Baseline, spectroscopy

Until fairly recently, IR spectroscopy was scarcely used in quantitative analysis owing to its many inherent shortcomings (e.g. extensive band overlap, failure to fulfil Beer s law over wide enough concentration ranges, irreproducible baselines, elevated instrumental noise, low sensitivity). The advent of FTIR spectroscopy, which overcomes some of these drawbacks, in addition to the development of powerful chemometric techniques for data processing, provides an effective means for tackling the analysis of complex mixtures without the need for any prior separation of their components. 

Normal transmission IRLD can also be used to characterize polymeric fibers, although scattering can induce sloping baselines. Raman spectroscopy then becomes a convenient alternative. Rutledge et al. have recently probed the orientation in electrospun nanofibers composed of a core of Bombyx mori fibroin and an outer shell of poly (ethylene oxide) [24], The orientation values were low, less than 0.1, as is often the case in electrospun fibers. 

The extent of homogeneous mixing of pharmaceutical components such as active drug and excipients has been studied by near-IR spectroscopy. In an application note from NIRSystems, Inc. [47], principal component analysis and spectral matching techniques were used to develop a near-IR technique/algorithm for determination of an optimal mixture based upon spectral comparison with a standard mixture. One advantage of this technique is the use of second-derivative spectroscopy techniques to remove any slight baseline differences due to particle size variations. 

The use of surface-enhanced resonance Raman spectroscopy (SERRS) as an identification tool in TLC and HPLC has been investigated in detail. The chemical structures and common names of anionic dyes employed as model compounds are depicted in Fig. 3.88. RP-HPLC separations were performed in an ODS column (100 X 3 mm i.d. particla size 5 pm). The flow rate was 0.7 ml/min and dyes were detected at 500 nm. A heated nitrogen flow (200°C, 3 bar) was employed for spraying the effluent and for evaporating the solvent. Silica and alumina TLC plates were applied as deposition substrates they were moved at a speed of 2 mm/min. Solvents A and B were ammonium acetate-acetic acid buffer (pH = 4.7) containing 25 mM tributylammonium nitrate (TBAN03) and methanol, respectively. The baseline separation of anionic dyes is illustrated in Fig. 3.89. It was established that the limits of identification of the deposited dyes were 10 - 20 ng corresponding to the injected concentrations of 5 - 10 /ig/ml. It was further stated that the combined HPLC-(TLC)-SERRS technique makes possible the safe identification of anionic dyes [150],... 

As the enzyme itself is usually the focus of interest, information on the behavior of that enzyme can be obtained by incubating the enzyme with a suitable substrate under appropriate conditions. A suitable substrate in this context is one which can be quantified by an available detection system (often absorbance or fluorescence spectroscopy, radiometry or electrochemistry), or one which yields a product that is similarly detectable. In addition, if separation of substrate from product is necessary before quantification (for example, in radioisotopic assays), this should be readily achievable. It is preferable, although not always possible, to measure the appearance of product, rather than the disappearance of substrate, because a zero baseline is theoretically possible in the former case, improving sensitivity and resolution. Even if a product (or substrate) is not directly amenable to an available detection method, it maybe possible to derivatize the product with a chemical species to form a detectable adduct, or to subject a product to a second enzymatic step (known as a coupled assay, discussed further later) to yield a detectable product. But, regardless of whether substrate, product, or an adduct of either is measured, the parameter we are interested in determining is the initial rate of change of concentration, which is determined from the initial slope of a concentration versus time plot. 

In spectroscopy applications, a hrst derivative effectively removes baseline offset variations in the spectral prohles. Second-derivative pretreatment resnlts in the removal of both baseline offset differences between spectra and differences in baseline slopes between spectra. Its historical effectiveness in NIR diffuse reflectance applications snggests that baseline slope changes are common in these applications, although there is no theoretical basis for snch variations. 

However, the benefit of EMSC over MSC is the ability to explicitly use prior knowledge of the spectroscopy and chemistry of the problem to both (a) enable better estimates of the baseline offset and multiplicative effects in the spectra, and (b) enable filtering/removal of spectral effects that are known or suspected to be irrelevant to the problem. In practice, the challenge in using EMSC effectively in PAT applications is often in the determination of spectral profiles to use in H, P and S. For example, S and P can be populated with measured, estimated, or even library spectra corresponding to relevant pure components and irrelevant... 

Bogen, D. C., et al, Baseline Precipitation Chemistry Measurements by Ion Chromatography, paper 361, Pittsburgh Conference on Analytical Chemistry and Applied Spectroscopy, Cleveland, Ohio (1978). 

We have shown that the radiant flux spectrum, as recorded by the spectrometer, is given by the convolution of the true radiant flux spectrum (as it would be recorded by a perfect instrument) with the spectrometer response function. In absorption spectroscopy, absorption lines typically appear superimposed upon a spectral background that is determined by the emission spectrum of the source, the spectral response of the detector, and other effects. Because we are interested in the properties of the absorbing molecules, it is necessary to correct for this background, or baseline as it is sometimes called. Furthermore, we shall see that the valuable physical-realizability constraints presented in Chapter 4 are easiest to apply when the data have this form. 

Many of the most effective constraints set well-defined limits to the data function (or its spectrum) beyond which the correct function is not allowed to go. An important example of this type of constraint is nonnegativity, whereby the correctly restored function is not allowed to extend below the zero baseline and thereby take on nonphysical negative values. This is an appropriate constraint for spectroscopy and optical images. A further example of the constraints of fixed limits is that of an upper bound to the values of the restoration. Another important constraint of this type is that of finite extent, for which no deviations from zero are allowed for the spatial function over those intervals on the spatial axis that lie outside the known... 

FTIR-spectroscopy was performed in a Nicolet 5 SXC instrument using KBr pellets. The spectra were baseline corrected and the 1125 cm-1 band was used as reference peak to compare bleached and unbleached spectra. The 1505 cm-1 peak was used as the reference peak to obtain the subtraction spectra. 

The main limitation of this model [6,14] is that it assumes that the measured response at a given sensor is due entirely to the constituents considered in the calibration step, whose spectra are included in the matrix of sensitivities, S. Hence, in the prediction step, the response of the unknown sample is decomposed only in the contributions that are found in S. If the response of the unknown contains some contributions from constituents that have not been included in S (in addition to background problems and baseline effects), biased predicted concentrations may be obtained, since the system will try to assign this signal to the components in S. For this reason, this model can only be used for systems of known qualitative composition (e.g. gas-phase spectroscopy, some process monitoring or pharmaceutical samples), in which the signal of all the pure constituents giving rise to a response can be known. For the same reason, CLS is not useful for mixtures where interaction between constituents or deviations from the Lambert-Beer law (nonlinear calibration curves) occur. 

Figure 17.1 Thermal unfolding of bamase measured by calorimetry and spectroscopy. The heat capacity of bamase (trace A) was measured using differential scanning calorimetry with a baseline (trace B) of buffer versus buffer Tm is 310.9 0.01 K, A D-N(cai) = 98.4 0.2kcal/mol, and A//D N(vh) = 98.1 0.3 kcal/mol. The ellipticity at 230 nm in the circular dichroism (trace C) under identical conditions fits Tm = 310.5 0.1 K, and A//D N(vh) = 93 3 kcal/mol (equation 17.5).

In other cases, a baseline corrected peak height for a particular absorber may be employed as a term in the equation. In such a case, the wavelength difference on either side of a peak maximum will affect the contribution of that complex term. That increment or gap, in fact, under such circumstances becomes a part of the calibration. It is as important a contribution to the calibration as the coefficients on the wavelength terms. In this correlation spectroscopy, classical band assignments are not always possible. Little specific near-infrared literature exists in advance of most applications and it is not always possible to predict which wavelengths will produce the best linearity and the best sensitivity for a given analytical problem. In the empirical approach a variety of statistical treatments have been attempted. By far the most... 

---