Baseline, spectroscopic

The long-baseline spectroscopic techniques require path lengths in air of 3-20 km, which... 

In some cases a principal components analysis of a spectroscopic- chromatographic data-set detects only one significant PC. This indicates that only one chemical species is present and that the chromatographic peak is pure. However, by the presence of noise and artifacts, such as a drifting baseline or a nonlinear response, conclusions on peak purity may be wrong. Because the peak purity assessment is the first step in the detection and identification of an impurity by factor analysis, we give some attention to this subject in this chapter. 

The NIR spectrum depends not only on the chemical composition of the sample, but also on some physical properties such as the size, form, particle distribution and degree of compression of the sample. This is useful in some cases as it enables the spectroscopic determination of some physical parameters of the sample. However, physical differences can lead to multiplicative effects in the spectrum, which, together with other additive effects such as baseline shift or chemical absorption, can complicate calibration models and detract from the quality of the results of quantitative analyses if not properly understood and accounted for. 

In practice, spectroscopic data have sloping baselines because the signals of the denatured and native states change approximately linearly with [denaturant], and so these factors must be introduced into the equation for fitting data (M. M. Santoro and P. Bolen, Biochemistry 27, 8063 (1988)). The author prefers the use of the equation... 

In this section, we present the development of an automated protocol for prostate tissue histology [164] from infrared spectroscopic imaging data as an example of the techniques described (Fig. 8.11). The data is three dimensional with x-y—axes representing the image plane and the 2-axis representing the spectral dimension. After data acquisition, two important pre-processing steps, namely baseline correction and de-noising, are performed. Since the entire data set is derived from human tissue samples, the spectra have similar characteristics and, therefore, a manually chosen set of pre-defined wave number could be used as the reference points for baseline correction. It is... 

Chemometric techniques have gained enormous significance in the treatment of spectral information by virtue of their ability to process the vast amount of data produced by modern instruments over short periods of time with a view to extracting the information of interest they contain and improving the quality of the results. In some cases, the operator is unacquainted with the chemometric techniques (spectral smoothing, baseline drift correction) embedded in the software used by the instrument in others, the chemometric tools involved are inherent in the application of the spectroscopic technique concerned (e.g. in NIR spectroscopy) and thus indispensable to obtaining meaningful results. 

Spectral subtraction can also be used to enhance the more subtle differences between two samples by the nulling of spectral features common to two spectra, which leaves the differences between samples as excursions from an otherwise featureless baseline. Interpretation of such difference spectra is usually done in conjunction with measurement of other spectroscopic changes, such as band shifts or intensity changes, that are produced in a series of spectra of a bulk phase sample perturbed in some manner. Examples will be discussed further below in the various subsections. 

There is a caveat in using Eq. (21.34), illustrated by the simulated titration curve at 10 [iM Mg2+ (Fig. 21.5). This titration curve shows a significant fraction of folded RNA in the absence of Mg2+, where. C0bs 0.15. Spectroscopic titrations and most other methods used to assess Kohs assume a baseline value of 9 = 0 when [Mg2 = 0 and normalize the titration curve accordingly. Two of the calculated curves in Fig. 21.5 have been fit to a modified Hill equation that incorporates this assumption. The apparent value of Ar 2+ at the midpoint of one curve (1.99) is larger than the value used to calculate the titration curve (1.45 see legend to Fig. 21.5). 

In most implementations of PLS it is conventional to centre both the x and c data, by subtracting the mean of each column before analysis. In fact, there is no general scientific need to do this. Many spectroscopists and chromatographers perform PCA uncentred however, many early applications of PLS (e.g. outside chemistry) were of such a nature that centring the data was appropriate. Much of the history of PLS in analytical chemistry relates to applications in NIR spectroscopy, where there are specific spectroscopic problems, such as due to baselines, which, in turn would favour centring. However, as generally applied to analytical chemistry, uncentred PLS is perfectly acceptable. Below, though, we review the most widespread implementation for the sake of compatibility with the most common computational implementations of the method. 

In many spectroscopic techniques, it is not unusual to encounter baseline offsets from spectrum to spectrum. If present, these kinds of effects can have a profound effect on a PCA model by causing extra factors to appear. In some cases, the baseline effect may consist of a simple offset however, it is not uncommon to encounter other kinds of baselines with a structure such as a gentle upward or downward sloping line caused by instrument drift, or even a broad curved shape. For example, in Raman emission spectroscopy a small amount of fluorescence background signals can sometimes appear as broad, weak curves. 

Furusjo, E. and Danielsson, L.-G., Target testing procedure for determining chemical kinetics from spectroscopic data with absorption shifts and baseline drift, Chemom. Intell. Lab. Syst., 2000, 50, 63-73. 

Chemometrics finds widespread use in spectroscopy, and there are a number of reviews that describe advances in this area. In a review by Geladi [41], some of the main methods of chemometrics are illustrated with examples. A series of three reviews addresses the topic of chemometrics in spectroscopy [42-44], Part 1 has 199 references and focuses specifically on chemometric techniques applied to spectroscopic data [42], Part 2 has 68 references and focuses on data-preprocessing methods and data transformations aimed at reducing noise, removing effects of baseline offsets, and filtering to remove noise [43], Part 3 focuses on multiway methods of analysis applied to spectroscopic data [44],... 

Since T2 is readily determined from time-domain CARS with high accuracy (<2%), a combined analysis of frequency- and time-domain data was proposed and demonstrated (45), plotting the spontaneous Raman data in normalized frequency units, Aa> x T2 (note abscissa scale of Fig. 7b). In this way the bandshape only depends on the ratio rc/T2, and only this ratio has to be deduced from the wings of the Raman band. With respect to the experimental uncertainties (ordinate value of the baseline, overlap with neighboring combination tones), the approach is more reliable than the determination of two quantities, rc and T2, from the spectroscopic data. 

Such a regeneration of the CIP from the radical pair accords with the invariance of the charge-transfer spectra even on prolonged irradiation. Back electron transfer from the radical pair is also supported in the time-resolved spectroscopic studies by the restoration of the transient absorbances to the original baselines in Fig. 3. 

Kinetics of e -h recombination may depend on its mode if one electron is excited and this is recombined with h , the recombination rate obeys the first-order rate law, while if multiple e -h+ appears at the same time within a photocatalyst particle, the rate obeys the second-order rate law. Actually, in a femtosecond pmnp-probe diffuse reflection spectroscopic analysis of tita-nia samples, photoabsorption at 620 nm by trapped electrons showed second-order decay with a component of baseline as follows ... 

In this method, the vessel is filled with a solute, and a supercritical fluid continuously flows through the vessel. Mild flow rate is used so that the outlet stream is assumed to reach equilibrium, which is then analyzed for the solute concentration by chromatographic, spectroscopic, gravimetric, dielectric, and other techniques. The apparatus used by Bristow et al. (2001) is shown in Figure 9. Here, solute is filled in the sample vessel. Continuous flows of supercritical fluid or a fluid mixture with cosolvent are maintained. For on-line analysis, such as using UV detector, the fluid mixture is bypassed directly to the analyzer for baseline correction, and then the fluid is allowed to pass through the sample vessel. For off-line analysis, the sample is collected in the solvent or cold trap for a given period of time (f), and then analyzed for solute amount. Solubility is calculated as... 

Infrared spectroscopy has been used to monitor the kinetics of the sol-gel reaction by Prassas and Hench (18). In particular, the time-dependent behavior of bands they observed at 1060 and 950 cm, assigned to the asymmetric Si-O-Si stretch and Si-OH vibrations, respectively, provides on interpretive baseline for the infrared spectroscopic studies of the more complex sol-gel reaction that takes place within the polymer matrix as reported in our work. It was reported that, as the reaction proceeds, the 1060 cm peak shifts to lower wave numbers, with new bands appearing in its neighborhood, and that the appearance of cyclic structures in the later stages is evidenced by a band at around 1080 cm. ... 

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