Preprocessing baseline corrections

Calibration Design 9 samples, selected using a mixture design Preprocessing baseline correction using the average of the first 10 measurement variables. 

Tlic first set of preprocessing tools discussed are those that operate on each sample. Table 3.1 lists the four methods discus.scd normalizing, weighting, smoothing, and baseline corrections. Norntalization can be used to remove... 

Calibration design 22-sampIe mixture design Validation design leave-one-out cross-validation Preprocessing single-point baseline correction at 1100 nm Variable range 550 measurement variables 1100-2198 nra... 

The data processing can be divided into three phases. Phase 1 is the removal of poor quality spectra with an automated routine. Phase 2 is the data preprocessing of the spectra, which passed the quality test. This usually entails some type of baseline correction and normalization process. Phase 3 is multivariate image reconstruction where the spectra are classified and reproduced as color points... 

The previously discussed standardization methods require that calibration-transfer standards be measured on both instruments. There may be situations where transfer standards are not available, or where it is impractical to measure them on both instruments. In such cases, if the difference between the two instruments can be approximated by simple baseline offsets and path-length differences, preprocessing techniques such as baseline correction, first derivatives, or MSC can be used to remove one or more of these effects. In this approach, the desired preprocessing technique is applied to the calibration data from the primary instrument before the calibration model is developed. Prediction of samples from the primary or secondary instrument is accomplished simply by applying the identical preprocessing technique prior to prediction. See Section 5.9 for a brief overview of preprocessing methods and Chapter 4 for a more detailed discussion. A few methods are briefly discussed next. 

A number of reviews describe the various steps involved in NMR data analysis (2,99—101). In general, two major approaches are used for statistical analysis of NMR data a chemometric approach as well as a quantitative metabolomics approach (102,103). The chemometric approach uses the complex NMR spectra directly for analysis after subjecting the data to preprocessing steps such as baseline correction, peak alignment, solvents peak removal, and normalization. Often binning and data scaling are used to account for small peak shifts and better emphasize the low-intensity peaks, respectively. Subsequently, metabolite features that distinguish sample classes are identified and their metabolite identities are established. While this direct approach... 

In Szymanska et al. (45), PCA was performed on electrophoretic data of urinary nucleoside profiles, in order to distinguish profiles of healthy controls from cancer patients. Prior to PCA, the data were preprocessed using baseline correction, COW, and normalization according to creatinine concentration. After adequate preprocessing, PCA allowed us to reveal data structure and to evaluate differences between the healthy controls and the cancer patient profiles. 

Chemometric techniques, which can easily cope with this type of data by the use of matrices, will maximize the benefit of the multivariate character. These calculation techniques require that corresponding data points (for instance the top of a peak) in different electropherograms are located in the same column of the matrix. As a consequence, preprocessing the CE data is recommended. Peak shifts are commonly corrected with warping techniques, for example, COW, while column centering, normalization, baseline correction, and MSC are also frequently performed preprocessing techniques. 

The success of a library search in analytical chemistry depends very much on the data representation. Different kinds of data (numerical, textual, spectral, structural) require appropriate treatments. Usually, spectral data collected from the instruments are not in a form suitable for direct input either into a collection or as a query for a library search. Once the spectrum is in the computer, a number of preprocessing steps (e.g., smoothing, baseline correction, normalization, peak and intensity detection, reduction of the measurement space, autocorrelation, deconvolution, shape tracing, etc.) should be applied in order to bring the data into a standardized format... 

However, there are some methods that are reasonably automated to be used as part of a calibration model. The list of baseline correction methods presented in the following section is not exhaustive, and there are many other ways of autocorrecting the spectrum baseline as a chemometric preprocessing step. 

What type of data analysis will be needed It could be simple band area or band ratio analyses, curve-fitting analysis, or sophisticated chemometric approaches. In this step, data preprocessing steps can be identified and implemented for baseline correction, scaling and so forth, (see Chapter 7 for a further discussion on this subject.)... 

The spectra for the 29 training set samples are shown in Figure 4.50 (the baseline is corrected at 1600 nm and the classes are offset for claritjO Ideally, the spectra for each sample within a class would overlay and the features would-be different between the classes. In Figure 4.50, there appears to be significant within-class variation, which is addressed in the next section. No unusual samples are observed, but this finding is reevaluated after preprocessing is applied. 

Phase 2 - data preprocessing. There are many ways to process spectral data prior to multivariate image reconstruction and there is no ideal method that can be generally applied to all types of tissue. It is usual practice to correct the baseline to account for nonspecific matrix absorptions and scattering induced by the physical or bulk properties of the dehydrated tissue. One possible procedure is to fit a polynomial function to a preselected set of minima points and zero the baseline to these minima points. However, this type of fit can introduce artifacts because baseline variation can be so extreme that one set of baseline points may not account for all types of baseline variation. A more acceptable way to correct spectral baselines is to use the derivatives of the spectra. This can only be achieved if the S/N of the individual spectra is high and if an appropriate smoothing factor is introduced to reduce noise in the derivatized spectra. Derivatives serve two purposes they minimize broad... 

Compared to spectra obtained in the mid-infrared region, NIR spectra contain fewer, less resolved, peaks. Due to scattering and other effects, a set of NIR spectra on similar samples often exhibits constant baseline offsets from one to the next. To eliminate these baseline offset differences, reduce (but not eliminate) scattering effects, and increase the resolution of neighboring peaks, first- or second-deriva-tization is often applied to NIR spectra prior to their use in calculations. Other preprocessing techniques, such as standard normal variate (SNV) or multiplicative scatter correction (MSC), may be applied to more effectively reduce scattering effects that arise from particle size differences among samples.36... 

Brown, C.D., Vega-Montoto, L., and Wentzell, P.D., Derivative preprocessing and optimal corrections for baseline drift in multivariate calibration, Appl. Spectrosc., 54, 1055-1068, 2000. 

This is a commonly used preprocessing in image analysis that works better if it is performed after image denoising. In this way, the baseline is better defined and can, as a consequence, be better subtracted. Again, a typical basehne correction. 

Whittenburg showed that BPT can also be used as a preprocessing technique. Instead of directly using BPT for signal parameter determination, they first used it to correct the first data point which, when affected by experimental imperfections, gives rise to baseline roll. This in turn makes it possible to improve spectral parameters determination using BPT - as the authors did - or standard techniques. Kotyk et presented the second part of their comparison between DFT and BPT in the case of truncated FIDs. The conclusions are similar in that the second shows superiority in terms of precision and accuracy of the measurement of both frequencies and amplitudes. 

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