New Progress in Optical Fiber-Based Biosensors—2nd Edition

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

Deadline for manuscript submissions: 25 April 2025 | Viewed by 921

Special Issue Editors


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Guest Editor
Graduate Program in Electrical Engineering, Federal University of Espirito Santo, Vitoria 29075-910, Brazil
Interests: optical fiber sensors; fiber Bragg gratings; polymer optical fibers; instrumented insoles; interferometers; movement analysis; actuators; robotic systems; IoT; data processing; machine learning algorithms
Special Issues, Collections and Topics in MDPI journals

E-Mail Website
Guest Editor
Graduate Program in Electrical Engineering, Federal University of Espirito Santo, Vitoria 29075-910, Brazil
Interests: fiber Bragg gratings; fiber-optic biosensors; fiber-optic chemical sensors; in-fiber interferometers; Instrumentation; optical fiber sensors; POF-based sensors; rehabilitation robotics; structural health monitoring; surface Plasmon resonance
Special Issues, Collections and Topics in MDPI journals

Special Issue Information

Dear Colleagues,

Optical fiber biosensors are a promising technology that merge photonics and biotechnology, utilizing the principles of light propagation (phase, amplitude, frequency, light polarization) in optical fibers for sensing applications. These sensors are valued for their high specificity and sensitivity, real-time and in situ detection, miniaturization potential, and multiplexing capabilities. They can measure a wide range of physical, chemical, and biological parameters and have seen rapid advancements and growing applications in areas like medical diagnostics, environmental monitoring, food safety, drug discovery and development, and biotechnology and research.

In these sensors, the optical beam is transmitted through the optical fiber, which responds to external stimuli, detecting biological molecules or interactions by integrating biological recognition elements such as enzymes, antibodies, DNA, or aptamers that selectively bind to the target analyte (e.g., proteins, pathogens, toxins). The conversion of the biological interaction into a measurable optical signal is performed by fluorescence, luminescence absorbance, or refractive index changes, leading to Label-Based Biosensors, which require a fluorescent or colorimetric label to attach to the target analyte to produce an optical signal proportional to the analyte concentration, and Label-Free Biosensors, which measure changes in optical properties without needing to label the target analyte.

Despite the significant advantages of optical biosensors, there are several challenges in terms of the cost, stability, scalability, repeatability, and performance of the sensor due to the degradation of the biorecognition elements over time, especially under harsh environmental conditions. Additionally, the development of highly specific and reliable sensors for complex biological samples remains a challenge. In the future, optical biosensors integrated with lab-on-a-chip technologies and microfluidic systems will lead to more compact, portable, and cost-effective devices, with the detection of multiple analytes revolutionizing point-of-care diagnostics and personalized medicine.

In this context, it is a pleasure to announce the second edition of the Special Issue titled “New Progress in Optical Fiber-Based Biosensors”. All authors are cordially invited to submit original research and reviews of new fabrication processes, materials, transducing devices, and immobilization methods for optical biosensors. We hope that this Special Issue will further encourage and promote scientific contributions by researchers in the field of biosensors.

This Special Issue welcomes contributions addressing, but not limited to, the following:

  • Novel interrogation methods for biosensors;
  • Surface plasmon (SPR) and localized resonance (LSPR) for biosensing;
  • Biosensors for healthcare applications;
  • Biosensors for aquaculture and environment monitoring;
  • Wearable sensors, devices, and electronics;
  • Lab-on-a-chip;
  • Sensor devices, technology, and applications;
  • Advanced materials for sensing;
  • Nanophotonics.

Prof. Dr. Arnaldo Leal-Junior
Dr. Camilo A.R. Díaz
Guest Editors

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Keywords

  • optical fiber biosensors
  • surface plasmon resonance (SPR)
  • localized surface plasmon resonance (LSPR)
  • evanescent field
  • grating-based sensors
  • interferometer-based sensors
  • point-of-care sensors
  • nanophotonics
  • surface-enhanced Raman scattering (SERS)

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Published Papers (1 paper)

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14 pages, 5050 KiB  
Article
Surface-Enhanced Raman Scattering Combined with Machine Learning for Rapid and Sensitive Detection of Anti-SARS-CoV-2 IgG
by Thais de Andrade Silva, Gabriel Fernandes Souza dos Santos, Adilson Ribeiro Prado, Daniel Cruz Cavalieri, Arnaldo Gomes Leal Junior, Flávio Garcia Pereira, Camilo A. R. Díaz, Marco Cesar Cunegundes Guimarães, Servio Túlio Alves Cassini and Jairo Pinto de Oliveira
Biosensors 2024, 14(11), 523; https://doi.org/10.3390/bios14110523 - 29 Oct 2024
Viewed by 772
Abstract
This work reports an efficient method to detect SARS-CoV-2 antibodies in blood samples based on SERS combined with a machine learning tool. For this purpose, gold nanoparticles directly conjugated with spike protein were used in human blood samples to identify anti-SARS-CoV-2 antibodies. The [...] Read more.
This work reports an efficient method to detect SARS-CoV-2 antibodies in blood samples based on SERS combined with a machine learning tool. For this purpose, gold nanoparticles directly conjugated with spike protein were used in human blood samples to identify anti-SARS-CoV-2 antibodies. The comprehensive database utilized Raman spectra from all 594 blood serum samples. Machine learning investigations were carried out using the Scikit-Learn library and were implemented in Python, and the characteristics of Raman spectra of positive and negative SARS-CoV-2 samples were extracted using the Uniform Manifold Approximation and Projection (UMAP) technique. The machine learning models used were k-Nearest Neighbors (kNN), Support Vector Machine (SVM), Decision Trees (DTs), logistic regression (LR), and Light Gradient Boosting Machine (LightGBM). The kNN model led to a sensitivity of 0.943, specificity of 0.9275, and accuracy of 0.9377. This study showed that combining Raman spectroscopy and a machine algorithm can be an effective diagnostic method. Furthermore, we highlighted the advantages and disadvantages of each algorithm, providing valuable information for future research. Full article
(This article belongs to the Special Issue New Progress in Optical Fiber-Based Biosensors—2nd Edition)
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