Mass-based classification

Spengler, B. and Hester, A. (2008) Mass-based classification (MBC) of peptides highly accurate precnusor ion mass values can be used to directly recognize peptide phosphorylation. J. Am. Soc. Mass Spectrom., 19, 1808-1812. 

The rate and manner of delivering energy influence such important fluid-fluid parameters as the dispersed phase holdup, the interfacial area, the mass transfer coefficient (Calderbank, 1958 Nagel et al., 1972, 1973 Kastanek, 1976 Zahradnik et al., 1982 Oldshue, 1983). Thus an energy-based classification would be both useful and appropriate. In this classification, the contactors are essentially divided into three broad groups ... 

K. Varmuza, W. Werther, F. Stand, A. Kerber, and R. Laue. Computer-assisted structure elucidation of organic compounds, based on mass spectra classification and exhaustive isomer generation. Software-Entwicklung in der Chemie, 10 303-314,1996. 

Table 8.1 Typing oiS. aureus using MALDI-TOF mass spectra and a hierarchical artificial neural network (ANN) overview of the ANN classification results of the external test data set with spectra from S. aureus. The MALDI-TOF-MS-based classification was established by teaching a hierarchical ANN model with mass spectra ofS. aureus fiom six clonal complexes. With the exception of CC8, the data indicated no reliable differentiation between spectra from the individual clonal complexes of y. aureus. Reproduced from Lasch et al. (2014) with permission...

Mass spectra of chemical compounds have a high information content. This article describes computer-assisted methods for extracting information about chemical structures from low-resolution mass spectra. Comparison of the measured spectrum with the spectra of a database (library search) is the most used approach for the identification of unknowns. Different similarity criteria of mass spectra as well as strategies for the evaluation of hitlists are discussed. Mass spectra interpretation based on characteristic peaks (key ions) is critically reported. The method of mass spectra classification (recognition of substructures) has interesting capabilities for a systematic structure elucidation. This article is restricted to electron impact mass spectra of organic compounds and focuses on methods rather than on currently available software products or databases. 

Although the size separation/classification methods are adequate in some cases to produce a final saleable mineral product, in a vast majority of cases these produce Httle separation of valuable minerals from gangue. Minerals can be separated from one another based on both physical and chemical properties (Fig. 8). Physical properties utilized in concentration include specific gravity, magnetic susceptibility, electrical conductivity, color, surface reflectance, and radioactivity level. Among the chemical properties, those of particle surfaces have been exploited in physico-chemical concentration methods such as flotation and flocculation. The main objective of concentration is to separate the valuable minerals into a small, concentrated mass which can be treated further to produce final mineral products. In some cases, these methods also produce a saleable product, especially in the case of industrial minerals. 

The evidence on which this theory of stellar evolution is based comes not only from known nuclear reactions and the relativistic equivalence of mass and energy, but also from the spectroscopic analysis of the light reaching us from the stars. This leads to the spectral classification of stars, which is the cornerstone of modem experimental astrophysics. The spectroscopic analysis of starlight reveals much information about the... 

Carbide decompositions yield no volatile product and, therefore, many of the more convenient experimental techniques based on gas evolution or mass change cannot be applied. This is a probable reason for the relative lack of information about the kinetics of reaction of these and other compounds which are correctly classifed under this heading, such as borides, silicides, etc. 

A curious episode in 1973 characterizes the early time in this field. A pharmacological activity problem (discrimination between sedatives or tranquilizers) was related to mass spectral data without needing or using the chemical structures of the considered compounds (Ting et al. 1973). In a critical response (Clerc et al. 1973) it was reported that similar success rates—better than 95%—can be obtained for a classification of the compounds whether their names contain an even or odd number of characters, just based on mass spectral data Obviously, in both papers the prediction performance was not estimated properly. 

Isotopic abundances are listed either as their sum being 100 % or with the abundance of the most abundant isotope normalized to 100 %. The latter is used throughout this book because this is consistent with the custom of reporting mass spectra normalized to the base peak (Chap. 1). The isotopic classifications and isotopic compositions of some common elements are listed below (Table 3.1). A full table of the elements is included in the Appendix. 

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