RESUMEN
BACKGROUND: Enhancing the quality control of medicinal plants is a complex challenge due to their rich variety of chemical compounds present at varying and extreme concentrations. Chromatographic fingerprints, which have become essential for characterising these complex natural materials, require achieving optimal separation conditions to effectively maximise the number of detected peaks. The challenges in optimising fingerprints and other complex multi-analyte samples include the unavailability of standards, the presence of unknown constituents and the substantial workload that would require conventional optimisation methods based on models. RESULTS: This work introduces an interpretive optimisation approach which operates on the premise of predicting chromatograms using global models. Initially, a multi-linear gradient experimental design is sequentially executed to accommodate all peaks in the chromatogram in an adequate time window. Following this, a small set of sample peaks (reference peaks) is selected based on their consistent traceability across all chromatograms in the design. Using this reference dataset, a global model is constructed, initially focused solely on the reference peaks and later extended to encompass all detected peaks in the sample. The aim is to find gradients that maximise resolution while minimising analysis time. These optimised gradients are applied successfully to enhance the separation of medicinal plant extracts, with particular emphasis on peppermint and pennyroyal extracts. SIGNIFICANCE: The proposed optimisation relying on global models can be applied to highly complex samples even in the absence of standards, or in cases where standards are available but their use is impractical due to workload constraints. Moreover, in discerning the most promising gradients for highly complex samples, peak purity has demonstrated superior reliability and competitiveness compared to peak capacity as chromatographic objective function.
Asunto(s)
Extractos Vegetales , Cromatografía Liquida/métodos , Extractos Vegetales/química , Extractos Vegetales/análisis , Extractos Vegetales/aislamiento & purificación , Plantas Medicinales/química , Cromatografía Líquida de Alta Presión/métodosRESUMEN
As a result of their metabolic processes, medicinal plants produce bioactive molecules with significant implications for human health, used directly for treatment or for pharmaceutical development. Chromatographic fingerprints with solvent gradients authenticate and categorise medicinal plants by capturing chemical diversity. This work focuses on optimising tea sample analysis in HPLC, using a model-based approach without requiring standards. Predicting the gradient profile effects on full signals was the basis to identify optimal separation conditions. Global models characterised retention and bandwidth for 14 peaks in the chromatograms across varied elution conditions, facilitating resolution optimisation of 63 peaks, covering 99.95 % of total peak area. The identified optimal gradient was applied to classify 40 samples representing six tea varieties. Matrices of baseline-corrected signals, elution bands, and band ratios, were evaluated to select the best dataset. Principal Component Analysis (PCA), k-means clustering, and Partial Least Squares-Discriminant Analysis (PLS-DA) assessed classification feasibility. Classification limitations were found reasonable due to tea processing complexities, involving drying and fermentation influenced by environmental conditions.
Asunto(s)
Análisis de Componente Principal , Té , Cromatografía Líquida de Alta Presión/métodos , Té/química , Té/clasificación , Análisis de los Mínimos Cuadrados , Análisis Discriminante , Camellia sinensis/clasificación , Camellia sinensis/químicaRESUMEN
Conventional retention models lead to accurate descriptions of the elution behaviour from the fitting of data for single solutes or from a set of solutes, one by one. However, the simultaneous fitting of several solutes through a regression process that separates the contributions of column and solvent from those of each solute is also possible. The result is a global retention model constituted by a set of equations with some common parameters (those associated with column and solvent), whereas others, specific to each solute, differ for each equation. This work explores the possibilities, advantages, and limitations of global models when they are applied to the optimisation of chromatographic resolution. A set constituted by 13 drugs (diuretics and ß-blockers) and a training experimental design of seven multi-linear gradients are considered. Since standards for all compounds were available, the optimisation based on global models could be compared with the conventional optimisation, which is based on individual models. In their current state, global models do not predict changes in elution order, but they do allow for incorporating additional solutes (e.g., new analytes or matrix peaks) with only one new experiment. This possibility is explored by extending the model for the 13 analytes to include 26 peaks associated with a contamination in the injector. The combination of individual and global models allows an optimisation where the effects of matrix peaks on the separation of analytes can be integrated.
Asunto(s)
Cromatografía , Proyectos de Investigación , Cromatografía/métodos , Solventes/química , Diuréticos , Antagonistas Adrenérgicos betaRESUMEN
Medicinal plants contain a large variety of chemical compounds in highly variable concentrations, so the quality control of these materials is especially complex. With this purpose, regulatory institutions have accepted chromatographic fingerprints as a valid tool to perform the analyses. In order to improve the results, separation conditions that maximise the number of detected peaks in these chromatograms are needed. This work reports the extension of a simulation strategy, based on global retention models previously developed for selected compounds, to all detected peaks in the full chromatogram. Global models contain characteristic parameters for each component in the sample, while other parameters are common to all components and describe the combined effects of column and solvent. The approach begins by detecting and measuring automatically the position of all peaks in a chromatogram, obtained preferably with the slowest gradient. Then, the retention time for each detected component is fitted to find the corresponding solute parameter in the global model, which leads to the best agreement with the measured experimental value. The process is completed by developing bandwidth models for the selected compounds used to build the global retention model based on gradient data, which are applied to all peaks in the chromatogram. The usefulness of the simulation approach is demonstrated by predicting chromatographic fingerprints for three medicinal plants with specific separation problems (green tea, lemon balm and linden), using several multi-linear gradients that lead to problematic predictions.
Asunto(s)
Melissa , Plantas Medicinales , Té/química , Tilia , Plantas Medicinales/química , Extractos Vegetales/química , Cromatografía Líquida de Alta Presión/métodosRESUMEN
We report here the improvement of a procedure to obtain global models, able to describe the retention behaviour of several sample components simultaneously. The reported global models include parameters that account for the general effects of column and solvent on retention and are common for all components, whereas other parameters are specific of each sample component. These models are fitted by alternate regression and offer a prediction performance comparable to individual retention models. The approach is suitable to samples of natural products including a large number of components in extremely diverse concentrations and in the absence of standards. Guidelines are given for the successful development of sample-oriented experimental designs (i.e. adapted to the elution of the components of the natural products), constituted by multi-linear gradients. These designs also facilitate peak tracking. The model proposed by Neue and Kuss to describe the retention was found to yield the best predictions. The approach is applied to the extracts of samples of green tea, lemon balm and linden, yielding excellent predictions of retention for selected components.
Asunto(s)
Melissa , Té , Cromatografía Líquida de Alta Presión/métodos , Extractos Vegetales/química , Solventes/química , Té/química , TiliaRESUMEN
The resolution of samples containing unknown compounds of different nature, or without standards available, as is the case of chromatographic fingerprints, is still a challenge. Possibly, the most problematic aspect that prevents systematic method development is finding models that describe without bias the retention behaviour of the compounds in the samples. In this work, the use of global models (able to describe the whole sample) is proposed as an alternative to the use of individual models for each solute. Global models contain parameters that are specific for each solute, while other parameters ârelated to the column and solventâ are common for all solutes. A special regression procedure is presented for the construction of global models, which are applied to predict highly complex chromatograms, such as chromatographic fingerprints, for diverse experimental conditions in isocratic and gradient elution. Another interesting application is the prediction of molecular properties, such as log Po/w, from the specific solute parameters of the global models. The examined adapted models are based on the equations proposed by Snyder, Schoenmakers, Neue and Kuss, Jandera, and Bosch Rosés to describe the retention. In all cases, the predictive capability was very satisfactory. Two cases of study were considered: chromatograms of camomile extracts analysed using acetonitrile gradients, and a set of 145 known compounds in a wide range of structures and functionalities, eluted isocratically with acetonitrile/water mobile phases.