Chemical variance

Vivo-Truyols, G., Schoenmakers, P.J. (2006). Chemical variance, a useful tool for the interpretation and analysis of two-dimensional chromatograms. J. Chromatogr. A 1120, 273-281. 

The probabilistic nature of a confidence interval provides an opportunity to ask and answer questions comparing a sample s mean or variance to either the accepted values for its population or similar values obtained for other samples. For example, confidence intervals can be used to answer questions such as Does a newly developed method for the analysis of cholesterol in blood give results that are significantly different from those obtained when using a standard method or Is there a significant variation in the chemical composition of rainwater collected at different sites downwind from a coalburning utility plant In this section we introduce a general approach to the statistical analysis of data. Specific statistical methods of analysis are covered in Section 4F. 

The following texts provide additional information about ANOVA calculations, including discussions of two-way analysis of variance. Graham, R. C. Data Analysis for the Chemical Sciences. VCH Publishers New York, 1993. 

More recently, a number of tests of chemical stabihty of the latex concentrate have been developed. Chemical stabihty variance in the raw concentrate has considerable effect on the dipping characteristics of latex compounds, and can also affect mechanical stabihty of the compound. A broad rule is that, while latex MST can be increased or decreased without necessarily affecting its chemical stabihty, any change in the latter always is reflected in the MST. A new test, in which chemical stabihty is deterrnined by measurement of the effect of weak 2inc acetate solution added to a second mechanical stabihty sample and the result contrasted with the original MST, is available to numerically quantify chemical stabihty (56). 

Equation (1) can be used in a general way to determine the variance resulting from the different dispersion processes that occur in an LC column. However, although the application of equation (1) to physical chemical processes may be simple, there is often a problem in identifying the average step and, sometimes, the total number of steps associated with the particular process being considered. To illustrate the use of the Random Walk model, equation (1) will be first applied to the problem of radial dispersion that occurs when a sample is placed on a packed LC column in the manner of Horne et al. [3]. 

A model must be introduced to simulate fast chemical reactions, for example, flamelet, or turbulent mixer model (TMM), presumed mapping. Rodney Eox describes many proposed models in his book [23]. Many of these use a probability density function to describe the concentration variations. One model that gives reasonably good results for a wide range of non-premixed reactions is the TMM model by Baldyga and Bourne [24]. In this model, the variance of the concentration fluctuations is separated into three scales corresponding to large, intermediate, and small turbulent eddies. 

The UPD and anodic oxidation of Pb monolayers on tellurium was investigated also in acidic aqueous solutions of Pb(II) cations and various concentrations of halides (iodide, bromide, and chloride) [103]. The Te substrate was a 0.5 xm film electrodeposited in a previous step on polycrystalline Au from an acidic Te02 solution. Particular information on the time-frequency-potential variance of the electrochemical process was obtained by potentiodynamic electrochemical impedance spectroscopy (PDEIS), as it was difficult to apply stationary techniques for accurate characterization, due to a tendency to chemical interaction between the Pb adatoms and the substrate on a time scale of minutes. The impedance... 

To investigate the variance structure in the raw physical/chemical data material a PCA was performed on the autoscaled Y-data. Figure 3 shows a loading plot of the Y-data as a function of the two first PC s describing together 57 % of the total variance. 

It is quite clear from Schemes 2.1-2.5 that in rubbers polymer identification and additive analysis are highly interlinked. This is at variance to procedures used in polymer/additive analysis. The methods for qualitative and quantitative analysis of the composition of rubber products are detailed in ASTM D 297 Rubber Products-Chemical Analysis [39]. 

Today, ICP-AES is an indispensable inorganic analytical tool. However, because of the high plasma temperature, ICP-AES suffers from some severe spectral interferences caused by line-rich spectra of concomitant matrix elements such as Fe, Al, Ca, Ni, V, Mo and the rare-earth elements. This is at variance with AAS. The spectral interference can of course be minimised by using a (costly) high-resolution spectrometer. On the other hand, the high temperature of the ICP has the advantage of reducing chemical interferences, which can be a problem in AAS. 

In 1968 the average selling costs were 10% of the sales dollar. There is, however, a large variance in this percentage. For basic bulk chemicals such as sulfuric acid, caustic, and soda ash the sales costs are less than 5% of the selling price, while for specialty chemicals having a low sales volume the sales costs may exceed 50% of the selling price. 

The general principles of testing chemical homogeneity of solids are given e.g. by Malissa [1973], Cochran [1977], and Danzer et al. [1979]. The terms of variation o20tal and o2nal can be separated by analysis of variance (Sect. 5.1.1). According to Danzer and Kuchler [1977] there exists an exponential dependence between the total variance and the reciprocal sample mass... 

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