Search-match software

A typical suite of X-ray diffractograms is shown in Fig. 8 for bottom ash samples. Diffraction peaks differ between sample treatments. With bottom ash, a large amorphous background signal is present. Thirty to 40 peaks are selected for analysis in the search match software. As shown in Tables 6 to 8, a number of metal phosphates were found in the treated samples and the treated and leached samples for the bottom ash, scrubber residue, and vitrification dust samples. Apatite family and tertiary metal phosphates are common to both the treated and unleached samples and the treated and leached samples for all three ashes. 

Most powder diffraction databases only serve angular dispersive X-ray diffraction. Energy dispersive X-ray diffraction data can be transformed into an angular dispersive equivalent that can then be used in conventional search-match software. Users of neutron diffraction data are currently limited to performing phase identification using a list of crystal structures imported into a Rietveld program. It is wise to first run samples destined for neutron diffraction sample in a powder XRD prior to confirm phase purity, and to use calculated patterns to assist in phase identification of possible undesired phases due to ancillary equipment or sample environment. 

Identification of any phase is done using standard data files with search match software program to identify various peaks in the diflfractogram. Applications of Powder Diffraction... 

Peak position and intensities (or areas) can be used to identify the phases present in the material (Fig. 1.9). Those parameters are typically compared with the PDF database from the International Center of Diffraction Data (a conunercial database with entries listed as Ox-xxx-xxxx, where x are numbers) [8]. Other databases are also available (such as the RRUFF Project or the FIZ Inorganic Crystal Structure Database [4]) but the magnitude of the ICDD database, with half a million entries makes it a very popular choice. Search/match software is typically used to assist in the process. A common approach is to match at least the three strongest peaks... 

In order to collect diffraction data from a sample, representative material is prepared as finely ground material and then presented to the X-ray beam in such a way that individual crystallites are randomly orientated. The resultant diffraction trace, as a plot of intensity against d spacing or against 2B value, can be compared to traces of known material or published standard reference profiles enabling identification to be made. The International Centre for Diffraction Data (www.icdd.com) publishes reference patterns on an annual basis. Sophisticated search-match software to combine diffraction peak positions with chemical constraints for the system under examination can be employed. 

Data collection software is generally supplied by the instrument manufacturers. This data collection software would also normally be equipped to perform preliminary analysis including peak finding routines and search/match of powder diffraction patterns from a database. The most popular database for powder diffraction data is maintained by the International Centre for Diffraction Data. ... 

In a modern diffractometer, computer software performs the search-match task. All PDFs of the ICDD can be stored in computer. A search-match program can find all the possible matches for a specimen. The software algorithms also can identify more than one crystalline phase in a specimen. It searches the recorded pattern with its background, and adds candidate crystalline phases together to compose, rather than decompose, an observed multiphase pattern. 

Table 17.3 Available search-match and phase identification software.

Spectral searches using a library of reference spectra can be a useful tool in identification. Search algorithms have improved over the years and now use the concept of artificial intelligence. Several software packages can be used to conduct searches in spectral libraries in which the main peaks of known compounds are encoded. The compounds offering the best matches are retained as potential candidates. Library searches involve three stages ... 

All four of the proteomic technologies mentioned above (2D-PAGE, HPLC, MS, and protein arrays) are dependent on the use of bioinformatics as a tool for data mining and elucidation. Often, MS runs generate lists of thousands of potential peptide biomarkers, and only with the help of dedicated software tools can the data be analyzed. Computer searches involving databanks of peptides and proteins are used to compare the lists of masses of the proteolytic peptides to theoretical proteolytic products. Matches between the observed mass and the calculated mass can serve as a way of identifying proteins of interest [28-32],... 

The third of the three adjacent peaks (29.207 minutes) is readily identified as 2-hydroxybenzaldehyde (salicylaldehyde) by comparison of the MS or IR spectrum with library data. It should be noted that the automated search routine in the MS software picked 3-and 4-hydroxybenzaldehyde as better matches than the 2-hydroxy compound, even though the 76 ion in the spectrum of the unknown is present only in the mass spectrum of the 2-hydroxy compound. However, the IR search routine correctly identified the 2-isomer. This illustrates that casual operators who rely on automated search routines for compound identification are much less likely to make errors when they have access to both IR and MS searching. 

These spectra were converted from. ras to. abs files with no change in the Y-axis scale and then used to search Sadtler libraries of commercial material using Mattson First software with a correlation coefficient search algorithm which takes into account baseline drift and differences in scaling. As can be seen the sample spectrum and the library spectrum are usually quite similar. The library spectra shown here were the best hits in each of the searches with a search correlation coefficient of >0.9 with 1.0 representing a perfect match. 

Since its first introduction in 1982, the DOCK software has been extended in several directions. The matching spheres can be labeled with chemical properties,61 and distance bins are used to speed up the search process.62,63 Recently, the search algorithm for distance-compatible matches was changed28 to the clique-detection algorithm introduced by Crippen and co-workers.55 Furthermore, several scoring functions are now applied in combination with the DOCK algorithm.64-68... 

The age of computer technology is here. The software packages currently available for computer color matching have tremendously improved over the past few years and will continue to improve dramatically as current human and computer research continues to search for those color recognition patterns that more closely mimic the human eye and brain processes. So far, the eye still has the lead, but for how much longer ... 

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