ABSTRACT
BACKGROUND: The latest chromatographic retention models are capable of accurately describe the dependencies of retention over a wide range of experimental conditions. By using a suitable conversion, these models can be transformed into equations expressing the optimization criteria as function of multiples variables. Even though that theoretical models significantly reduce the experimental requirements for optimizations, these models have been barely used. Instead, most optimizations rely on empirical exploration of the relationships between criterions and variables. There is a need for a strategy to reduce the required number of experiments in multivariated optimization of separations, and Fundamental Models offer a clear opportunity for addressing it. RESULTS: A Fundamental Model is used to give the simultaneous dependence of chromatographic retention of seven ionizable pesticides on the three variables: solvent composition, temperature and pH (w, T, pH). Based on few experiments, the 10 parameters required to predict the chromatographic retention of those compounds, taken as model analytes, can be obtained. Two mathematical treatments to convert retentions into resolutions between pairs are used: one considering extracolumn dispersions and other neglecting these contributions. Using the Overlapped Resolutions Maps, extended to four dimensions, two optimal conditions can be found for the two different mathematical conversions. Chromatographic conditions were empirically evaluated obtaining the best results for the optimization considering extracolumn dispersions, proving that this condition is a true optimal. It was demonstrated that any small shift in any of the variables from this true optimal leads to a loss in resolution. SIGNIFICANCE: Fundamental Models describing chromatographic retention as a simultaneous function of multiple variables are nowadays very accurate. In this work is demonstrated that these models are useful not only to predict retentions, but also to optimize separations, even in the more challenging mode: isocratic, isothermal and iso-pH. However, the success in the optimization procedure depends also on the proper definition of the mathematical conversion of the Fundamental Models into optimization criteria.
ABSTRACT
Nowadays, the higher peak capacity achievable by comprehensive two-dimensional liquid chromatography (LC×LC) for the analysis of vegetal samples is well-recognized. In addition, numerous compounds may be present in very different amounts. Cannabinoids and terpenes represent the main components of Cannabis sativa inflorescence samples, whose quantities are relevant for many application purposes. The analyses of both families are performed by different methods, at least two different separation methodologies, mainly according to their chemical characteristics and concentration levels. In this work, concentration differences and sample complexity issues were addressed using an LC×LC method that incorporates an optimized modulation strategy, namely smart active modulation, for the simultaneous analysis of cannabinoids and terpenes. The system was built by interposing an active flow splitter pump between both dimensions. This set up aimed to exploit the known advantages of LC×LC. In addition, here we proposed to use the splitter pump for online control over the splitting ratio to facilitate the selective dilution of different eluted fractions containing compounds with highly different concentrations. This work represents the first application and demonstration of smart active modulation (SAM) in LC×LC to simultaneously determine analytes with significant differences in concentration levels present in complex samples. The proposed method was tested with eight different strains, from which fingerprints were taken, and numerous cannabinoids and terpenes were identified in these samples. With this strategy, between 49 and 54 peaks were obtained in the LC×LC chromatograms corresponding to different strains. THCA-A was the main component in six strains, while CBDA was the main component in the other two strains. The main terpenes found were myrcene (in five strains), limonene (in two strains), and humulene (in one strain). Additionally, numerous other cannabinoids and terpenes were identified in these samples, providing valuable compositional information for growers, as well as medical and recreational users. The SAM strategy here proposed is simple and it can be extended to other complex matrices.
