Data processing steps

Fig. 5.2 (continued) Data processing steps with 2D NMR raw data. 

Fig. 8.2. Overview of data processing steps and classification methods discussed in this chapter...

Therefore, using these hypothetical assumptions it is possible to remove unwanted false positives from the data processing step. However, if a compound contains... 

The very existence of the powder diffraction pattern, which is an experimentally measurable function of the crystal structure and other parameters of the specimen convoluted with various instrumental functions, has been made possible by the commensurability of properties of x-rays and neutrons with properties and structure of solids. As in any experiment, the quality of structural information, which may be obtained via different pathways (two possibilities are illustrated in Figure 2.62 as two series of required steps), is directly proportional to the quality of experimental data. The latter is usually achieved in a thoroughly planned and well executed experiment as will be detailed in Chapter 3. Similarly, each of the data processing steps, which were described in this chapter and are summarized in Figure 2.62, requires knowledge, experience and careful execution, and we will describe them in practical terms in Chapters 4 through 7. 

Figure 4.1. The flowchart illustrating common steps employed in a structural characterization of materials by using the powder diffraction method. It always begins with the sample preparation as a starting point, followed by a properly executed experiment both are considered in Chapter 3. Preliminary data processing and profile fitting are discussed in this chapter in addition to common issues related to phase identification and analysis. Unit cell determination, crystal structure solution and refinement are the subjects of Chapters 5,6, and 7, respectively. The flowchart shows the most typical applications for the three types of experiments, although any or all of the data processing steps may be applied to fast, overnight and weekend experiments when justified by their quality and characterization goals.

FIGURE 3 Typical metabolomics workflow. Biological samples are quenched, for example, with liquid nitrogen to stop enzymatic reactions. Afterward, they are extracted with a suitable solvent. For different analytical methods, further processing steps like SPE or solvent exchange to deuterated solvents for NMR are needed. After measurement, different data processing steps are needed to yield a suitable data matrix for downstream analysis. 

Figure 10.8.2 Schematic diagram showing apparatus and data-processing steps used in on-line Fourier analysis of ac voltammetric data. The steps in the large dashed box are carried out in a computer, usually by the fast Fourier transform (FFT) algorithm (see Section A.6).

Fig. 29.1. VLSPS workflow. The lefthand stack of spiral CT images represent data acquisition the pink part in the center of the figure outlines the different data processing steps. The blue part on lower right corner symbolizes the quantitat ive results generated by the VLSPS, and all parts together lead to a decision regarding the best-suited therapeutic approach...

Kinetic dependences of integral spectra were received for all samples of powders. All spectra were treated identically. Data processing steps for different samples are presented in Figures 3 and 4. All spectra were normalized to values at start time-pwint. 

The sum of costs required to operate the sampie preparation, anaiysis, and data processing steps of a measurement. 

All data preprocessing was carried out by software written in-house in 64-bit MATLAB (The Mathworks, Natick, MA, USA) for the automatic analysis of an entire data set at one time. The sequence of data processing steps for each of the 409,600 spectra data set is summarized later. A more detailed discussion of all steps involved can be found in the literature [16]. 

---