Systematic optimization of program parameters

2 Systematic optimization of program parameters Optimization without solute recognition 

The concept of linear solvent strength (LSS) gradients developed by Snyder (see also sections 5.4.2 and 6.2.2) incorporates optimization of both the shape and the slope of gradient programs. The shape of an LSS gradient is determined by 

The relationship between retention and composition under isocratic conditions, i.e. the function 

The combination of these two factors determines the required shape of an LSS gradient. Linear gradients were shown to result for RPLC in section 5.4, whereas a concave gradient was found to be optimal for LSC in section 6.2.2. 

The optimal slope of the gradient also follows from the LSS concept, since it was shown by Snyder et aL [616] that optimum values for the gradient steepness parameter b are in the range 0.2 b 0.4. If the function f( p) is known, then the optimum slope of the gradient can be calculated. For example, in RPLC the relationship between retention and composition over the range 1 k 10 can be described by 

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