Detecting urine metabolites related to training performance in swimming athletes by means of Raman spectroscopy and principal component analysis

Surface-enhanced Raman spectroscopy (SERS) can provide rapid fingerprinting of biomaterial in a nondestructive manner. The adsorption of colloidal silver to biological material suppresses native biofluorescence while providing electromagnetic surface enhancement of the normal Raman signal. This work validates the applicability of qualitative SER spectroscopy for analysis of bacterial species by utilizing principal component analysis (PCA) to show discrimination of biological threat simulants, based upon multivariate statistical confidence limits bounding known data clusters. Gram-positive Bacillus spores ( Bacillus atrophaeus, Bacillus anthracis, and Bacillus thuringiensis) are investigated along with the Gram-negative bacterium Pantoea agglomerans.

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Raman spectroscopy combined with principal component analysis and k nearest neighbour analysis for non-invasive detection of colon cancer

Laser Physics ◽

10.1088/1054-660x/26/3/035702 ◽

2016 ◽

Vol 26 (3) ◽

pp. 035702 ◽

Cited By ~ 24

Author(s):

Xiaozhou Li ◽

Tianyue Yang ◽

Siqi Li ◽

Deli Wang ◽

Youtao Song ◽

...

Keyword(s):

Principal Component Analysis ◽

Colon Cancer ◽

Raman Spectroscopy ◽

Principal Component ◽

Component Analysis ◽

Nearest Neighbour ◽

Non Invasive ◽

Neighbour Analysis

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Raman Spectroscopy Combined with Principal Component Analysis for Screening Nasopharyngeal Cancer in Human Blood Sera

Applied Spectroscopy ◽

10.1177/0003702817723928 ◽

2017 ◽

Vol 71 (11) ◽

pp. 2497-2503 ◽

Cited By ~ 11

Author(s):

Saranjam Khan ◽

Rahat Ullah ◽

Samina Javaid ◽

Shaheen Shahzad ◽

Hina Ali ◽

...

Keyword(s):

Principal Component Analysis ◽

Raman Spectroscopy ◽

Human Blood ◽

Nasopharyngeal Cancer ◽

Principal Component ◽

Component Analysis ◽

Data Sets ◽

Analysis Technique ◽

Raman Spectral Data ◽

Raman Spectral

This study demonstrates the analysis of nasopharyngeal cancer (NPC) in human blood sera using Raman spectroscopy combined with the multivariate analysis technique. Blood samples of confirmed NPC patients and healthy individuals have been used in this study. The Raman spectra from all these samples were recorded using 785 nm laser for excitation. Important Raman bands at 760, 800, 815, 834, 855, 1003, 1220–1275, and 1524 cm−1, have been observed in both normal and NPC samples. A decrease in the lipids content, phenylalanine, and β-carotene, whereas increases in amide III, tyrosine, and tryptophan have been observed in the NPC samples. The two data sets were well separated using principal component analysis (PCA) based on Raman spectral data. The spectral variations between the healthy and cancerous samples have been further highlighted by plotting loading vectors PC1 and PC2, which shows only those spectral regions where the differences are obvious.

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Ammonia borane structural study by temperature through high-resolution Raman spectroscopy and principal component analysis

Spectrochimica Acta Part A Molecular and Biomolecular Spectroscopy ◽

10.1016/j.saa.2020.118661 ◽

2020 ◽

Vol 241 ◽

pp. 118661

Author(s):

R. Hinojosa-Nava ◽

E.V. Mejía-Uriarte ◽

R.Y. Sato-Berrú

Keyword(s):

Principal Component Analysis ◽

Raman Spectroscopy ◽

High Resolution ◽

Structural Study ◽

Principal Component ◽

Ammonia Borane ◽

Component Analysis

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Comparison of Gaussian and Poisson noise models in a hybrid reference spectrum and principal component analysis algorithm for Raman spectroscopy

10.1117/12.2005455 ◽

2013 ◽

Cited By ~ 3

Author(s):

Dominique Van de Sompel ◽

Ellis Garai ◽

Cristina Zavaleta ◽

Sanjiv S. Gambhir

Keyword(s):

Principal Component Analysis ◽

Raman Spectroscopy ◽

Principal Component ◽

Component Analysis ◽

Poisson Noise ◽

Reference Spectrum ◽

Analysis Algorithm ◽

Principal Component Analysis Algorithm ◽

Noise Models

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Principal component analysis to enhance enantioselective Raman spectroscopy

The Analyst ◽

10.1039/c8an01886c ◽

2019 ◽

Vol 144 (6) ◽

pp. 2080-2086 ◽

Cited By ~ 2

Author(s):

Claudia C. Rullich ◽

Johannes Kiefer

Keyword(s):

Principal Component Analysis ◽

Raman Spectroscopy ◽

Esr Spectroscopy ◽

Principal Component ◽

Component Analysis ◽

Data Evaluation ◽

Spectroscopy Data ◽

Automatic Data ◽

Fully Automatic

Principal component analysis (PCA) applied to enantioselective Raman (esR) spectroscopy data enhances the performance of the method and opens up opportunities for a fully automatic data evaluation.

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