DryLab is used in method development with the goal of enhancing separations through the exclusive use of gradient time and column temperature. The resulting method is simple, fast, demonstrates excellent transferability and is ideal for the quantitative analysis of pigments in dilute natural water samples.

Rational selection of optimized experimental conditions for chromatographic separation of analytes is realized nowadays by means of specialized method development software. Two such programs, DryLab (LC Resources, USA, in Europe: Molnár-Institut, Berlin) and ChromSword (Merck, Darmstadt), were compared in a few aspects in this paper. The aim was to make a comparison of the quality of the software packages in the separation of neutral compounds, performed isocratically in RP-HPLC systems. A discussion of the differences in predicted and experimental chromatographic retention parameters is reported. The conclusion reached is, that the two programs provide good predictions of retention data, when predictions are based on %B changes using two initial experimental runs. An additional option of ChromSword, employing the quantitative structure-retention relationship (QSRR), did not to provide precise predictions of the separation, because molecular structural data were used as inputs. Predictions based on molecular structure were unaccurate. The comparison of the performance was only done with ca. 5% of the funtional capabilities, which DryLab was offering. Gradient data were not compared at all.

Excellent description of the mathematical background and practical aspects of HPLC gradient elution technique as a fundamental of the DryLab-software .

Development of a method for a high performance liquid chromatography (HPLC) separation can be a major undertaking. Before the separation can be made, the sample must be in a suitable form to inject, and pretreatment steps are often required to remove major interferences or materials that might shorten the column life. After conditions for adequate separation are determined, some level of method validation is usually performed. Sample pretreatment and method validation are beyond the scope of the present discussion, which concentrates on achieving separation. This article describes only the major steps that are required for most samples. For additional information, the reader is urged to consult the reference by Snyder et al. (see Further Reading) which covers HPLC method development in detail. Additional method development information can be found in the other monographs listed.

Software is described that allows the rapid development of separations by means of isocratic reversed-phase liquid chromatography (RP-LC) based on the optimization of column temperature (T) and mobile phase strength (%B). For a given sample, four initial experiments are carried out at two different temperatures, using either isocratic or (better) gradient elution. If isocratic experiments are chosen for computer simulation, it is necessary to select appropriate values of %B for these initial runs. Literature data for solute retention as a function of T are reviewed as a basis for estimating values of %B at the two values of T selected. The paper describes use of a newly introduced version of DryLab to optimize reversed-phase isocratic separations by varying temperature and %B.

The choice of T and t_G as variables for controlling selectivity and resolution during reversed-phase liquid chromatography (RPLC) method development can be used to minimize problems caused by column batch-to-batch irreproducibility. When a new column fails to provide adequate separation of the sample, altered values of T and t_G can be predicted that will restore the separation obtained with the previous column. Alternatively, columns from different manufacturers can be tested during method development, in order to find a common set of conditions (T and t_G) that provide acceptable separation with two or more of these columns. In this way, any of several columns from different sources become usable for the method. Examples are shown of these different computer-assisted procedures for minimizing problems due to column variability.