Enhanced selectivity and search speed for method development using one-segment-per-component optimization strategies

Abstract

a b s t r a c tLinear gradient programs are very frequently used in reversed phase liquid chromatography to enhance the selectivity compared to isocratic separations. Multi-linear gradient programs on the other hand are only scarcely used, despite their intrinsically larger separation power. Because the gradient-conformity of the latest generation of instruments has greatly improved, a renewed interest in more complex multisegment gradient liquid chromatography can be expected in the future, raising the need for better performing gradient design algorithms. We explored the possibilities of a new type of multi-segment gradient optimization algorithm, the so-called "one-segment-per-group-of-components" optimization strategy. In this gradient design strategy, the slope is adjusted after the elution of each individual component of the sample, letting the retention properties of the different analytes auto-guide the course of the gradient profile. Applying this method experimentally to four randomly selected test samples, the separation time could on average be reduced with about 40% compared to the best single linear gradient. Moreover, the newly proposed approach performed equally well or better than the multi-segment optimization mode of a commercial software package. Carrying out an extensive in silico study, the experimentally observed advantage could also be generalized over a statistically significant amount of different 10 and 20 component samples. In addition, the newly proposed gradient optimization approach enables much faster searches than the traditional multi-step gradient design methods.

On the inherent data fitting problems encountered in modeling retention behavior of analytes with dual retention mechanism

“…This gives also some support for our previous experimental work, wherein accurate (reversed-phase) retention modeling was obtained using Eq. (4) and scouting runs from 5 to 95% ACN in 5 and 10 min, together with an extra run from 20 to 80%ACN in 5 min [25].…”

Possibilities of retention modeling and computer assisted method development in supercritical fluid chromatography

“…The best multi-segment gradient was obtained via the previously published one-segment-per-component search [15]. This gradient design strategy allows for an adjustment of the gradient slope after the elution of each individual component of the sample.…”

“…For multi-segment gradients, the fundamental gradient equation becomes a sum of integrals, each describing the retention during one segment of the gradient [15]. This sum of integrals can still be solved in a straightforward way, to obtain an analytical expression for the effective retention factor k eff = (t R − t 0 )/t 0 .…”

“…When the modeling is very accurate, the separation power can be increased, using multisegments gradients. However, smart algorithms are needed to find out the optimal gradient conditions among the innumerous combinations [15]. On the other hand, if no accurate retention modeling is possible, a hybrid method called the predictive elution window shifting and stretching approach (PEWS 2 ) can be used for the gradient optimization in both RPLC and the HILIC mode [13,18].…”

“…In reversed-phase, the retention behavior of a component can therefore in general be examined with a set of well-chosen experiments that cover the entire intended experimental space. The retention space between experimental data points is then modeled, and computer predictions, based on these models, are used in MD processes [15][16][17][18].…”