Quantitative factors method development

Conclusion. It has been demonstrated that the methods developed for the calculation of physicochemical effects can form the foundation for a general quantitative treatment of chemical reactivity. Based on the factors calculated with these various methods, reactivity functions can be elaborated that are able to assign a numerical reactivity to bonds and combinations of bonds in a molecule. In this manner the course and outcome of organic reactions can be predicted. A quantitative treatment of chemical reactivity is also an essential component in synthesis design since it allows evaluation of the feasibility of various synthetic reactions and pathways. 

This work has demonstrated that organically bound sulfur forms can be distinguished and in some manner quantified directly in model compound mixtures, and in petroleum and coal. The use of third derivatives of the XANES spectra was the critical factor in allowing this analysis. The tentative quantitative identifications of sulfur forms appear to be consistent with the chemical behavior of the petroleum and coal samples. XANES and XPS analyses of the same samples show the same trends in relative levels of sulfide and thiophenic forms, but with significant numerical differences. This reflects the fact that use of both XPS and XANES methods for quantitative determinations of sulfur forms are in an early development stage. Work is currently in progress to resolve issues of thickness effects for XANES spectra and to define the possible interferences from pyritic sulfur in both approaches. In addition these techniques are being extended to other nonvolatile and solid hydrocarbon materials. 

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. 

Recently, Riviere and Brooks (2007) published a method to improve the prediction of dermal absorption of compounds dosed in complex chemical mixtures. The method predicts dermal absorption or penetration of topically applied compounds by developing quantitative structure-property relationship (QSPR) models based on linear free energy relations (LFERs). The QSPR equations are used to describe individual compound penetration based on the molecular descriptors for the compound, and these are modified by a mixture factor (MF), which accounts for the physical-chemical properties of the vehicle and mixture components. Principal components analysis is used to calculate the MF based on percentage composition of the vehicle and mixture components and physical-chemical properties. 

Prior to performing a formal validation, the analytical chemist should have performed some prevalidation during method development. The expectation is that a well-developed HPLC method should subsequently be validated with no major surprises or failures. Prior to validation, specificity and some degree of robustness should be demonstrated. In addition, some form of system suitability criteria will have been established. System suitability evaluates the capability of an HPLC system to perform a specific procedure on a given day. It is a quality check to ensure that the system functions as expected and that the generated data will be reliable. Only if the system passes this test should the analyst proceed to perform the specific analysis. System suitability can be based on resolution of two specified components, relative standard deviation, tailing factor, limit of quantitation or detection, expected retention times, number of theoretical plates, or a reference check. 

Local considerations. There are many other local factors which dictate the path of method development. An increasingly common example is the development of a method which, as well as serving for quantitation, may be directly transferred to HPLC-mass spectrometry (commonly referred to as LC-MS) to allow the identification of minor components in a sample. This is achievable if the desired separation can be obtained using an ammonium acetate buffer instead of the more usual phosphate buffer. On the other hand, if it was intended that the unknown minor components should be isolated in milligram quantities to allow full structural elucidation using techniques such as H NMR as well as mass spectrometry, then conditions would be developed especially for the isolation unless the conditions for quantitative work coincidentally also lent... 

The sheer number of samples, their diversity, and a lack of suitable standards for quantitation can at first seem an insurmountable challenge. Since method development for individual samples is not feasible due to time and economic factors, a broadly applicable generic method must be developed, without sacrificing information content. Also, an inject-to-inject cycle time of... 

During an optimization phase in method development, the three factors in Table 2.3 were selected to develop the enantioseparation of a nonsteroidal anti-inflammatory drug (28). All examined factors were quantitative (A-C). 

Many factors are important for successful extraction and particularly the quantitative determination of volatiles by HS-SPME. As an example, this chapter reviews the method development for HS-SPME of volatiles from polar polyamide 6.6 matrix including the effect of fiber material, extraction time, incubation time, extraction temperature and quantitative determination by... 

Most LBAs require some level of sample dilution prior to analysis due to either the assay MRD or high analyte concentrations in the study samples. It is imperative to demonstrate during method development that the analyte, when present in levels above the ULOQ, can be diluted to concentrations within the quantitative range. This may be accomplished by illustrating that the analytical recovery of an ultrahigh matrix spike ( 100 1000 times ULOQ), diluted serially in assay matrix, remains acceptable over a wide concentration range (when corrected for the dilution factor). Dilutional linearity experiments often reveal the presence of a prozone or Hook effect, which is discussed in the next section. 

It is of course of interest to determine which of the methods of quantitation provides the most accurate and precise quantitative data. It is equally important to consider the constant trade-off for precision and sensitivity. At very low concentrations, precision often becomes limited by extraneous factors, such as wall effects. In such cases, high-precision measurements are becoming virtually unobtainable. In analogy to the Quantitative Ingredient Declarations (QUID) in food analysis, which require statements as to the uncertainty of the measurement and the variability of the results (sampling ), also for industrial polymer analysis intra- and interlaboratory variation and the meaning of average analytical results needs to be established. It is the responsibility of the analyst to adequately describe the instrumentation and performance to duplicate the repeatability and accuracy of the developed method. 

Analyte recoveries in P T experiments can vary widely due to matrix effects, purging efficiency, volatiUty, purge ceU design, choice of adsorbent, isolation temperature, and many other factors. Quantification with the various headspace techniques always requires method development in terms of extraction time and temperature in order to avoid degradation. With dynamic headspace (DHS) nearly 100% recovery of volatiles is possible provided headspace temperature is appropriate to remove most of the analyte in a reasonable time. Kolb et al. [32] have outlined the prospects of quantitation by means of headspace techniques. 

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