Two-Dimensional Correlation of Fourier Transform Near-Infrared and Fourier Transform Raman Spectra I: Mixtures of Sugar and Protein

1996 ◽  
Vol 50 (4) ◽  
pp. 467-475 ◽  
Author(s):  
William Fred McClure ◽  
Hisashi Maeda ◽  
Jian Dong ◽  
Yongliang Liu ◽  
Yukihiro Ozaki

Two-dimensional (2D) correlation of near-infrared (NIR) and Raman spectra was carried out for mixtures of protein (lysozyme) and sugar (sucrose) to investigate the potential of this technique for qualitative NIR spectral interpretation. Cross-correlation by least-squares was employed to assess changes in both sets of spectra which result from changes in the set of sample spectra. Fourier transform (FT) NIR and NIR-excited FT-Raman spectra were measured for each of the samples under the same conditions, and point-for-point 2D cross-correlation was calculated. In this technique, each wavenumber in the NIR region gives rise to a sliced Raman spectrum where each data point is correlated to the NIR wavenumber, while each wavenumber in a Raman spectrum provides a sliced NIR spectrum in which each data point is correlated to the Raman wavenumber. For example, choosing NIR wavenumbers 7272, 6960, 6324, and 4812 cm−1 gives sliced Raman spectra with features attributable to sucrose, while choosing NIR wavenumbers at 8424, 5148, 5052, and 4584 cm−1 provides slices with distinct lysozyme features. Therefore, the technique permits the determination of the most probable origin of NIR signals by connecting NIR spectra, which have rather broad and overlapped bands, to Raman spectra consisting of sharp and clearly separated bands. It is also possible to produce sliced NIR spectra of lysozyme and sucrose by properly selecting wavenumbers in their Raman spectra. The NIR slices explain which wavenumbers in the NIR region are correlated to lysozyme or to sucrose. Thus, 2D correlation spectroscopy helps explain the reasons why certain wavenumbers are selected in a chemometric calibration model.

1996 ◽  
Vol 4 (1) ◽  
pp. 139-152 ◽  
Author(s):  
F.E. Barton ◽  
D.S. Himmelsbach ◽  
D.D. Archibald

Two-dimensional correlation spectroscopy across the near infrared (NIR) and mid-infrared (MIR) regions have been used to explain the NIR spectra of hard red winter and spring wheat and provide additional confidence in analytical models developed with empirical data. Recent studies have shown that the major C–H stretching vibrations and some of the aromatic C–H and ring stretching vibrations and the minor vibrations in the “fingerprint” region are correlated also. The technique has been expanded to include Raman spectra. The Raman spectra were enhanced with Maximum Likelihood methods to improve signal-to-noise (S/N) while maintaining resolution. This was necessary to eliminate the effects of fluorescence which degrades S/N. The use of NIR lasers at 1.1 μm generally eliminates fluorescence as a problem, but it is still quite prevalent in agricultural materials. The original study did not show any significant correlations to aromatic functionality. However, the band at 1552 nm correlates to the Raman and not to the MIR. This band has shown up in NIR spectroscopy models for the determination of lignin, but is not readily observed in the MIR. Thus it correlates to a Raman active rather than a MIR active band. The same phenomena are observed for the amide I, II and III bands for wheat. The interesting features from NIR and MIR are that there are correlations that distinguish winter from spring wheat. These, and the Raman spectra of wheat, will be shown. These studies show that multiple regions of the electromagnetic spectrum can be, and in deed need to be, used to interpret adequately the spectral and statistical results we have traditionally obtained in the NIR.


1997 ◽  
Vol 51 (8) ◽  
pp. 1154-1158 ◽  
Author(s):  
Masahiko Shimoyama ◽  
Hisashi Maeda ◽  
Hidetoshi Sato ◽  
Toshio Ninomiya ◽  
Yukihiro Ozaki

This paper demonstrates the usefulness of near-infrared (NIR) Fourier transform (FT) Raman spectroscopy and chemometrics in nondestructive discrimination of biological materials. The discrimination among three kinds of materials—hard ivories, soft ivories, and mammoth tusks—has been investigated as an example. NIR (1064-nm) excited FT-Raman spectra were measured in situ for these materials, and principal component analysis (PCA) of the obtained spectra was carried out over the 1800–400-cm−1 region. The two kinds of ivories are clearly discriminated from one another on the basis of a one-factor plot. It was found that treatment of the Raman data by multiplicative scatter correction (MSC) greatly improves the ability to discriminate. Principal component weight loadings show that the discrimination relies upon the ratio of collagen and hydroxyapatite included in two kinds of ivories. The discrimination among the hard and soft ivories and mammoth tusks was made by a three-factor plot for FT-Raman spectra after the MSC treatments. Partial least-squares regression (PLSR) enabled us to make a calibration model which predicts the specific gravity of the hard and soft ivories.


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