scholarly journals Modern Raman spectroscopy for biomedical applications

2011 ◽  
Vol 6 (4) ◽  
pp. 24-28 ◽  
Author(s):  
Jürgen Popp ◽  
Christoph Krafft ◽  
Thomas Mayerhöfer
Keyword(s):  
Raman Spectroscopy ◽  
Biomedical Applications

Virtual spectral histopathology of colon cancer - biomedical applications of Raman spectroscopy and imaging

2020 ◽  
Vol 303 ◽  
pp. 112676
Author(s):  
Beata Brozek-Pluska ◽  
Adam Dziki ◽  
Halina Abramczyk
Keyword(s):  
Colon Cancer ◽  
Raman Spectroscopy ◽  
Biomedical Applications ◽  
Spectral Histopathology

scholarly journals Spatially offset Raman spectroscopy for biomedical applications

2021 ◽  
Author(s):  
Fay Nicolson ◽  
Moritz F. Kircher ◽  
Nick Stone ◽  
Pavel Matousek
Keyword(s):  
Raman Spectroscopy ◽  
Biomedical Applications ◽  
Biomedical Analysis ◽  
Non Invasive ◽  
Recent Advances

Recent advances in non-invasive biomedical analysis using SORS are discussed.

Nanostructured probes to enhance optical and vibrational spectroscopic imaging for biomedical applications

2017 ◽  
Author(s):  
Anil K. Kodali ◽  
Rohit Bhargava
Keyword(s):  
Raman Spectroscopy ◽  
Biomedical Applications ◽  
Spectroscopic Imaging ◽  
Surface Enhanced Raman Spectroscopy ◽  
Ultrasensitive Detection ◽  
Optical Resonances ◽  
Surface Enhanced ◽  
Different Types ◽  
Surface Enhanced Raman ◽  
Ir And Raman

This article describes the use of nanostructured probes to enhance optical and vibrational spectroscopic imaging for biomedical applications. Engineered probes and surfaces are promising tools for enhancing signals for ultrasensitive detection of diseases like carcinoma. Two methods of interest are surface-enhanced infrared absorption (SEIRA) spectroscopy and surface-enhanced Raman spectroscopy (SERS) for IR and Raman modalities, respectively. SERS and SEIRA can be broadly categorized under a common modality termed surface-enhanced vibrational spectroscopy. This article first reviews various breakthrough findings reported in SERS and SEIRA, along with different types ofsubstrates and contrast agents used in realizing the enhancement and theories proposed to explain these findings. It then considers the configurations of nano-LAMPs and presents example results demonstrating their optical resonances and tunability. Finally, it evaluates a few techniques for fabricating multilayered nanoparticles and highlights some issues with respect to fabrication.

scholarly journals Raman Plus X: Biomedical Applications of Multimodal Raman Spectroscopy

Sensors ◽  
2017 ◽  
Vol 17 (7) ◽  
pp. 1592 ◽  
Author(s):  
◽  
◽  
◽  
◽  
◽  
...  
Keyword(s):  
Raman Spectroscopy ◽  
Biomedical Applications

scholarly journals Spectral pre and post processing for infrared and Raman spectroscopy of biological tissues and cells

2016 ◽  
Vol 45 (7) ◽  
pp. 1865-1878 ◽  
Author(s):  
Hugh J. Byrne ◽  
Peter Knief ◽  
Mark E. Keating ◽  
Franck Bonnier
Keyword(s):  
Raman Spectroscopy ◽  
Spectral Data ◽  
Biomedical Applications ◽  
Biological Tissues ◽  
Spectral Quality ◽  
Post Processing ◽  
Factors Influencing ◽  
Infrared And Raman Spectroscopy ◽  
Processing Steps

This review presents the current understanding of the factors influencing the quality of spectra recorded and the pre-processing steps commonly employed to improve on spectral quality, as well as some of the most common techniques for classification and analysis of the spectral data for biomedical applications.

scholarly journals Deep Convolutional Neural Networks as a Unified Solution for Raman Spectroscopy-Based Classification in Biomedical Applications

2021 ◽  
Author(s):  
Mohammadrahim Kazemzadeh ◽  
Colin Hisey ◽  
Kamran Zargar ◽  
Peter Xu ◽  
Neil Broderick
Keyword(s):  
Machine Learning ◽  
Raman Spectroscopy ◽  
Raman Spectra ◽  
Biomedical Applications ◽  
Data Augmentation ◽  
Surface Enhanced Raman Spectroscopy ◽  
Machine Learning Algorithms ◽  
Cancer Tissue ◽  
Deep Convolutional Neural Networks ◽  
Surface Enhanced Raman

<div>Machine learning has shown great potential for classifying diverse samples in biomedical applications based on their Raman spectra. However, the acquired spectra typically require several preprocessing steps before standard machine learning algorithms can accurately and reliably classify them. To simplify this workflow and enable future growth of this technology, we present a unified solution for classifying biological Raman spectra without any need of prepossessing, including denoising and baseline establishment. This method is developed based on a custom version of a convolutional neural network (CNN) elicited from ResNet architecture, combined with our proposed data augmentation technique. The superiority of this method compared to conventional classification techniques is shown by applying it to Raman spectra of different grades of bladder cancer tissue and surface enhanced Raman spectroscopy (SERS) spectra of various strains of E. Coli extracellular vesicles (EVs). These results show that our method is far more robust compared to its conventional counterparts when dealing with the various kinds of spectral baselines produced by different Raman spectrometers.</div>

scholarly journals Looking for a perfect match: multimodal combinations of Raman spectroscopy for biomedical applications

2021 ◽  
Vol 26 (08) ◽  
Author(s):  
Iwan W. Schie ◽  
Clara Stiebing ◽  
Jürgen Popp
Keyword(s):  
Raman Spectroscopy ◽  
Biomedical Applications ◽  
Perfect Match

scholarly journals Raman Spectroscopy: Stimuli Responsive Hydrogels for Biomedical Applications

2008 ◽  
Vol 80 (9) ◽  
pp. 1312-1312
Author(s):  
A. Naddaf ◽  
H.-J. Bart
Keyword(s):  
Raman Spectroscopy ◽  
Biomedical Applications ◽  
Stimuli Responsive ◽  
Responsive Hydrogels
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