Gradient separations optimization parameters

It is clearly beyond the scope of this chapter to consider further the selection of which variables to use in the simplex optimization. To summarize our own relatively limited experience, however (boxes in Table IV represent combinations examined to date), we recommend the following For a relatively simple separation, begin with a two-parameter simplex that includes either initial pressure (or density), using as many characteristics of the analytes and/or sample matrix to logically deduce which remaining variable to optimize. For a more complex separation, or one in which little is known about the sample, try a 4 or 5-variable simplex that includes the initial pressure and pressure gradient (or initial density and density gradient) as optimization variables. 

Of course, the simultaneous optimization of different (primary) program parameters (initial and final composition, slope and shape of the gradient) and secondary parameters (nature and relative concentration of modifiers) may involve too many parameters, so that an excessive number of experiments will be required to locate the optimum. This problem may be solved by a separate optimization of the program (primary parameters) and the selectivity (secondary parameters) based on the concept of iso-eluotropic mixtures (see section 3.2.2). This will be demonstrated below (section 6.3.2.2). However, the transfer of... 

Optimizing gradient separations requires the measurement of corresponding isocratic parameters (e.g., k, and 5) for many samples this is more easily done using gradient elution than isocratic elution. 

Others have examined the necessary parameters that should be optimized to make the two-dimensional separation operate within the context of the columns that are chosen for the unique separation applications that are being developed. This is true for most of the applications shown in this book. However, one of the common themes here is that it is often necessary to slow down the first-dimension separation system in a 2DLC system. If one does not slow down the first dimension, another approach is to speed up the second dimension so that the whole analysis is not gated by the time of the second dimension. Recently, this has been the motivation behind the very fast second-dimension systems, such as Carr and coworker s fast gradient reversed-phase liquid chromatography (RPLC) second dimension systems, which operate at elevated temperatures (Stoll et al., 2006, 2007). Having a fast second dimension makes CE an attractive technique, especially with fast gating methods, which are discussed in Chapter 5. However, these are specialized for specific applications and may require method development techniques specific to CE. 

The peptides generated by proteolysis are separated using reverse-phase HPLC to minimize mass overlap and ionization suppression caused by ion competition in the electrospray source [40]. The optimized LC gradient parameters efficiently separate peptides while minimizing loss of deuterium through back exchange with solvent. Increased sensitivity can be achieved by using capillary HPLC columns and nanoelectrospray methods [47]. 

As the gradient time and other parameters (temperature, pH, or initial concentration of solvent B) often show synergistic effects on separation, simultaneous optimization of two or more parameters... 

Moret et al. (67,68) studied all the parameters that influence amine recovery under conditions where a liquid-liquid purification step with an organic solvent follows the acid extraction, prior to derivatization with DBS and RP-HPLC analysis. The optimized methods of sample preparation for different foods, including cheese, meat, and fish, are given. The same research group (69) optimized the extraction conditions for Phe, Put, Cad, His, Tyr, Spe, and Spd. Food samples were first mixed with TCA and centrifuged and then basified and extracted with BuOH/CHCl3 (1 1). The BAs were then derivatized with DNS and separated on a Spherisorb 3S TG column with an ACN-H20 gradient. The method was applied to samples of tuna, salmon, and salami. 

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