Principal component analysis to enhance enantioselective Raman spectroscopy

The Analyst ◽  
2019 ◽  
Vol 144 (6) ◽  
pp. 2080-2086 ◽  
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.

Investigation of sport supplements quality by Raman spectroscopy and principal component analysis

2016 ◽  
Vol 87 ◽  
pp. 1-7 ◽  
Author(s):  
Mariana R. Almeida ◽  
Letícia P. de Souza ◽  
Rodrigo S. Cesar ◽  
Rafael A. Sousa ◽  
Celly M.S. Izumi
Keyword(s):  
Principal Component Analysis ◽  
Raman Spectroscopy ◽  
Principal Component ◽  
Component Analysis ◽  
Sport Supplements

Data evaluation of tar oil degradation using comprehensive GC2/ MS: individual compounds and principal component analysis

2012 ◽  
Vol 87 (9) ◽  
pp. 1237-1245 ◽  
Author(s):  
Viktoriya Vasilieva ◽  
Les Janik ◽  
Kerstin E. Scherr ◽  
Eva Edelmann ◽  
Andreas P. Loibner
Keyword(s):  
Principal Component Analysis ◽  
Principal Component ◽  
Component Analysis ◽  
Data Evaluation ◽  
Oil Degradation ◽  
Tar Oil

Monitoring of chemotherapy leukemia treatment using Raman spectroscopy and principal component analysis

2014 ◽  
Vol 29 (3) ◽  
pp. 1241-1249 ◽  
Author(s):  
José Luis González-Solís ◽  
Juan Carlos Martínez-Espinosa ◽  
Juan Manuel Salgado-Román ◽  
Pascual Palomares-Anda
Keyword(s):  
Principal Component Analysis ◽  
Raman Spectroscopy ◽  
Principal Component ◽  
Component Analysis ◽  
Leukemia Treatment

Bacillus Spore Classification via Surface-Enhanced Raman Spectroscopy and Principal Component Analysis

2008 ◽  
Vol 62 (3) ◽  
pp. 267-272 ◽  
Author(s):  
J. Guicheteau ◽  
L. Argue ◽  
D. Emge ◽  
A. Hyre ◽  
M. Jacobson ◽  
...  
Keyword(s):  
Principal Component Analysis ◽  
Raman Spectroscopy ◽  
Bacterial Species ◽  
Principal Component ◽  
Component Analysis ◽  
Surface Enhanced Raman Spectroscopy ◽  
Raman Signal ◽  
Surface Enhanced ◽  
Surface Enhanced Raman ◽  
Bacillus Spores

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.

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

2017 ◽  
Vol 71 (11) ◽  
pp. 2497-2503 ◽  
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.

Sign in / Sign up

Export Citation Format

Share Document