited to microfluidic “lab-on-a-chip”, quantum dot, surface-enhanced Raman spectroscopy, molecularly imprinted polymers, aptamers, etc.

Prof. Xiaonan Lu
Dr. Yaxi Hu
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. Biosensors is an international peer-reviewed open access monthly journal published by MDPI.

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Keywords

Food safety
Rapid detection
Sensors
Spectroscopy
Microfluidic “lab-on-a-chip”
Recognition elements
Fluorescence spectroscopy
Electrochemistry
Photonics

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Published Papers (7 papers)

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Research

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16 pages, 2927 KiB

Open AccessArticle

Reflectance Spectroscopy with Multivariate Methods for Non-Destructive Discrimination of Edible Oil Adulteration

by Ning Su, Shizhuang Weng, Liusan Wang and Taosheng Xu

Biosensors 2021, 11(12), 492; https://doi.org/10.3390/bios11120492 - 2 Dec 2021

Cited by 6 | Viewed by 2745

Abstract

The visible and near-infrared (Vis-NIR) reflectance spectroscopy was utilized for the rapid and nondestructive discrimination of edible oil adulteration. In total, 110 samples of sesame oil and rapeseed oil adulterated with soybean oil in different levels were produced to obtain the reflectance spectra of 350–2500 nm. A set of multivariant methods was applied to identify adulteration types and adulteration rates. In the qualitative analysis of adulteration type, the support vector machine (SVM) method yielded high overall accuracy with multiple spectra pretreatments. In the quantitative analysis of adulteration rate, the random forest (RF) combined with multivariate scattering correction (MSC) achieved the highest identification accuracy of adulteration rate with the full wavelengths of Vis-NIR spectra. The effective wavelengths of the Vis-NIR spectra were screened to improve the robustness of the multivariant methods. The analysis results suggested that the competitive adaptive reweighted sampling (CARS) was helpful for removing the redundant information from the spectral data and improving the prediction accuracy. The PLSR + MSC + CARS model achieved the best prediction performance in the two adulteration cases of sesame oil and rapeseed oil. The coefficient of determination (

R_{P_{c v}}^{2}

) and the root mean square error (

R M S E_{P_{c v}}

) of the prediction set were 0.99656 and 0.01832 in sesame oil adulterated with soybean oil, and the

R_{P_{c v}}^{2}

and

R M S E_{P_{c v}}

were 0.99675 and 0.01685 in rapeseed oil adulterated with soybean oil, respectively. The Vis-NIR reflectance spectroscopy with the assistance of multivariant analysis can effectively discriminate the different adulteration rates of edible oils. Full article

(This article belongs to the Special Issue Sensors for Food Safety)

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14 pages, 868 KiB

Open AccessArticle

Rapid Detection of Fatty Acids in Edible Oils Using Vis-NIR Reflectance Spectroscopy with Multivariate Methods

by Ning Su, Fangfang Pan, Liusan Wang and Shizhuang Weng

Biosensors 2021, 11(8), 261; https://doi.org/10.3390/bios11080261 - 3 Aug 2021

Cited by 8 | Viewed by 3207

Abstract

The composition and content of fatty acids are critical indicators to identify the quality of edible oils. This study was undertaken to establish a rapid determination method for quality detection of edible oils based on quantitative analysis of palmitic acid, stearic acid, arachidic acid, and behenic acid. Seven kinds of oils were measured to obtain Vis-NIR spectra. Multivariate methods combined with pretreatment methods were adopted to establish quantitative analysis models for the four fatty acids. The model of support vector machine (SVM) with standard normal variate (SNV) pretreatment showed the best predictive performance for the four fatty acids. For the palmitic acid, the determination coefficient of prediction (

R_{P}^{2}

) was 0.9504 and the root mean square error of prediction (

R M S E_{P}

) was 0.8181. For the stearic acid,

R_{P}^{2}

and

R M S E_{P}

were 0.9636 and 0.2965. In the prediction of arachidic acid,

R_{P}^{2}

and

R M S E_{P}

were 0.9576 and 0.0577. In the prediction of behenic acid, the

R_{P}^{2}

and

R M S E_{P}

were 0.9521 and 0.1486. Furthermore, the effective wavelengths selected by successive projections algorithm (SPA) were useful for establishing simplified prediction models. The results demonstrate that Vis-NIR spectroscopy combined with multivariate methods can provide a rapid and accurate approach for fatty acids detection of edible oils. Full article

(This article belongs to the Special Issue Sensors for Food Safety)

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13 pages, 2638 KiB

Open AccessArticle

Raman Microspectroscopic Analysis of Selenium Bioaccumulation by Green Alga Chlorella vulgaris

by Martin Kizovský, Zdeněk Pilát, Mykola Mylenko, Pavel Hrouzek, Jan Kuta, Radim Skoupý, Vladislav Krzyžánek, Kamila Hrubanová, Olga Adamczyk, Jan Ježek, Silvie Bernatová, Tereza Klementová, Alžběta Gjevik, Martin Šiler, Ota Samek and Pavel Zemánek

Biosensors 2021, 11(4), 115; https://doi.org/10.3390/bios11040115 - 10 Apr 2021

Cited by 5 | Viewed by 4483

Abstract

Selenium (Se) is an element with many commercial applications as well as an essential micronutrient. Dietary Se has antioxidant properties and it is known to play a role in cancer prevention. However, the general population often suffers from Se deficiency. Green algae, such as Chlorella vulgaris, cultivated in Se-enriched environment may be used as a food supplement to provide adequate levels of Se. We used Raman microspectroscopy (RS) for fast, reliable, and non-destructive measurement of Se concentration in living algal cells. We employed inductively coupled plasma-mass spectrometry as a reference method to RS and we found a substantial correlation between the Raman signal intensity at 252 cm⁻¹ and total Se concentration in the studied cells. We used RS to assess the uptake of Se by living and inactivated algae and demonstrated the necessity of active cellular transport for Se accumulation. Additionally, we observed the intracellular Se being transformed into an insoluble elemental form, which we further supported by the energy-dispersive X-ray spectroscopy imaging. Full article

(This article belongs to the Special Issue Sensors for Food Safety)

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10 pages, 1633 KiB

Open AccessArticle

An Array of MOX Sensors and ANNs to Assess Grated Parmigiano Reggiano Cheese Packs’ Compliance with CFPR Guidelines

by Marco Abbatangelo, Estefanía Núñez-Carmona, Veronica Sberveglieri, Dario Zappa, Elisabetta Comini and Giorgio Sberveglieri

Biosensors 2020, 10(5), 47; https://doi.org/10.3390/bios10050047 - 2 May 2020

Cited by 8 | Viewed by 4119

Abstract

Parmigiano Reggiano cheese is one of the most appreciated Italian foods on account of its high nutrient content and taste. Due to its high cost, these characteristics make this product subject to counterfeiting in different forms. In this study, an approach based on an array of gas sensors has been employed to assess if it was possible to distinguish different samples based on their aroma. Samples were characterized in terms of rind percentage, seasoning, and rind working process. From the responses of the sensors, five features were extracted and the capability of these parameters to recognize target classes was tested with statistical analysis. Hence, the performance of the sensors’ array was quantified using artificial neural networks. To simplify the problem, a hierarchical approach has been used: three steps of classification were performed, and in each step one parameter of the grated cheese was identified (firstly, seasoning; secondly, rind working process; finally, rind percentage). The accuracies ranged from 88.24% to 100%. Full article

(This article belongs to the Special Issue Sensors for Food Safety)

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Review

Jump to: Research

16 pages, 1617 KiB

Open AccessReview

Time Domain (TD) Proton NMR Analysis of the Oxidative Safety and Quality of Lipid-Rich Foods

by Tatiana Osheter, Charles Linder and Zeev Wiesman

Biosensors 2022, 12(4), 230; https://doi.org/10.3390/bios12040230 - 9 Apr 2022

Cited by 6 | Viewed by 3092

Abstract

Food safety monitoring is highly important due to the generation of unhealthy components within many food products during harvesting, processing, storage, transportation and cooking. Current technologies for food safety analysis often require sample extraction and the modification of the complex chemical and morphological structures of foods, and are either time consuming, have insufficient component resolution or require costly and complex instrumentation. In addition to the detection of unhealthy chemical toxins and microbes, food safety needs further developments in (a) monitoring the optimal nutritional compositions in many different food categories and (b) minimizing the potential chemical changes of food components into unhealthy products at different stages from food production until digestion. Here, we review an efficient methodology for overcoming the present analytical limitations of monitoring a food’s composition, with an emphasis on oxidized food components, such as polyunsaturated fatty acids, in complex structures, including food emulsions, using compact instruments for simple real-time analysis. An intelligent low-field proton NMR as a time domain (TD) NMR relaxation sensor technology for the monitoring of T₂ (spin-spin) and T₁ (spin-lattice) energy relaxation times is reviewed to support decision-making by producers, retailers and consumers in regard to food safety and nutritional value during production, shipping, storage and consumption. Full article

(This article belongs to the Special Issue Sensors for Food Safety)

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21 pages, 2550 KiB

Open AccessReview

Applications of Fluorescence Spectroscopy, RGB- and MultiSpectral Imaging for Quality Determinations of White Meat: A Review

by Ke-Jun Fan and Wen-Hao Su

Biosensors 2022, 12(2), 76; https://doi.org/10.3390/bios12020076 - 28 Jan 2022

Cited by 23 | Viewed by 5601

Abstract

Fluorescence spectroscopy, color imaging and multispectral imaging (MSI) have emerged as effective analytical methods for the non-destructive detection of quality attributes of various white meat products such as fish, shrimp, chicken, duck and goose. Based on machine learning and convolutional neural network, these techniques can not only be used to determine the freshness and category of white meat through imaging and analysis, but can also be used to detect various harmful substances in meat products to prevent stale and spoiled meat from entering the market and causing harm to consumer health and even the ecosystem. The development of quality inspection systems based on such techniques to measure and classify white meat quality parameters will help improve the productivity and economic efficiency of the meat industry, as well as the health of consumers. Herein, a comprehensive review and discussion of the literature on fluorescence spectroscopy, color imaging and MSI is presented. The principles of these three techniques, the quality analysis models selected and the research results of non-destructive determinations of white meat quality over the last decade or so are analyzed and summarized. The review is conducted in this highly practical research field in order to provide information for future research directions. The conclusions detail how these efficient and convenient imaging and analytical techniques can be used for non-destructive quality evaluation of white meat in the laboratory and in industry. Full article

(This article belongs to the Special Issue Sensors for Food Safety)

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22 pages, 2866 KiB

Open AccessReview

Application of Raman Spectroscopic Methods in Food Safety: A Review

by Marlen Petersen, Zhilong Yu and Xiaonan Lu

Biosensors 2021, 11(6), 187; https://doi.org/10.3390/bios11060187 - 8 Jun 2021

Cited by 112 | Viewed by 13337

Abstract

Food detection technologies play a vital role in ensuring food safety in the supply chains. Conventional food detection methods for biological, chemical, and physical contaminants are labor-intensive, expensive, time-consuming, and often alter the food samples. These limitations drive the need of the food industry for developing more practical food detection tools that can detect contaminants of all three classes. Raman spectroscopy can offer widespread food safety assessment in a non-destructive, ease-to-operate, sensitive, and rapid manner. Recent advances of Raman spectroscopic methods further improve the detection capabilities of food contaminants, which largely boosts its applications in food safety. In this review, we introduce the basic principles of Raman spectroscopy, surface-enhanced Raman spectroscopy (SERS), and micro-Raman spectroscopy and imaging; summarize the recent progress to detect biological, chemical, and physical hazards in foods; and discuss the limitations and future perspectives of Raman spectroscopic methods for food safety surveillance. This review is aimed to emphasize potential opportunities for applying Raman spectroscopic methods as a promising technique for food safety detection. Full article

(This article belongs to the Special Issue Sensors for Food Safety)

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