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Fast and Accurate Sensing Technologies for Bacteria

A special issue of Applied Sciences (ISSN 2076-3417). This special issue belongs to the section "Optics and Lasers".

Deadline for manuscript submissions: closed (29 February 2024) | Viewed by 993

Special Issue Editors


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Guest Editor
College of Optical Science and Engineering, Zhejiang University, Hangzhou 310058, China
Interests: nanophotonics; metamaterials; optical sensing and communication; applied electromagnetics; smart technologies
Special Issues, Collections and Topics in MDPI journals

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Guest Editor
College of Biosystems Engineering and Food Science, Zhejiang University, Hangzhou, China
Interests: food safety; nanomaterials; electrochemical detection; nucleic acid amplification; pathogens
Special Issues, Collections and Topics in MDPI journals

Special Issue Information

Dear Colleagues,

Bacterial detection is crucial for people's daily lives, as it can help prevent and control disease transmission, ensure food safety, and assist in targeted drug therapy and environmental pollution control. Many technologies, such as optical sensing techniques, microfluidic, lateral flow immunochromatography assay, nano-hydrogel, electrochemical immunosensor, chemiluminescence analysis, mass spectrometry, single molecule technology, nanomaterials, and nanotechnology have been developed to identify the species and measure the concentrations of bacteria. Some optical sensing techniques, such as fiber optics, laser-based measurements, fluorescence, spectra detection, and interferometry, have also been applied to detect and quantify bacteria and related protein or nucleic acid biomarkers in a non-intrusive, accurate, and fast way.

Fast and accurate sensing technologies for bacteria have made significant strides in various fields, such as healthcare, food safety, and environmental monitoring. These technologies enable the rapid detection and identification of bacteria, aiding in timely interventions and improving public health. Additionally, the integration of artificial intelligence and machine learning enhances the accuracy and speed of identification. These evolving technologies collectively offer promising solutions for addressing bacterial contamination and infections in diverse applications. This Special Issue focuses on the latest developments in fast and accurate sensing technologies for bacteria, and some papers will be published in Applied Sciences. In contrast, some other papers will appear in Biosensors.

You may choose our Joint Special Issue in Biosensors.

Prof. Dr. Sailing He
Dr. Xingyu Lin
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. Applied Sciences 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 2400 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

  • bacterial detection
  • sensing technologies
  • rapid identification
  • high sensitivity
  • biosensors
  • nanotechnology
  • optical sensing
  • electrochemical sensors

Published Papers (1 paper)

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Research

14 pages, 5503 KiB  
Article
Detecting Multiple Mixed Bacteria Using Dual-Mode Hyperspectral Imaging and Deep Neural Networks
by He Zhu, Jing Luo and Sailing He
Appl. Sci. 2024, 14(4), 1525; https://doi.org/10.3390/app14041525 - 14 Feb 2024
Viewed by 783
Abstract
Identifying and analyzing mixed pathogenic bacteria is important for clinical diagnosis and antibiotic therapy of multiple bacterial infection. In this paper, a dual-mode hyperspectral microscopic detection technology with hybrid deep neural networks (DNNs) was proposed for simultaneous quantitative analysis of four kinds of [...] Read more.
Identifying and analyzing mixed pathogenic bacteria is important for clinical diagnosis and antibiotic therapy of multiple bacterial infection. In this paper, a dual-mode hyperspectral microscopic detection technology with hybrid deep neural networks (DNNs) was proposed for simultaneous quantitative analysis of four kinds of pathogenic bacteria in mixed samples. To acquire both transmission and fluorescence spectra regarding the mixed pathogens, we developed a dual-mode hyperspectral detection system with fine spectral resolution and wide wavelength range, which can also generate spatial images that can be used to calculate the total amount of mixed bacteria. The dual-mode spectra were regarded as mixed proportion characteristics and the input of the neural network for predicting the proportion of each bacterium present in the mixture. To better analyze the dual-mode spectral data, we customized a mixed bacteria measurement network (MB-Net) with hybrid DNNs architectures based on spectral feature fusion. Using the fusion strategy, two DNNs frameworks applied for transmission/fluorescence spectral feature processing were stacked to form the MB-Net that processes these features simultaneously, and the achieved average coefficient of determination (R2) and RMSE of validation set are 0.96 and 0.03, respectively. To the best of our knowledge, it is the first time of simultaneously detecting four types of mixed pathogenic bacteria using spectral detection technology, showing excellent potential in clinical practice. Full article
(This article belongs to the Special Issue Fast and Accurate Sensing Technologies for Bacteria)
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