Data file identifiers

Now, what can EPA or anyone else do about this elusive but real problem A start has already been made via a data quality workshop which was initiated by the CMA (Chemical Manufacturers Association) and co-sponsored by EPA, NBS (National Bureau of Standards) and NAS (National Academy of Sciences). This resulted in a group of about 40 experienced participants from government, industry and academia reviewing criteria for data quality in four areas of information relating to properties, health and environmental effects. From this beginning, we eventually hope to see the contents of data-bases or data files identified as to the level of reliability of extracted information. The user will then at least have the ability to judge the value of the information he received. 

Indexings and Lattice Parameter Determination. From a powder pattern of a single component it is possible to determine the indices of many reflections. From this information and the 20-values for the reflections, it is possible to determine the unit cell parameters. As with single crystals this information can then be used to identify the material by searching the NIST Crystal Data File (see "SmaU Molecule Single Stmcture Determination" above). 

SAIC provided much of the data used in this book from its proprietary files of previously analyzed and selected information. Since these data were primarily from the nuclear power industry, a literature search and industry survey described in Chapter 4 were conducted to locate other sources of data specific to the process equipment types in the CCPS Taxonomy. Candidate data resources identified through this effort were reviewed, and the appropriate ones were selected. Applicable failure rate data were extracted from them for the CCPS Generic Failure Rate Data Base. The resources that provided failure information are listed in Table 5.1 with data reference numbers used in the data tables to show where the data originated. 

Abstract A relatively small number of mammalian pheromones has been identified, in contrast to a plethora of known insect pheromones, but two remarkable Asian elephant/insect pheromonal linkages have been elucidated, namely, (Z)-7-dodecen-1-yl acetate and frontalin. In addition, behavioral bioassays have demonstrated the presence of a chemical signal in the urine of female African elephants around the time of ovulation. Our search for possible ovulatory pheromones in the headspace over female African elephant urine has revealed for the first time the presence of a number of known insect pheromones. This search has been facilitated by the use of a powerful new analytical technique, automated solid phase dynamic extraction (SPDE)/GC-MS, as well as by novel macros for enhanced and rapid comparison of multiple mass spectral data files from Agilent ChemStation . This chapter will focus on our methodologies and results, as well as on a comparison of SPDE and the more established techniques of solid phase microextraction (SPME) and stir bar sorptive extraction (SBSE). 

Of course one may employ automated library searches ( library percent reports ) to check for compound identities, but algorithms for library matching are not infallible, and mass spectral libraries are not exhaustive, thus some compounds of interest will likely not be identified. Additional dilemmas are presented by mere reliance on retention times and library percent reports to ascertain the presence of common or unique peaks from among multiple mass spectral data files. As illustrated in Table 2.1, the TICs from the GC-MS of urine from four elephants evidence a peak at essentially the same retention time, but the library search results are inconclusive as to their common identity or lack thereof. As will be seen below, our novel macros can assist in making such decisions for a large number of peaks. 

The 8(b) inventory accumulation was the next major activity. For a decentralized company like Monsanto or, for that matter, most major chemical companies, the experience of centralized information gathering was a new experience. However, we believe that the experience not only was novel, but proved to be beneficial from several points of view. First of all, it enabled us to evolve a network of expertise. Second it gave us a central data-base on which to build other information important from a corporate point of view, and permit a one-time expense for developing a system. Third, it revealed that we needed to improve our data files in some areas. And, fourth, it gave our central staff departments some surprises as to substance locations. We used the Chemical Abstract Service Registry Profile capabilities to gather all the known synonyms and added our internal numeric and common identifiers to access the file via dozens of possible names or numbers. 

END HEADER DATA and the end of file identifier FILE ENDS... 

X-Ray powder diffraction patterns are catalogued in the JCPDS data file,7 and can be used to identify crystalline solids, either as pure phases or as mixtures. Again, both the positions and the relative intensities of the features are important in interpretation of powder diffraction patterns, although it should be borne in mind that diffraction peak heights in the readout from the photon counter are somewhat dependent on particle size. For example, a solid deposit accumulating in a heat exchanger can be quickly identified from its X-ray powder diffraction pattern, and its source or mechanism of formation may be deduced—for instance, is it a corrosion product (if so, what is it, and where does it come from) or a contaminant introduced with the feedwater ... 

General information Product, including description (e.g., tablet, dose) method name, including revision and technique (e.g., UV) project information concerning sample(s) date of test and lot number equipment identifier raw data file name of computerized system and analyst. 

In order to better access, utilize, and interpret existing teratology and reproduction toxicity data, a comprehensive data extraction project funded by the EPA is in progress at ETIC. Specific experimental data are extracted from selected ETIC master file documents. These data are entered in approximately 60 data fields, which comprise a data extraction file record. These data fields include information such as the identification of the test agent, test animals, experimental protocol, and results. Data field identifiers are listed in Table 6. 

Identify equipment and software to access or maintain application source code and data files. This information should be included in the business continuity plans. 

Data files are automatically deleted after a hardcopy is generated. There is no requirement to identify the analyst or time/date stamping of spreadsheet hardcopies. 

Computers on the Internet are identified through IP addresses. IP addresses of the sending and receiving computers together with some other information are included in a header created for each packet. This is the reason why all packets from one data file find their way to the same computer... 

Figure 2.23 shows the diffraction spectrum of a powder sample of calcium phosphate after subtracting background. With assistance of a computer, we can identify the peak positions in the spectrum and search for a possible match between the spectrum and a PDF data file. Additional chemical information is often used to help in the search process. For example, this specimen contents Ca, P and O. The computer quickly searches for a compound containing Ca, P and O. It finds a match between the diffraction spectrum of a sample with data for hydroxyapatite (Figure 2.24). There are two important parameters in a standard data file shown in Figure 2.24 the position of diffraction (20) and relative intensities of peaks (j ), or int-f in the PDF. I is the peak intensity with the maximum value in a spectrum. The highest int-f value is 999 which should be read as 0.999 in the relative intensity. The PDF may also list the corresponding d-spacing of peaks, which are the true crystal properties.

All GCMS data files were examined with a general search procedure developed for scanning GCMS data for anthropogenic chemicals at trace levels. Hard copies of the mass spectral data were examined manually to verify computer matches and Identify compounds not selected by the computer program. Identified compounds were then quantitated by multiplying their peak area with appropriate response factors obtained from analyses of quantitative standards under Identical Instrumental tunes and... 

NONMEM For the operational qualification, a careful review of the parameters discussed in Section 2.9 of the NONMEM Users Guide—Part III (17) should be performed. These values should be identified and set during the IQ and tested properly during the QQ. The specific examples provided for NQNMEM s PREDPP, NM-TRAN, and associated library subroutines are highly recommended as a starting point for the QQ. The Phenobarbital and Theophylline data files provided with the software (18) offer even more extensive testing appropriate (with modification) for a PQ. The output is well documented and individuals may seek to modify or parameterize the examples for their needs. 

Powder X-ray diffraction patterns were obtained with a SIEMENS D-5000 diffractometer using the Ka-radiation of a copper anode. The samples were analyzed after deposition on a quartz monocrystal sample-holder supplied by Siemens. The crystalline phases were identified by reference to the ASTM data files. 

---