Sample overlap

Lucky you. You have a solid instead of a liquid. This presents one problem. What are you going to dissolve the solid in Once it s a solution, you handle it just like a liquid sample. Unfortunately, if the solvent has protons and you know there ll be much more solvent than sample, you ll get a major proton signal from your solvent. Not a good thing, especially if the signals from your solvent and sample overlap. 

One important factor which limits the performance of flame AAS is interference, both spectral and chemical. Spectral interference occurs where emission lines from two elements in the sample overlap. Despite the huge number of possible emission lines in typical multielement samples, it is rarely a problem in AA, unless molecular species (with broad emission bands) are present in the flame (in which case, a higher temperature might decompose the interfering molecule). If spectral interference does occur (e.g., A1 at 308.215 nm, V at 308.211 nm) it is easily avoided by selecting a second (but perhaps less sensitive) line for each element. 

Figure 15 shows the results from such calculations for low mass (Q) and high mass (Q)13% PET-PCL samples. We see that the PET content increases with increasing M for M< 4x 104 and approaches a constant value of -14% in the high molar mass range. For the 58% PET-PCL sample, the composition distribution is nearly constant. The composition of the high-molar mass 13% PET-PCL sample overlaps with that of the low mass 13% PET-PCL sample in the same molec-... 

A typical application is the determination of position, size and orientation of an object together with the material information as control parameters for an automatic sorter. For clean, non-overlapping samples it is sufficient to determine the centre of each object and the approximate size to control the sorting unit. However, frequently these ideal conditions cannot be guaranteed, as samples overlap or surface adhesions may interfere with the classification, as shown in Fig. 7.4. Such artefacts will lead to (partially) false information about size and position of an object and hence to incomplete or erroneous sorting. 

Where sample overlap does become a problem, further clarification was often achieved by increasing the number of clusters. Table VIII... 

Clusters of clean, connected sands of the Upper Marine Molasse and Lower Freshwater Molasse within the main trend samples overlap, and the 6 0 vs. 6 - C values correlate this supports the mixing model. 

Sample overlap is a result of the continuous dispersion process occurring during sample transport towards the detector. The effect is minimised by improving the design of the flow manifold and/or by reducing the sampling rate. 

The Sr/ Sr ratios of the beachrock cements of the southern Caribbean offshore islands of Venezuela (Los Roques Island) are above that of modem seawater. By contrast the Sr/ Sr ratios of Puerto Rico samples overlap those of modem seawater, and the Red Sea values are below that of modem seawater. The increase in the Sr/ Sr ratios of the beachrock cement with decreasing age suggests that weathering ratios were high or that the weathered rocks were exceptionally radiogenetic, or reflect changes in the global isotope mass balance. 

Because IMS is a method that separates gas phase ions through collisions with a buffer gas, all analytes must be transported from the sample matrix and converted to a gas phase ion before ion mobility separation and detection can be performed. Thus, the type of introduction method largely depends on the physical characteristics of the analyte. The remainder of this chapter is divided into four sections based on the characteristics of the sample vapor, semivolatile, aqueous, and solid. While these categories are somewhat arbitrary with significant sample overlap, it is useful to think of volatile samples as those compounds that exist or partially exist as vapors under ambient temperature and pressure semivolatile samples as those compounds that can be volatilized but have vapor pressures too low to detect by IMS under ambient temperature and pressure aqueous samples as those compounds that are not volatile but can be dissolved in water and solid samples as compounds not in a solution. Table 3.1 lists a number of example analytes according to the categories discussed in this chapter. 

Image contrast arises because the force between tip and sample is a function of both tip-sample separation and the material properties of the tip and the sample. To date, in most applications the AFM was used to image surface topography in the contact mode. Then, image contrast is obtained from the very short-range repulsion that occurs when the electron orhitals of tip and sample overlap (Bom repulsion). However, further interactions between tip and sample can be used to investigate material properties on a nanometer scale. 

When complex classification problems arise (e.g. the different classes of sample overlap or distribute in a non-linearly separable shape) one can have resource either to ANNs (which implies that one must be aware of their stochastic nature and of the optimisation tasks that will be required) or increase the dimensionality of the data (i.e. the variables that describe the samples) in the hope that this will allow a better separation of the classes. How can this be possible Let us consider a trivial example where the samples were drawn/ projected into a two-dimensional subspace (e.g. two original variables, two principal components, etc.) and the groups could not be separated by a linear border (in the straight line sense. Figure 6.9a). However, if three variables were considered instead, the groups would be separated easily (Figure 6.9b). How to get this(these) additional dimension(s) is what SVM addresses. 

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