Application of Chemometrics Approaches to Analysis of Mid-Infrared Spectra of Ibuprofen Diluted in Supercritical Carbon Dioxide

2018 ◽  
Vol 72 (10) ◽  
pp. 1548-1560
Author(s):  
Roman D. Oparin ◽  
Michael G. Kiselev

This work represents a comprehensive analysis of mid-infrared (mid-IR) spectra of ibuprofen diluted in supercritical CO2 (in the temperature range of 40–90 ℃ and at the CO2 density corresponding to 1.3 of its critical value). The study employed mathematical approaches based on data matrix analysis such as two-dimensional cross-correlation analysis (2D-COS) and principal component analysis (PCA). Two-dimensional cross-correlation analysis allowed us to reveal correlations between the spectral contributions constituting the analytical spectral band and assigned to certain ibuprofen conformers, as well as the significance of these correlations. It has been shown that the considerable increase in the total intensity of the analytical spectral band, proportional to the equilibrium ibuprofen concentration in the supercritical CO2 phase, is accompanied by certain redistribution of intensities of the spectral components related to the corresponding conformers. The PCA allowed us to determine the changes of intensities of individual spectral contributions for each thermodynamic point in the considered temperature range. It has been shown that these two complementary methods provide more precise information that may be used as the initial data in the classical analysis of spectral data based on spectral curve deconvolution into individual spectral contributions.

2017 ◽  
Vol 72 (2) ◽  
pp. 288-296 ◽  
Author(s):  
Michał Kwaśniewicz ◽  
Mirosław A. Czarnecki

Effect of the chain length on mid-infrared (MIR) and near-infrared (NIR) spectra of aliphatic 1-alcohols from methanol to 1-decanol was examined in detail. Of particular interest were the spectra-structure correlations in the NIR region and the correlation between MIR and NIR spectra of 1-alcohols. An application of two-dimensional correlation analysis (2D-COS) and chemometric methods provided comprehensive information on spectral changes in the data set. Principal component analysis (PCA) and cluster analysis evidenced that the spectra of methanol, ethanol, and 1-propanol are noticeably different from the spectra of higher 1-alcohols. The similarity between the spectra increases with an increase in the chain length. Hence, the most similar are the spectra of 1-nonanol and 1-decanol. Two-dimensional hetero-correlation analysis is very helpful for identification of the origin of bands and may guide selection of the best spectral ranges for the chemometric analysis. As shown, normalization of the spectra pronounces the intensity changes in various spectral regions and provides information not accessible from the raw data. The spectra of alcohols cannot be represented as a sum of the CH3, CH2, and OH group spectra since the OH group is involved in the hydrogen bonding. As a result, the spectral changes of this group are nonlinear and its spectral profile cannot be properly resolved. Finally, this work provides a lot of evidence that the degree of self-association of 1-alcohols decreases with the increase in chain length because of the growing meaning of the hydrophobic interactions. For butyl alcohol and higher 1-alcohols the hydrophobic interactions are more important than the OH OH interactions. Therefore, methanol, ethanol, and 1-propanol have unlimited miscibility with water, whereas 1-butanol and higher 1-alcohols have limited miscibility with water.


2002 ◽  
Vol 56 (12) ◽  
pp. 1562-1567 ◽  
Author(s):  
Young Mee Jung ◽  
Hyeon Suk Shin ◽  
Seung Bin Kim ◽  
Isao Noda

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.


2017 ◽  
Vol 96 ◽  
pp. 59-69 ◽  
Author(s):  
Caiping Xi ◽  
Shuning Zhang ◽  
Gang Xiong ◽  
Huichang Zhao ◽  
Yonghong Yang

2012 ◽  
Vol 85 (18) ◽  
Author(s):  
R. P. Kurta ◽  
M. Altarelli ◽  
E. Weckert ◽  
I. A. Vartanyants

Sign in / Sign up

Export Citation Format

Share Document