Applications multi-component analysis

NMR spectroscopy is one of the most widely used analytical tools for the study of molecular structure and dynamics. Spin relaxation and diffusion have been used to characterize protein dynamics [1, 2], polymer systems[3, 4], porous media [5-8], and heterogeneous fluids such as crude oils [9-12]. There has been a growing body of work to extend NMR to other areas of applications, such as material science [13] and the petroleum industry [11, 14—16]. NMR and MRI have been used extensively for research in food science and in production quality control [17-20]. For example, NMR is used to determine moisture content and solid fat fraction [20]. Multi-component analysis techniques, such as chemometrics as used by Brown et al. [21], are often employed to distinguish the components, e.g., oil and water. 

Heil, C. Rapid, multi-component analysis of soybeans by FT-NIR spectroscopy. Thermo Fisher Scientific application note, www.thermo.com, 2010 Thermo Fisher Scientific, Madison, WI (www.thermofisher.com). 

If gas selectivity cannot be achieved by improving the sensor setup itself, it is possible to use several nonselective sensors and predict the concentration by model based, such as multilinear regression (MLR), principle component analysis (PCA), principle component regression (PCR), partial least squares (PLS), and multivariate adaptive regression splines (MARS), or data-based algorithms, such as cluster analysis (CA) and artificial neural networks (ANN) (for details see Reference 10) (Figure 22.5). For common applications of pattern recognition and multi component analysis of gas mixtures, arrays of sensors are usually chosen... 

The set of possible dependent properties and independent predictor variables, i.e. the number of possible applications of predictive modelling, is virtually boundless. A major application is in analytical chemistry, specifically the development and application of quantitative predictive calibration models, e.g. for the simultaneous determination of the concentrations of various analytes in a multi-component mixture where one may choose from a large arsenal of spectroscopic methods (e.g. UV, IR, NIR, XRF, NMR). The emerging field of process analysis,... 

The simplest IR sensor would consist of a source, a sample interface and a detector. Although quite sensitive, such an arrangement would have no selectivity as any IR absorbing substance would cause an attenuation of the detected radiation. To get the selectivity that is a main driving force behind the application of IR systems, the radiation has to be spectrally analysed. This can be accomplished either by measurement at discrete wavelengths or, for multi-component sensors or samples containing (potentially) interfering substances, by full spectral analysis of the collected radiation. 

Chapter 10 provides an exhaustive description of how these techniques can be applied to a large number of industrial alloys and other materials. This includes a discussion of solution and substance databases and step-by-step examples of multi-component calculations. Validation of calculated equilibria in multi-component alloys is given by a detailed comparison with experimental results for a variety of steels, titanium- and nickel-base alloys. Further selected examples include the formation of deleterious phases, complex precipitation sequences, sensitivity factor analysis, intermetallic alloys, alloy design, slag, slag-metal and other complex chemical equilibria and nuclear applications. 

Organophosphorus substances are often found in mixtures containing several compounds and their decomposition products. The analysis of such a multi-component sample requires the separation of the individual derivatives before identification and determination are possible. A number of techniques of general applicability are available to this end, mostly based on chromatography and mass spectrometry. In addition, methods have been developed for the analysis of individual compounds requiring no previous separation. 

Engineered variants of enzymes could be another approach in biosensor design for the discrimination and detection of various enzyme-inhibiting compounds when used in combination with chemometric data analysis using ANN. The crucial issues that should be addressed in the development of new analytical methods are the possibility of simultaneous and discriminative monitoring of several contaminants in a multi-component sample and the conversion of the biosensing systems to marketable devices suitable for large-scale environmental and food applications. 

After a discussion of the fundamental concepts in Section II, we present, in Section III, an approach to the lineshape theory of dynamic NMR spectra which comprises the most general case, namely that of a multi-component system where various intra- and inter-molecular exchange processes take place. We believe that a fully correct NMR theory of such an equilibrium has not been put forward yet. Section IV is concerned with the methods of simulation and analysis of complicated dynamic spectra. In Section V, we present our views on solving the numerous practical problems which usually appear upon the application of the theory to the analysis of dynamic spectra. 

Without any doubt the most valuable development in mass spectrometry has been multi-compound/multi-isotope analysis. This implies the application of GC-C/P-IRMS to the on-line analysis, not only of carbon but also of other isotopes, preferably of hydrogen and oxygen, in the individual components of a mixture, and the use of the metabolic and isotopic correlations obtained from such an analysis. In the course of this chapter, the potential of (positional) oxygen isotope analysis has been emphasised several times and this will still be a challenge of the future. The advantage of GC-C/ P-IRMS is its speed in performance and the very moderate demand on sample size and purity, and also its implication for automation. The information available can easily be correlated to that of other (classic) analyses. However, a disadvantage will be always that the data concern a global mean value for the whole molecule in question. 

For the more instrumental methods of quantitative chemical analysis, I have taken a rather eclectic approach, merely illustrating some aspects that are especially suitable for spreadsheet exploration, such as Beer s law and its applications to the analysis of multi-component mixtures, chromatographic plate theory, polarography, and cyclic voltammetry. 

The problem considered here is a geometrically simple one in two dimensions designed to simulate isothermal flow and reaction in a medium in which some percentage of the rock is reactive (e.g, a carbonate cement) while the remainder is treated as inert (e.g, a quartz sandstone at low temperature). The analysis presented here should apply in most respects to the case where the entire rock is reactive (e.g., a pure limestone), although in this instance the flow can no longer be treated as Darcian. The problem as formulated here is essentially the same as that considered by Ortoleva et al. (2). Although our formulation is based on a one-component system, the results should be broadly applicable to relatively simple multi-component reactions (e.g., calcite dissolution). 

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