Selectivity optimization interpretive methods

Selection of interpretive methods applied for selectivity optimization in chromatography. 

Table 5.7d suggests that simultaneous interpretive methods are highly promising for selectivity optimization, but that there is still much room for improvement if research effort is directed at... 

The Sentinel method is the outstanding exponent of the group of interpretive methods, as it has already been applied successfully for selectivity optimization in programmed solvent LC. However, other interpretive methods, based either on fixed experimental designs or on iterative procedures, can be applied along the same lines. It was seen in section 6.3.2.3 that the extension of the Sentinel method to incorporate gradient optimization was fairly straightforward. 

Chemometrics has been defined as the chemical discipline that uses mathematical and statistical methods to design or select optimal measurement procedures and experiments and to provide maximum chemical information by analysing chemical data (Kowalski, 1978). It is a relatively new discipline that assists with (i) the planning of experiments, and (ii) the manipulation and interpretation of large data sets. Some aspects of chemometrics can be done using an appropriate speadsheet but the majority of applications require the use of dedicated software. The fundamental principles of most of the processes involved in chemometrics are those of statistics. You are therefore advised to become familiar with the material in Chapters 40 and 41 before proceeding. 

Moreover, the relationships making part of the Kowalska model (e.g., Eqs. 41-44) are—contrary to the relationships offered by the other approaches discussed in this chapter—more flexible and hence more accurate, due to the fact that they (i) strongly depend on the chemical nature of the mixed mobile phases, and (ii) couple together the coefficient with the mobile phase composition in a manner which is nonlinear by principle (the important feature that does not always occur with the remaining models of solute retention, no matter how much this nonlinearity was closer to the empirical practice of chromatography than the assumed straight-line simplifications). Thus it seems reasonable to expect that Eqs. 41-44 can be employed in the interpretational methods of selectivity optimization at least as successfully, as any other already established retention model, and occasionally even significantly better than them. 

The analytical procedures for Level 3 are specific to selected components identified by Level 2 analysis and are oriented toward determining the time variation in the concentrations of key indicator materials. In general, the analysis will be optimized to a specific set of stream conditions and will therefore not be as complex or expensive as the Level 2 methods. Both manual and instrumental techniques may be used, provided they can be implemented at the process site. Continuous monitors for selected pollutants should be incorporated in the analysis program as an aid in interpreting the data acquired through manual techniques. The total Level 3 analysis program should also include the use of Level 2 analysis at selected intervals as a check on the validity of the key indicator materials which reflect process variability. 

Various comprehensive HPLC systems have been developed and proven to be effective both for the separation of complex sample components and in the resolution of a number of practical problems. In fact, the very different selectivities of the various LC modes enable the analysis of complex mixtures with minimal sample preparation. However, comprehensive HPLC techniques are complicated by the operational aspects of transferring effectively from one operation step to another, by data acquisition and interpretation issues. Therefore, careful method optimization and several related practical aspects should be considered. 

Robust formulations are today an absolute prerequisite. Concerning the production of granules, the granule size distribution should not vary from batch to batch. The key factors are the correct amount and the type of granulating liquid. The interpretation of the power consumption method can be very important for an optimal selection of the type of granulating liquid. The possible variation of the initial particle size distribution of the active substance and/or excipients can be compen-... 

Selecting the right variables often improves the models and makes interpretation easier. When there are too many descriptors, and especially when these descriptors do not have a clear physico-chemical meaning (e.g., connectivity indices and other 2D descriptors), stochastic methods such as genetic algorithms and evolutionary strategies can be used for finding an optimal subset of descriptors [91,92]. 

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