Consequence matrix

Infrared spectroscopy is by far the most popular tool for the inverstigation of matrix-isolated species. By virtue of the suppression of most rotations in sohd matrices, IR spectra recorded under these conditions typically show patterns of very narrow peaks, compared to spectra obtained under normal laboratory conditions (solution, Nujol, or KBr pellets), where bands due to different vibrations often overlap to the extent that they cannot be separated. As a consequence, matrix isolation IR spectra are—at least potentially—are a very rich source of information on the species under investigation. Whether and how all this information can be used depends on the ability to assign the spectra, a subject to which we will return below. 

Determinations by both techniques can be subject to chemical and/or physical interference effects caused by the sample matrix. However, after fractionation of the sample the species are usually in a less complex matrix, a buffer or electrolyte solution. Consequently, matrix interferences effects are minimised. On the other hand, the species may be diluted in the process and this could be detrimental for the determination of species present at very low concentrations. At the present state of the art GFAAS can be used for the determination of analytes at the 1 pgl level. However, at this level contamination in the reagents and equipment limit the number of species that can be detected with confidence. 

This conclusion was only partly confirmed by scanning electron microscopy micrographs of RuC>4 stained surfaces taken at the crack tip of deformed specimens at 1ms-1, where the non-nucleated and /3-nucleated materials showed, respectively, a semi-brittle and semi-ductile fracture behavior. While some limited rubber cavitation was visible for both resins, crazes—and consequently matrix shearing—could not develop to a large extent whether in the PP or in the /1-PP matrix (although these structures were somewhat more pronounced in the latter case). Therefore, a question remains open was the rubber cavitation sufficient to boost the development of dissipative mechanisms in these resins ... 

Matrix effects, which result from the interference of LC co-eluting compounds on the ionization of analytes during the ESI process, induce either ion suppression or enhancement. The effects are matrix-dependent, and ultimately affect the LC-MS quantitative results. Several measures, which include sample extraction, clean-up, dilution, and chromatography, are mandatory and effective to reduce matrix effects. Sample extraction and/or clean-up as discussed in Chapter 4 remove the majority of endogenous compounds present in samples, but a small amount often remains in the final sample extracts. Dilution is the simplest clean-up approach and should be considered first as long as the required detection concentrations are achieved. LC or UHPLC separates analytes from some matrices, which definitely helps to reduce matrix effects. However, no matter what procedures are adopted, matrix effects may not be completely eliminated. Consequently, matrix effects need to be evaluated and compensation is made to achieve the... 

A particular case of the above reasoning is that with each of standard solutions contain only one dissolved chemical component (other than those present in the other standard solutions), so N = M. In this case notation S refers to their common value (N = M = S). Consequently, matrix Cst[S,S] of the concentrations of components in standard solutions is square and diagonal (only elements on the matrix main diagonal differ from zero) (17)). 

A representative consequence matrix is given in Table 1.9. The matrix has four levels of consequence covering worker safety, public safety, the environment, and economic loss. There are no rules as to how many levels should be selected, nor does any major regulatory body insist on a particular size of matrix. However, many companies choose four levels three levels does not provide sufficient flexibility and differentiation, but five levels imply a level of accuracy that is probably not justified. The steps in Table 1.9, from low to very severe, are roughly in... 

Once the consequences associated with an incident have been identified, the next step is to estimate the frequency with which the incident may occur. A representative frequency matrix is given in Table 1.13. As with the consequence matrix, four value levels are provided. The use of just three levels is probably too coarse, but five levels or more implies a degree of accuracy that probably could not be justified (precision is not the same as accuracy). 

As with the consequence matrix, the steps in Table 1.12 are roughly an order of magnitude greater than the one before it. 

If the incident was a near miss or of low consequence, the report should p obably explain why it was investigated in depth. The consequence matrix (Chapter 1) may be placed in this section of the report, with a discussion as to where this particular incident fits into that matrix. 

Table 3. Extract of consequence matrix after assignment of part-worth utility, alternative 1.

Table 4. Extract of final consequence matrix, alternative 1. 

In the second step of the utihty model, the input values are evaluated. For this purpose the decision matrix is transformed into a consequence matrix. So the input values are transformed into the part-worth utilities. Thus the consequence matrix consists only of the part-worth utility values. In order to perform this transformation, the utility model assumes that the decision maker has a precise idea about the utility placing to criterion s characteristics. Based on his preferences, different transformation functions can be considered, linear, concave and convex. In case the decision maker has a clear preference in respect of utility allocation, as in the present case study, the use of the utility model is possible. However specifying the preferences for all criteria and related characteristics in industrial business is not always easy (Aven 2007). On this note when clear preferences cannot be worked out, application of the utility model is not reasonable. 

Another aspect that is common to matrix interferences (direct contrihutions of matrix components to the signal measured for analyte and/or SIS) and matrix effects (suppression or enhancement of ionization) is that of the consequences of the presence of metabolites or other types of degradates when analyzing incurred analytical samples. Such interferences are in principle absent from the control matrix used for matrix matched calibrators, QC samples etc. Thus use of re-analysis of incurred samples to evaluate and consequent matrix effects was discussed in Section 9.4.7b, and applies equally to matrix interferences arising from presence of metabolites. Variations of metabolite levels among samples (e.g. from different time points in a pharmacokinetic study), which can lead to parallel variations in the extent of both matrix effects and matrix interferences, are an example of how some problems can arise unexpectedly despite prior precantions. 

In general, the furnace programs used to atomize lead are optimized with regarding to linearity of the method and background absorption. Even though furnace programs and matrix modifiers reduce matrix effects, they do not eliminate interferences. Consequently matrix-matched standards or standard addition are essential for accurate determination of lead [57,62]. Examples of recent ETAAS methodologies for blood lead determination are shown in Table 4. 

Arc excitation may be used for either qualitative or quantitative analysis. The precision obtainable with an arc is, however, generally poorer than that with a spark and much poorer than that with a plasma or flame. Furthermore, emission intensities of solid samples are highly sample dependent. As a consequence matrix matching of standards and samples is usually required for satisfactory results. The internal-standard method can be used to partially offset this problem. 

Before you can determine the extent of the consequences of each scenario, it is very important to develop a consequence matrix. In this case, the matrix in Table 14.3 illustrates the damage states from negligible to catastrophic. It indicates both the qualitative and quantitative consequences of various kinds of consequences of an uncontrolled cryo leak. 

The same method can be used to solve any number of related equations simultaneously, boiling the problem down to a single step diagonalizing the matrix. Consequently, matrix diagonalization routines comprise a key element in computer programs designed to solve problems and simulate processes in virtually every realm of chemistry and physics. 

Raman provides easy sampling, whereas IR spectroscopy frequently needs some form of sample preparation. Materials which are difficult to handle in IR (highly viscous liquids, solids requiring pellets, mulls, or diffuse reflectance) are often easily measured by Raman. Unlike IR reflectance spectra, Raman spectra of solid samples are not affected by sample properties such as particle size. A significant difference with infrared absorption spectroscopy is that the Raman signal is emitted from the sample. Consequently, matrix effects are seldom as severe in RS as they are with mid-IR and NIR. Water may be used as a solvent with no loss in signal or resolution. Glass, even tinted, does not interfere with the Raman spectra. 

A representative consequence matrix is shown in Table 1.4. The matrix has four levels of consequence covering worker safety, public safety, environment, and economic loss. There are no rules as to how many levels should be selected, nor does... 

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