Improving chemometric results by optimizing the dimension reduction for Raman spectral data sets

2014 ◽  
Vol 45 (10) ◽  
pp. 930-940 ◽  
Author(s):  
Wilm Schumacher ◽  
Stephan Stöckel ◽  
Petra Rösch ◽  
Jürgen Popp
Keyword(s):  
Dimension Reduction ◽  
Spectral Data ◽  
Data Sets ◽  
Raman Spectral Data ◽  
Raman Spectral

Raman spectral data denoising based on wavelet analysis

2008 ◽  
Author(s):  
Chen Chen ◽  
Fei Peng ◽  
Qinghua Cheng ◽  
Dahai Xu
Keyword(s):  
Wavelet Analysis ◽  
Spectral Data ◽  
Raman Spectral Data ◽  
Raman Spectral

Self-Modeling Mixture Analysis Applied to FT-Raman Spectral Data of Hydrogen Peroxide Activation by Nitriles

1997 ◽  
Vol 51 (3) ◽  
pp. 407-415 ◽  
Author(s):  
V. Vacque ◽  
N. Dupuy ◽  
B. Sombret ◽  
J. P. Huvenne ◽  
P. Legrand
Keyword(s):  
Spectral Data ◽  
Chemical Transformations ◽  
Mixture Analysis ◽  
Ft Raman Spectroscopy ◽  
Raman Spectral Data ◽  
Raman Spectral ◽  
Kinetics Of ◽  
Peroxide Activation ◽  
Ft Raman

In the analytical environment, spectral data resulting from analysis of samples often represent mixtures of several components. Extraction of information about pure components of these kinds of mixtures is a major problem, especially when reference spectra are not available or when unstable intermediates are formed. Self-modeling multivariate mixture analysis has been developed for this type of problem. In this paper two examples will be used to show the potential of this technique coupled with FT-Raman spectroscopy to elucidate reaction mechanisms and to follow in situ the kinetics of chemical transformations.

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.

Infrared and Raman spectral data and conformations of 1-pentyne

2011 ◽  
Vol 989 (1-3) ◽  
pp. 38-44
Author(s):  
Anne Horn ◽  
Henning Hopf ◽  
Peter Klaeboe
Keyword(s):  
Spectral Data ◽  
Raman Spectral Data ◽  
Raman Spectral

Study on early rice blast diagnosis based on unpre-processed Raman spectral data

2020 ◽  
Vol 234 ◽  
pp. 118255
Author(s):  
Xiaoyu Zhao ◽  
Zihao Liu ◽  
Yan He ◽  
Wei Zhang ◽  
Liang Tong
Keyword(s):  
Spectral Data ◽  
Rice Blast ◽  
Raman Spectral Data ◽  
Raman Spectral ◽  
Early Rice
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