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Biosensors
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Biosensors for Food and Agricultural Research
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Biosensors for Food and Agricultural Research

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

Deadline for manuscript submissions: closed (31 October 2020) | Viewed by 14280

Special Issue Editors

Dr. Eric S. McLamore

E-Mail Website1 Website2
Guest Editor
1: Agricultural Sciences, Clemson University, Clemson, SC 29631, USA
2: Material Science and Engineering, North Carolina State University, Raleigh, NC 27695, USA
Interests: aptamers; protein-based biosensors; RNA virus; bacteria; phosphorus; nitrate
Special Issues, Collections and Topics in MDPI journals
Dr. Carmen L. Gomes

E-Mail Website
Guest Editor
Mechanical Engineering, Iowa State University, Ames, IA 50011, USA
Interests: food process engineering; food safety and quality; nanotechnology; delivery systems; biosensors
Special Issues, Collections and Topics in MDPI journals
Prof. Dr. Silvana Andreescu

E-Mail Website
Guest Editor
Department of Chemistry and Biomolecular Science, Clarkson University, 8 Clarkson Avenue, Potsdam, NY 13699-5810, USA
Interests: biomolecular recognition of nanoscale materialstechnologies for the immobilization of enzymes and biomolecular receptors; single-molecule electrochemistry; microelectrochemical probes for investigations of molecular mechanisms in biological systems; portable nanoparticle-based assays for food analysis and environmental monitoring; printable paper-based biosensors and field portable instrumentation
Special Issues, Collections and Topics in MDPI journals

Special Issue Information

Dear  Colleagues,

The manuscripts in this Special Edition on Food and Agricultural Biosensors provide both an overview of the state-of-the-art for biosensing in food and agriculture and also a collection of new biosensors that aim to provide rapid diagnostics of targets relevant to the food supply chain. Manuscripts presenting comprehensive reviews for the detailed analysis of sensor performance characteristics are welcome to showcase the current trends and challenges in biosensing. Reviews of photonic and electrochemical biosensors in food safety/quality summarizing the current state-of-the-art are also appropriate. Research articles that demonstrate detection of bacteria related to food and agriculture applications (both intact bacteria and microbial DNA), plant stress/disease markers in agriculture, or present innovative new approaches for food quality/safety analysis (such as edible sensors) are welcome.

Dr. Eric S. McLamore
Prof. Dr. Carmen L. Gomes
Prof. Dr. Silvana Andreescu
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.

Please visit the Instructions for Authors page before submitting a manuscript. The Article Processing Charge (APC) for publication in this open access journal is 2700 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

  • biosensor
  • agriculture
  • food
  • pathogen
  • edible
  • safety
  • sensor search engine
  • photonics
  • electrochemistry

Published Papers (3 papers)

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Research

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13 pages, 7052 KiB  
Article
Cranberry Proanthocyanidins-PANI Nanocomposite for the Detection of Bacteria Associated with Urinary Tract Infections
by Hilary Urena-Saborio, Anu Prathap M. Udayan, Emilia Alfaro-Viquez, Sergio Madrigal-Carballo, Jess D. Reed and Sundaram Gunasekaran
Biosensors 2021, 11(6), 199; https://doi.org/10.3390/bios11060199 - 19 Jun 2021
Cited by 4 | Viewed by 2801
Abstract
Consumption of cranberries is associated with the putative effects of preventing urinary tract infections (UTIs). Cranberry proanthocyanidins (PAC) contain unusual double A-type linkages, which are associated with strong interactions with surface virulence factors found on UTI-causing bacteria such as extra-intestinal pathogenic Escherichia coli [...] Read more.
Consumption of cranberries is associated with the putative effects of preventing urinary tract infections (UTIs). Cranberry proanthocyanidins (PAC) contain unusual double A-type linkages, which are associated with strong interactions with surface virulence factors found on UTI-causing bacteria such as extra-intestinal pathogenic Escherichia coli (ExPEC), depicting in bacterial agglutination processes. In this work, we demonstrated the efficacy of cranberry PAC (200 μg/mL) to agglutinate ExPEC (5.0 × 108 CFU/mL) in vitro as a selective interaction for the design of functionalized biosensors for potential detection of UTIs. We fabricated functionalized screen-printed electrodes (SPEs) by modifying with PAC-polyaniline (PANI) nanocomposites and tested the effectiveness of the PAC-PANI/SPE biosensor for detecting the presence of ExPEC in aqueous suspensions. Results indicated that the PAC-PANI/SPE was highly sensitive (limit of quantification of 1 CFU/mL of ExPEC), and its response was linear over the concentration range of 1–70,000 CFU/mL, suggesting cranberry PAC-functionalized biosensors are an innovative alternative for the detection and diagnosis of ExPEC-associated UTIs. The biosensor was also highly selective, reproducible, and stable. Full article
(This article belongs to the Special Issue Biosensors for Food and Agricultural Research)
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11 pages, 2659 KiB  
Article
A Dual Immunological Raman-Enabled Crosschecking Test (DIRECT) for Detection of Bacteria in Low Moisture Food
by Cheng Pan, Binbin Zhu and Chenxu Yu
Biosensors 2020, 10(12), 200; https://doi.org/10.3390/bios10120200 - 04 Dec 2020
Cited by 7 | Viewed by 2257
Abstract
Among the physical, chemical and biological hazards that could arise with respect to food safety, bacterial contamination has been one of the main concerns in recent years. Bacterial contamination in low moisture foods (LMFs) was an emerging threat which used to draw less [...] Read more.
Among the physical, chemical and biological hazards that could arise with respect to food safety, bacterial contamination has been one of the main concerns in recent years. Bacterial contamination in low moisture foods (LMFs) was an emerging threat which used to draw less attention as LMFs were considered at low risk of such a hazard. Bacteria can survive in low moisture environments and cause foodborne diseases once they enter the digestive system. Common detection methods such as ELISA and PCR are not well suited to LMFs, as most of them operate under aqueous environments. In this study, a Dual Immunological Raman-Enabled Crosschecking Test (DIRECT) was developed for LMFs using a nano-scaled surface enhanced Raman scattering (SERS) biosensor platform and multivariate discriminant analysis with a portable Raman spectrometer. It could provide a limit of detection (LOD) of 102 CFU/g of bacteria in model LMFs, with a detection time of 30–45 min. It has the potential to become a quick screening method for on-site bacteria detection for LMFs to identify food safety risks in real time. Full article
(This article belongs to the Special Issue Biosensors for Food and Agricultural Research)
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Review

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27 pages, 4462 KiB  
Review
Proximal Methods for Plant Stress Detection Using Optical Sensors and Machine Learning
by Alanna V. Zubler and Jeong-Yeol Yoon
Biosensors 2020, 10(12), 193; https://doi.org/10.3390/bios10120193 - 29 Nov 2020
Cited by 38 | Viewed by 8393
Abstract
Plant stresses have been monitored using the imaging or spectrometry of plant leaves in the visible (red-green-blue or RGB), near-infrared (NIR), infrared (IR), and ultraviolet (UV) wavebands, often augmented by fluorescence imaging or fluorescence spectrometry. Imaging at multiple specific wavelengths (multi-spectral imaging) or [...] Read more.
Plant stresses have been monitored using the imaging or spectrometry of plant leaves in the visible (red-green-blue or RGB), near-infrared (NIR), infrared (IR), and ultraviolet (UV) wavebands, often augmented by fluorescence imaging or fluorescence spectrometry. Imaging at multiple specific wavelengths (multi-spectral imaging) or across a wide range of wavelengths (hyperspectral imaging) can provide exceptional information on plant stress and subsequent diseases. Digital cameras, thermal cameras, and optical filters have become available at a low cost in recent years, while hyperspectral cameras have become increasingly more compact and portable. Furthermore, smartphone cameras have dramatically improved in quality, making them a viable option for rapid, on-site stress detection. Due to these developments in imaging technology, plant stresses can be monitored more easily using handheld and field-deployable methods. Recent advances in machine learning algorithms have allowed for images and spectra to be analyzed and classified in a fully automated and reproducible manner, without the need for complicated image or spectrum analysis methods. This review will highlight recent advances in portable (including smartphone-based) detection methods for biotic and abiotic stresses, discuss data processing and machine learning techniques that can produce results for stress identification and classification, and suggest future directions towards the successful translation of these methods into practical use. Full article
(This article belongs to the Special Issue Biosensors for Food and Agricultural Research)
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