4 Spectral Analysis in the NIR Spectroscopy
65
that they yield also a base for chemometrics, quantum chemical calculation, and
2D-COS. Chemometrics has most often been employed to extract rich quantitative
and qualitative information from NIR spectra (Chap. 7). A major part of chemometrics is multivariate data analysis such as principal component analysis/regression
(PCA/PCR) and partial least squares regression (PLSR), however, self-modeling
curve resolution (SMCR), which is used to predict pure component spectra and pure
component concentration profiles from a set of NIR spectra, is also becoming more
and more significant (Chap. 7). Using quantum chemical calculations such as density
function theory (DFT) calculations, one can calculate the intensities and frequencies
of overtones and combination bands (Chap. 5). Quantum chemical calculation is still
not always popular in NIR spectroscopy but it has already been applied not only to
simple compounds but also to rather complicated molecules such as long-chain fatty
acids, nucleic acid bases, and rosemaric acid [8]. 2D-COS is not a general method
but it is often useful to unravel complicated NIR spectra (Chap. 6). In addition,
neural network, AI, and machine learning have been started to be used to analyze
NIR spectra. They are very promising methods for the spectral analysis in NIR spectroscopy (Chap. 7). However, one should know that in the early 1990 s neural network
has already been tried to be applied to NIR spectra [9].
4.2 Conventional Spectral Analysis Method
Various kinds of conventional spectral analysis methods are used in NIR spectroscopy
[1, 2, 6, 7]. They are summarized as follows:
(1) Spectral analysis based on group frequencies
This is a traditional method established in IR and Raman spectroscopy. Spectral
analysis based on group frequencies built for the fundamentals is modified for overtones and combinations. Each functional group such as OH and CH groups shows
characteristic bands in particular regions. One can find tables for group frequencies
in the NIR region in a few NIR textbooks [1, 6].
(2) Calculation of derivative spectra
Derivative methods have long been popular in various spectroscopies [1–3, 6]. They
are useful for resolution enhancement as well as baseline correction. Figure 4.2A, B
shows a good example demonstrating the usefulness of the second derivative [10]. In
Fig. 4.2A, NIR spectra in the 7500–5500 cm
−1 region are shown for water-methanol
mixtures with a methanol concentration of 0–100 wt% at increments of 5 wt% at
25 °C. Figure 4.2B, a gives an enlargement of the 6000–5700 cm
−1 region of the NIR
spectra as shown in Fig. 4.2a. In the 6000–5700 cm
−1 region, many bands due to the
overtones and combination of the CH stretching modes of CH 3 group of methanol
are expected to appear. Figure 4.2B, b displays the second derivative of the spectra in
Fig. 4.2B, a. Note that a broad feature in the 6000–5750 cm
−1 region can be divided
65
that they yield also a base for chemometrics, quantum chemical calculation, and
2D-COS. Chemometrics has most often been employed to extract rich quantitative
and qualitative information from NIR spectra (Chap. 7). A major part of chemometrics is multivariate data analysis such as principal component analysis/regression
(PCA/PCR) and partial least squares regression (PLSR), however, self-modeling
curve resolution (SMCR), which is used to predict pure component spectra and pure
component concentration profiles from a set of NIR spectra, is also becoming more
and more significant (Chap. 7). Using quantum chemical calculations such as density
function theory (DFT) calculations, one can calculate the intensities and frequencies
of overtones and combination bands (Chap. 5). Quantum chemical calculation is still
not always popular in NIR spectroscopy but it has already been applied not only to
simple compounds but also to rather complicated molecules such as long-chain fatty
acids, nucleic acid bases, and rosemaric acid [8]. 2D-COS is not a general method
but it is often useful to unravel complicated NIR spectra (Chap. 6). In addition,
neural network, AI, and machine learning have been started to be used to analyze
NIR spectra. They are very promising methods for the spectral analysis in NIR spectroscopy (Chap. 7). However, one should know that in the early 1990 s neural network
has already been tried to be applied to NIR spectra [9].
4.2 Conventional Spectral Analysis Method
Various kinds of conventional spectral analysis methods are used in NIR spectroscopy
[1, 2, 6, 7]. They are summarized as follows:
(1) Spectral analysis based on group frequencies
This is a traditional method established in IR and Raman spectroscopy. Spectral
analysis based on group frequencies built for the fundamentals is modified for overtones and combinations. Each functional group such as OH and CH groups shows
characteristic bands in particular regions. One can find tables for group frequencies
in the NIR region in a few NIR textbooks [1, 6].
(2) Calculation of derivative spectra
Derivative methods have long been popular in various spectroscopies [1–3, 6]. They
are useful for resolution enhancement as well as baseline correction. Figure 4.2A, B
shows a good example demonstrating the usefulness of the second derivative [10]. In
Fig. 4.2A, NIR spectra in the 7500–5500 cm
−1 region are shown for water-methanol
mixtures with a methanol concentration of 0–100 wt% at increments of 5 wt% at
25 °C. Figure 4.2B, a gives an enlargement of the 6000–5700 cm
−1 region of the NIR
spectra as shown in Fig. 4.2a. In the 6000–5700 cm
−1 region, many bands due to the
overtones and combination of the CH stretching modes of CH 3 group of methanol
are expected to appear. Figure 4.2B, b displays the second derivative of the spectra in
Fig. 4.2B, a. Note that a broad feature in the 6000–5750 cm
−1 region can be divided