Application of FT‐Raman and FTIR measurements using a novel spectral reconstruction algorithm

Abstract

Recently, an advanced spectral reconstruction algorithm based on information entropy was developed to identify individual compounds contained in mixture spectra without recourse to any library or any a priori knowledge. In this study, standard mixtures containing various polycyclic aromatic hydrocarbons (PAHs) and a,!-dicarboxylic acids were measured by solid-state FT-Raman and FTIR spectroscopy, and this was followed by the application of the aforementioned algorithm to recover all pure component spectra contained therein. The results demonstrate that the developed algorithm successfully recovered the spectra of individual species even though only a very limited number of mixture spectral measurements were made. FT-Raman measurements coupled with chemometric analysis provides satisfactory pure spectral results for both PAHs and a,!-dicarboxylic acids. Additionally, FT-Raman measurements together with FTIR measurements provided even better spectral recovery for some of the a,!-dicarboxylic acids when using band-target entropy minimization (BTEM). This study demonstrates that chemometric analysis without a priori information provides a promising solution and simplified approach to identify individual components from samples containing a complex composition. In particular, this is desired for a better understanding of environmental samples.

Simultaneous determination of several crystal structures from powder mixtures: the combination of powder X-ray diffraction, band-target entropy minimization and Rietveld methods

“…BTEM has been widely used to study nonreactive as well as reactive systems using Fourier transform infrared (FTIR) (Widjaja et al, 2002;Li et al, 2003), Raman (Ong et al, 2003;Sin et al, 2003) and other spectroscopies. When BTEM spectral estimates are used in conjunction with spectral predictions from density functional theory, it has been possible to identify new non-isolatable species in chemical syntheses.…”

Application of Band-Target Entropy Minimization to Infrared Emission Spectroscopy and the Reconstruction of Pure Component Emissivities from Thin Films and Liquid Samples

“…Previous work indicates the validity of this methodology in spectral reconstruction and qualitative and quantitative analysis of pure component spectra from mixture absorbance spectra taken with Fourier transform infrared (FT-IR) spectroscopy and twodimensional nuclear magnetic resonance (2D NMR), as well as mixture intensity data taken with FT-Raman, powder X-ray diffraction (PXRD), and mass spectroscopy. [16][17][18][19][20] One of the most important aspects of BTEM is its ability to deal with nonstationary signal characteristics.…”

“…As mentioned in the introduction, BTEM is a self-modeling curve resolution (SMCR) program that uses a Shannon entropy type function. [16][17][18][19][20] BTEM analysis is performed after taking the SVD of the full data set. Once the right singular vectors are obtained, model-free deconvolution of the spectra was performed one spectrum at a time.…”

Adaptive band target entropy minimization: Optimization for the decomposition of spatially offset Raman spectra of bone

“…Sasaki et al used entropy minimization to estimate pure component spectra for whole mixtures simultaneously arguing that by minimizing the information entropy for each component, you can accurately and realistically model the spectra for the entire system. Approaches using target bands and dissimilarity functions were developed by Garland and co‐workers . Building upon their initial work, they suggested the idea of one‐at‐a‐time reconstruction using both information entropy and a target band in addition to the traditional SMCR constraints of spectral and concentration nonnegativity .…”