Peak search automatic

The crystal used for data coUection was transferred to an Enraf-Nonius CAD-4 diffractometer. Automatic peak search and indexing procedures yielded the same monoclinic cell as derived from the X-ray powder diffraction data and precession photographs. Testing showed that the cell was indeed primitive and that there was no superlattice present. Table 5 gives the crystal data and X-ray experimental parameters, and Table 6, the interatomic distances and angles. Positional and thermal parameters are given in Table S3 (Supporting Information)... 

Smoothing is a numerical conditioning procedure employed to suppress statistical noise, which is present in any powder diffraction pattern as a result of random intensity measurement errors (Eq. 3.8 in Chapter 3). It improves the appearance of the powder diffraction pattern. For example, smoothing can make quickly collected data (say in a 15 min experiment) look similar to a pattern collected in a longer (e.g. in an overnight) experiment, and may help with certain automatic procedures, such as background subtraction, Ka2 stripping and unbiased peak search. 

An automatic peak search is actually the simplest (one-dimensional) case in the more general two- or three-dimensional image recognition problem. Image recognition is easily done by a human eye and a brain but is hard to formalize when random errors are present and, therefore, difficult to automate. Many different approaches and methods have been developed two of them are most often used in peak recognition and will be discussed here. These are the second derivative method and the profile scaling technique. 

The results of an automatic peak search can be further improved by adding real and/or removing false peaks manually. An example of such a peak search is shown in Figure 4.8. 

Figure 4.8. Automatic peak search conducted using a second derivative method (top) and manually corrected reduced pattern (bottom). The upward arrow placed on the digitized pattern shows a false peak (which was eliminated manually) and the downward arrows show the missed peaks (which were added manually).

Approximate peak positions can be obtained using visual localization, or from the automatic peak search, or they may be calculated from unit cell dimensions, if the latter are known. 

Figure 4.23. The results of a qualitative analysis of a multiple phase sample. Three crystalline phases are clearly identifiable lithium silicate - Li2Si03, silicon oxide - SiOj (quartz), and a different pol)imorph of silicon oxide - tridymite. A low quality diffraction pattern collected during a fast experiment was employed in this example. The data shown on top were smoothed, the background was subtracted, and the Ktt2 components were stripped before the digitized pattern (shown below the smoothed profile) was obtained using an automatic peak search. Note, that many weak Bragg reflections were missed in the peak search,...

J. Lauterjung, G. Will and E. Hinze, A fully automatic peak-search program for the evaluation of Gauss-shaped diffraction patterns, Nucl. Instrum. Methods Phys. Res., Sect. A, 1985, 239, 281-287. 

Every crystalline phase in a sample has a unique powder diffraction pattern determined from the unit cell dimensions and the atomic arrangement within the unit cell. It can be considered a fingerprint of the material. Thus, powder diffraction can be used for phase identification by comparing measured data with diffraction diagrams from known phases. The most efficient computer searchable crystallographic database is the PDF-4 from the International Centre for Diffraction Data (ICDD) [3]. It is used by very efficient computer-based search-processes. In 2007 the PDF-4-i- database contains information about Bragg-positions and X-ray intensities for more than 450000 compounds, out of which there are about 107 500 data sets with atomic coordinates. New entries are added every year. The positions of the peaks in the measured pattern have to be determined. This can be done manually, but effective, fast and reliable automatic peak search methods have been developed. The method can obviously be successful only if the phases in the sample are included in the database. However, the database can also help to determine unknown phases if X-ray data exist for another isostructural compound albeit with a different composition. 

Figure 10.46. The Raman spectrum of sulphur in the spectral range 100-280 cm calculated by Fourier transformation of the interferogram top, no apodization (boxcar) bottom, apodization function Norton-Beer weak. In both cases, a zerofilling factor of 2 and the power spectrum for phase correction were chosen. Further parameter used Store page selected frequencies for file first 9394 and last 5894 Limit data page limit resolution to 4 cm, limit phase resolution to 32 cm, direction both, data points both Peak search page mode absolute largest value, symmetry of the interferogram automatic.

An automated standard addition technique was inqtlemented as a part of the analytical protocol. This tqtproach provides a reliable method for ronote, matrix matched Tc monitor instrument calibration. The monitor instrument was fully automated for continuous operation with software performing asynchronous device control and detector data acquisition. The data were automatically processed including a peak search and integration, as well as tiie instrument calibration via standard addition. Total analysis time was 12.S minutes per... 

Fig. 9. Modern analytical software includes automatic peak search routines and evaluation of net intensities for further quantification...

All systems use the idea of a region of interest (ROl), which the user sets up around a peak in the spectrum by visual inspection. The more elaborate systems have peak search routines that find the peaks and set the ROIs automatically. 

Many automatic baseline correction routines that may be applied without operator intervention are available. These routines may be applied by default for operations such as spectral searching. Automatic basehne functions typically use linear or polynomial baseline fits in regions of the spectmm where no peaks are detected. 

Multivariate data analysis usually starts with generating a set of spectra and the corresponding chemical structures as a result of a spectrum similarity search in a spectrum database. The peak data are transformed into a set of spectral features and the chemical structures are encoded into molecular descriptors [80]. A spectral feature is a property that can be automatically computed from a mass spectrum. Typical spectral features are the peak intensity at a particular mass/charge value, or logarithmic intensity ratios. The goal of transformation of peak data into spectral features is to obtain descriptors of spectral properties that are more suitable than the original peak list data. 

As an example of the application of gas chromatography-mass spectrometry, Fig. 1.7 shows a reconstructed chromatograph obtained for an industrial sludge. The Finnigan MAT 1020 instrument was used in this work. Of the 27 compounds searched for, 15 were found. These data were automatically quantified. This portion of the report contains the date and time at which the run was made, the sample description, who submitted the sample and the analyst, followed by the names of the compounds. If no match for a library entry was found, the component was listed as not found . Also shown is the method of quantification and the area of the peak (height could also have been chosen). 

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