Method development factor selection

The silanol induced peak tailing is also a function of the pH of the mobile phase. It is much less pronounced at acidic pH than at neutral pH. Therefore many of the older HPLC methods use acidified mobile phases. However, pH is an important and very valuable tool in methods development. The selectivity of a separation of ionizable compounds is best adjusted by a manipulation of the pH value. The retention factor of the non-ionized form of an analyte is often by a factor of 30 larger than the one of the ionized form, and it can be adjusted to any value in between by careful control of the mobile phase pH. This control must include a good buffering capacity of the buffer to avoid random fluctuations of retention times. 

The ultimate goal of an assay method is the separation and visualization of all components in a single chromatogram. Proper selection of detection wavelength is a critical part of method development. When choosing a detection wavelength, the following factors need to be taken into consideration ... 

Ion-radical reactions require special methods to stimulate or impede them. The specificity of these methods is determined with particular properties of ion-radicals. Many ion-radical syntheses are highly selective yielding products unattainable by other methods. The aim of this chapter is to analyze the phenomena that determine the ways to optimize ion-radical reactions. This chapter considers factors governing the development of the reactions with proven ion-radical mechanisms. Two groups of optimizing factors will be discussed physical and purely chemical ones. Factors such as solvent change and salt addition are certainly in the borderline between chemical and physical effects. 

Optimal values for the factors are selected from the tested levels for the factors (extremes or nominal) in function of a number of responses of the method (see also references [16,19]). When one changes the method conditions due to these results one has to be aware that a new method is defined. What is done here is in fact a simplistic way of optimizing a method. The optimization of a method however is a step that is expected to come much sooner in the method development than in the ruggedness testing. One also has to realize that when one defines a new method this requires a new full validation, including a ruggedness test. 

Analytical Method Development. From the point of view of sorbent selection, the important factors to consider in analytical method development are sorbent/solvent compatibility and the detection limit of the analyte. 

Measurement uncertainty is a critical parameter for nearly every kind of analytical system. Parameters in the second column of table 8.3 are not unimportant and must be established, but they are not likely to become limiting factors in the development of the method. In table 8.3, where selectivity is in parentheses, this is not to say that the method should not be demonstrably capable of analyzing the target analyte, but that it should be clear very quickly whether or not the method is doing its job. 

In the following, the atom/fragment contribution method (AFC method) developed by Meylan and Howard (1995) is used to illustrate the approach. This method is similar to the CLOGP method, but it is easier to see its application without using a computer program. Here, we confine ourselves to a few selected examples of fragment coefficients and correction factors. This will reveal how the method is... 

Biochemical research often requires the quantitative measurement of protein concentrations in solutions. Several techniques have been developed however, most have limitations because either they are not sensitive enough or they are based on reactions with specific amino acids in the protein. Since the amino acid content varies from protein to protein, no single assay will be suitable for all proteins. In this section we discuss five assays three older, classical methods that are occasionally used today and two newer methods that are widely used. In four of the methods, chemical reagents are added to protein solutions to develop a color whose intensity is measured in a spectrophotometer. A standard protein of known concentration is also treated with the same reagents and a calibration curve is constructed. The other assay relies on a direct spectrophotometric measurement. None of the methods is perfect because each is dependent on the amino acid content of the protein. However, each will provide a satisfactory result if the proper experimental conditions are used and/or a suitable standard protein is chosen. Other important factors in method selection include the sensitivity and accuracy desired, the presence of interfering substances, and the time available for the assay. The various methods are compared in Table 2.3. 

If there is no or little information on the method s performance characteristics, it is recommended that the method s suitability for its intended use in initial experiments be proven. These studies should include the approximate precision, working range, and detection limits. If the preliminary validation data appear to be inappropriate, the method itself, the equipment, the analysis technique, or the acceptance limits should be changed. In this way method development and validation is an iterative process. For example, in liquid chromatography selectivity is achieved through selection of mobile-phase composition. For quantitative measurements the resolution factor between two peaks should be 2.5 or higher. If this value is not achieved, the mobile phase composition needs further optimization. 

The method of choice is dependent upon the analyte, the assay performance required to meet the intended application, the timeline, and cost-effectiveness. The assay requirements include sensitivity, selectivity, linearity, accuracy, precision, and method robustness. Assay sensitivity in general is in the order of IA > LC-MS/MS > HPLC, while selectivity is IA LC-MS/MS > HPLC. However, IA is an indirect method which measures the binding action instead of relying directly on the physico-chemical properties of the analyte. The IA response versus concentration curve follows a curvilinear relationship, and the results are inherently less precise than for the other two methods with linear concentration-response relationships. The method development time for IA is usually longer than that for LC/MS-MS, mainly because of the time required for the production and characterization of unique antibody reagents. Combinatorial tests to optimize multiple factors in several steps of some IA formats are more complicated, and also result in a longer method refinement time. The nature of IAs versus that of LC-MS/MS methods are compared in Table 6.1. However, once established, IA methods are sensitive, consistent, and very cost-effective for the analysis of large volumes of samples. The more expensive FTMS or TOF-MS methods can be used to complement IA on selectivity confirmation. 

The next most important factor is to bring the capacity factors into the optimum range. At the same time or immediately thereafter, we should try to optimize the selectivity (a). Both are very important stages in the method development process, because no separation will be obtained if either k=0 or a= 1 (see section 1.5), no matter how efficient the column and how good the instrument. Very large k values should also be eliminated at this stage, because of both time and sensitivity considerations (see e.g. figure 6.1b). 

There have been a number of methods developed for the extraction of inulin from Jerusalem artichoke tubers (Aravina et al 2001 Barta, 1993 Ji et al., 2002 Vogel, 1993), a composite of which is illustrated in Figure 5.3. The specific method selected will depend on the end product desired, resources available, volume, and other factors. 

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