Classification Correlation

Amidon, G. L., Lennernas, H., Shan, V. P., Crison, J. R. A. A theoretical basis for a pharmaceutic drug classification correlation of in vitro drug product dissolution and in vivo bioavailability. Pharm. Res. 1995, 12, 413-420. 

This classification correlates with the chemical value chain and the product tree. Products produced in early stages of the product value chain are rather commodity-type products, while products produced in the very late stage of the value chain are rather specialty-type products. Commodity and specialty classification is often not straight-forward and can depend on a set of characteristics as shown in table 6 ... 

The grouping of solvents into classes with common characteristics can be useful in focusing attention on features that may play a role in experimental solvent effects. Reichardt s review of classification schemes is thorough (Reichardt, 1988). It is remarkable that solvent classification correlates strongly with the chemist s intuition. The new direction of the science demands that new properties be incorporated into mundane practices. These will include safety properties and environmental properties, as well as chemical properties. 

It has been shown in a series of experiments 3> that illuminated dyes can be classified into - and -type photoconductors. This classification correlates in many cases with the structure of the dyes. 

German, in 1947. A list of the PB numbers of these 22 volumes is available from the OTS (22). The abstracts are grouped by the German Patent Office classification in very broad classes. A subject outline of the classification correlated with the PB volumes and pages on which the abstracts of the patents occur has been prepared ( 3). The German patent classification is also given in an index published by the Special Libraries Association (33). 

Cryer HM, Miller FB et al. (1988) Pelvic fracture classification correlation with hemorrhage. J Trauma 28(7) 973-80... 

In the past [4-6] it was common to characterize amphiphiles according to their major performance in food systems (1) emulsification and stabilization, (2) protein interactions, (3) polysaccharide complexation, (4) aeration, and (5) crystal structure modification of fats. Such classifications correlate the surfactant chemical structure to its interaction (chemical or physical) with substrates such as fats, polysaccharides, and proteins. It was confirmed fhat certain surfactants interact molecularly with macromolecules, forming complexes and/or hybrids, and alter the macromolecular behavior at the interface. Such activity is an important new contribution of cosurfactants to the surface performance of other surfactants [7]. Such interactions are sometimes a very important contribution of amphiphiles to food systems. 

The possibilities for the application for neural networks in chemistry arc huge [10. They can be used for various tasks for the classification of structures or reactions, for establishing spcctra-strncturc correlations, for modeling and predicting biological activities, or to map the electrostatic potential on molecular surfaces. 

Neural networks have been applied to IR spectrum interpreting systems in many variations and applications. Anand [108] introduced a neural network approach to analyze the presence of amino acids in protein molecules with a reliability of nearly 90%. Robb and Munk [109] used a linear neural network model for interpreting IR spectra for routine analysis purposes, with a similar performance. Ehrentreich et al. [110] used a counterpropagation network based on a strategy of Novic and Zupan [111] to model the correlation of structures and IR spectra. Penchev and co-workers [112] compared three types of spectral features derived from IR peak tables for their ability to be used in automatic classification of IR spectra. 

In 1963 a classification of coals by rank (differing from the ECE scheme) was pubUshed by the International Committee for Coal Petrology (Table 2) (9). This includes a classification of brown coal that correlates a number of important properties including the percent reflectance of vitrinite in the coal. This is a simpler version of that used in German practice, which further subdivides soft brown coals into foHaceous and earthy. Most brown coals belong to the latter group. 

Some additional methods of classification are under development that center on the use of lignite for combustion in utihty boilers or electric power generation. Correlations based on the sodium concentration in the lignitic ash (10), or soluble A1 concentration (11) are used. The classifications are often given in terms of the severity of boiler fouling. 

Often the goal of a data analysis problem requites more than simple classification of samples into known categories. It is very often desirable to have a means to detect oudiers and to derive an estimate of the level of confidence in a classification result. These ate things that go beyond sttictiy nonparametric pattern recognition procedures. Also of interest is the abiUty to empirically model each category so that it is possible to make quantitative correlations and predictions with external continuous properties. As a result, a modeling and classification method called SIMCA has been developed to provide these capabihties (29—31). 

There is Httle correlation between classifications according to chemical type and appHcation properties. AppHcation classifications are of most practical usefulness to the dyer, and therefore the chemical constitutions of dyes are described here only briefly. Further detailed information on dye types (10) and their chemical manufacture (11) can be found elsewhere, and ia many other Eniyclopedia articles to which references are made. 

In batch classification, the removal of fines (particles less than any arbitrary size) can be correlated by treating as a second-order reaction K = (F/Q)[l/x(x — F)], where K = rate constant, F = fines removed in time 0, and x = original concentration of fines. 

For group B and D particles, nearly all the excess gas velocity (U — U,nj) flows as bubbles tnrough the bed. The flow of bubbles controls particle mixing, attrition, and elutriation. Therefore, ehitriation and attrition rates are proportional to excess gas velocity. Readers should refer to Sec. 17 for important information and correlations on Gel-dart s powder classification, minimum fluidization velocity, bubble growth and bed expansion, and elutriation. 

Comparisons (49) of measured concentrations of SFg tracer released from a 36-m stack, and those estimated by the PTMPT model for 133 data pairs over PasquiU stabilities varying from B through F, had a linear correlation coefficient of 0.81. Here 89% of the estimated values were within a factor of 3 of the measured concentrations. The calculations were most sensitive to the selection of stability class. Changing the stability classification by one varies the concentration by a factor of 2 to 4. 

An orbital correlation diagram can be constructed by examining the symmetry of the reactant and product orbitals with respect to this plane. The orbitals are classified by symmetry with respect to this plane in Fig. 11.9. For the reactants ethylene and butadiene, the classifications are the same as for the consideration of electrocyclic reactions on p. 610. An additional feature must be taken into account in the case of cyclohexene. The cyclohexene orbitals tr, t72. < i> and are called symmetry-adapted orbitals. We might be inclined to think of the a and a orbitals as localized between specific pairs of carbon... 

To demonstrate the excellent correlation (r- = 0.99) between the luminance of the images and molecular diversity, we plotted the luminance values of the map versus the mean similarity values of data sets (Fig. 4-13). From this plot, a scoring scheme for the classification of CSPs from specific to broad application range can be well established Crownpak CR > Pirkle DNBPG > Whelk > Chiralpak AD > Chiralcel OD. 

More recently, attempts have been made to correlate mathematically the chemical composition of natural waters and their aggressivity to iron by direct measurements on corrosion coupons or pipe samples removed from distribution systemsThis work has been of limited success, either producing a mathematical best fit only for the particular data set examined or very general trends. The particular interest to the water supply industry of the corrosivity of natural waters to cast iron has led to the development of a simple corrosion rig for the direct measurement of corrosion ratesThe results obtained using this rig has suggested an aggressivity classification of waters by source type i.e. 

Bush K, Jacoby GA, Medeiros AA (1995) A functional classification scheme for beta-lactamases and its correlation with molecular structure. Antimicrob Agents Che-mother 39 1211-1233... 

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