Separation techniques data interpretation

Automated data interpretation will usually be done using some statistical or AI technique. Because statistical classifiers are similar in their use to neural networks [Sarle, 1994] we will not discuss them separately. 

Data Interpretation extends data analysis techniques to label assignment and considers both integrated approaches to feature extraction and feature mapping and approaches with explicit and separable extraction and mapping steps. The approaches in this section focus on those that form numeric-symbolic interpreters to map from numeric data to specific labels of interest. 

There have been a large number of electron spin resonance (ESR) studies of coal and coal products,(1J but a microscopic interpretation of the resulting data has been hampered by the chemical heterogeneity of the coal samples examined. While several surveys of specially selected macerals have appeared, 3), the recent evolution of maceral separation techniques - now allows detailed ESR observations to be made on coals systematically fractionated in which coal rank, maceral type, and maceral density are simultaneously distinguished. The present report surveys the behavior of a variety of ESR properties of carbon radicals in exinite, vitrinite, and inertinite macerals in a variety of coals of different rank. These data... 

The techniques of laboratory- and industrial-scale separations utilizing adsorption and ion exchange have been described comprehensively by Mantell (M3), Cassidy (C2), and Nachod (Nl). Treybal (T4) has recently provided a unified and modern chemical engineering approach to fluid-solid separation operations. The present article will treat the problems of data interpretation and apparatus design more extensively than the authors cited, and will give major emphasis to fixed-bed operations. 

Increasing reproducibility of available separation techniques and sensitivity and affordability of mass spectrometers, as well as the desire and need to automate the identification process, have caused peptide mass fingerprinting and MS/MS sequencing to gain importance and to become the method of choice for many proteomics laboratories. Several tools are available to assist users in the interpretation of mass spectrometry data. Peptldent (http //www.expasy.org/tools/peptident.html) on the ExPASy server follows the concept of the other tools from the ExPASy proteomics suite, in that it takes into account annotation available in the SWISS-PROT/TrEMBL database, in particular as post-translational modifications and processing are concerned. The user can paste peptide masses (monoisotopic or average) into the Peptldent form, but peptide mass data can also be uploaded from a file on the user s local computer. Supported file formats are .pkm ... 

Determination of uranium in soil samples can be carried out by nondestructive analysis (NDA) methods that do not require separation of uranium (needed for alpha spectrometry or TIMS) or even digestion of the soil for analysis by ICPMS, ICPAES, or some other spectroscopic methods. These NDA methods can be divided into passive techniques that utilize the natural radioactive mission (gamma and x-ray) of the uranium and progeny radionuclides or active methods where neutrons or electromagnetic radiation are used to excite the uranium and the resultant emissions (gamma, x-rays, or neutrons) are monitored. In many cases, sample preparation is simpler for these nondestructive methods but the requiranent of a neutron source (from a nuclear reactor in many cases) or a radioactive source (x-ray or gamma) and relatively complex calibration and data interpretation procedures make the use of these techniques competitive only in some applications. In addition, the detection limits are usually inferior to the mass spectrometric techniques and the isotopic composition is not readily obtainable. 

Bottom-up proteomics methods still need refinement of protocols, and improvements in the standardization and availability of bioinformatics tools for comprehensive data analysis on a routine basis. Although recent innovations in mass spec-trometric instramentation have aeeelerated the speed and sensitivity of proteome analysis (Hebert et al. 2014), further improvements can be obtained by emphasizing the optimization, simplification, and automation of sample preparation, for example, through single-tube proteomics approaches integrating all steps from cell lysis to peptide fractionation (Hughes et al. 2014 Fan et al. 2014), peptide separation techniques, and bioinformatics tools for fast, automated data interpretation for strain-level identification of cultivable bacteria and comprehensive characterization of each isolated microbial strain in the near future. 

Some surface characterization techniques, such as Raman and infrared reflectance spectroscopies (see Appendix 3), have been used extensively in corrosion experiments in some laboratories, but the techniques are not considered sufficiently universal to be discussed here. Moreover, instrument operation and data interpretation are, at the moment, sufficiently complex and specialized that production of a series of protocols to suit most corrosion situations would be difficult. Other techniques such as XRD have been used frequently for routine characterization of thick corrosion layers (often after mechanical separation from the substrate). However, XRD has not been used on films much thinner than 2-3 pm, and, where it is used, a major problem has been the inability to determine the precise location of the various phases whose XRD patterns were reflected from the surface. Grazing-incidence XRD may provide some of this depth resolution in the future. Another technique on the horizon is SPM, which is described within the major corrosion application below. 

There are many obstacles involved in making a kinetic study of even the simplest of solid-state reactions ( ). In most solid-state reactions the impenetrable barrier to obtaining satisfactory rate data is the analysis of the products. This difficulty arises because conventional solvent and chemical separation techniques are not applicable. Another experimental difficulty arises from the fact that knowledge of the defect type and concentration must be known before any quantitative interpretation may be developed as to how the defect state affects solid-state reactions. Care must be taken at all times to keep the entire investigation within the boundary conditions set up by the methods used to investigate the data. 

Comparing the mass spectra of the interaction experiments with those acquired from control experiments reveals differences that can be related to the structure of the interaction complex. Depending on the reaction, intact proteins, proteolytic peptides or both are simultaneously analyzed by MS and, optionally, MS/MS. If the sample is too complex for direct analysis, a broad range of additional means of separation are available, e.g., electrophoresis, LC, or affinity capture, which can all be efficiently combined with ESI or MALDI MS (Sects. 4.7 and 4.8). A major challenge for all three techniques described below is the data interpretation. This concerns less the identity of the resulting products than the molecular puzzle they create. Relating the observed differences between sample and control experiments to the structure of the interaction complex can be difficult, and great care is recommended before the data in hand are considered evidence for the existence of a certain structural element. 

Although spectroscopy and quantum mechanics are closely interrelated it is nevertheless the case that there is still a tendency to teach the subjects separately while drawing attention to the obvious overlap areas. This is the attitude 1 shall adopt in this book, which is concerned primarily with the techniques of spectroscopy and the interpretation of the data that accme. References to texts on quantum mechanics are given in the bibliography at the end of this chapter. 

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