Drying process of microcrystalline cellulose studied by attenuated total reflection IR spectroscopy with two-dimensional correlation spectroscopy and principal component analysis

2006 ◽  
Vol 799 (1-3) ◽  
pp. 102-110 ◽  
Author(s):  
Akihiko Watanabe ◽  
Shigeaki Morita ◽  
Serge Kokot ◽  
Mika Matsubara ◽  
Katsuhiko Fukai ◽  
...  
Keyword(s):  
Principal Component Analysis ◽  
Ir Spectroscopy ◽  
Microcrystalline Cellulose ◽  
Principal Component ◽  
Component Analysis ◽  
Total Reflection ◽  
Attenuated Total Reflection ◽  
Correlation Spectroscopy ◽  
Two Dimensional ◽  
Drying Process

scholarly journals New Approach to Generalized Two-Dimensional Correlation Spectroscopy. 1: Combination of Principal Component Analysis and Two-Dimensional Correlation Spectroscopy

2002 ◽  
Vol 56 (12) ◽  
pp. 1562-1567 ◽  
Author(s):  
Young Mee Jung ◽  
Hyeon Suk Shin ◽  
Seung Bin Kim ◽  
Isao Noda
Keyword(s):  
Principal Component Analysis ◽  
Correlation Analysis ◽  
Principal Component ◽  
Component Analysis ◽  
Correlation Spectroscopy ◽  
Data Matrix ◽  
Two Dimensional ◽  
Noise Component ◽  
Reconstructed Data ◽  
2D Correlation Analysis

The direct combination of chemometrics and two-dimensional (2D) correlation spectroscopy is considered. The use of a reconstructed data matrix based on the significant scores and loading vectors obtained from the principal component analysis (PCA) of raw spectral data is proposed as a method to improve the data quality for 2D correlation analysis. The synthetic noisy spectra were analyzed to explore the novel possibility of the use of PCA-reconstructed spectra, which are highly noise suppressed. 2D correlation analysis of this reconstructed data matrix, instead of the raw data matrix, can significantly reduce the contribution of the noise component to the resulting 2D correlation spectra.

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