An approach to evaluate the information in chromatographic fingerprints: Application to the optimisation of the extraction and conservation conditions of medicinal herbs

Transferability of global retention models in reversed-phase liquid chromatography for natural products

Considerable progress has been made in enhancing resolution in reversed-phase liquid chromatography for the analysis of complex samples, particularly within the field of natural products, through the application of global retention models using multi-linear gradients. Global models effectively differentiate solute retention effects from those originating from the column and solvent, offering predictive capabilities comparable to conventional individual retention models, without the requirement for standards for all compounds. While conventional individual models result in higher accuracy, they frequently demand standards that are unavailable for natural product samples. Moreover, the creation of individual models can be time-consuming due to the need for repetitive work for additional compounds. Experimental validation of global models has demonstrated that the accuracy is enough for the prediction of complex chromatograms. Through a carefully designed experimental work, this study reports the correct determination of global parameters for column and solvent, with excellent consistency across various medicinal plant samples. The successful transfer of predictions and optimisation of resolution across diverse plant species (lemon balm, peppermint, and pennyroyal) is confirmed. This highlights the applicability of predictions using global models across botanical varieties.

Performance of global retention models in the optimisation of the liquid chromatographic separation (II): Complex multi-analyte samples

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.

Analysis and classification of tea varieties using high-performance liquid chromatography and global retention models

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.

Performance of global retention models in the optimisation of the chromatographic separation (I): Simple multi-analyte samples

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 β-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.

Global retention models and their application to the prediction of chromatographic fingerprints

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 ‒related to the column and solvent‒ 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 P_o/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.

A Novel and Practical Chromatographic "Fingerprint-ROC-SVM" Strategy Applied to Quality Analysis of Traditional Chinese Medicine Injections: Using KuDieZi Injection as a Case Study

Fingerprinting is widely and commonly used in the quality control of traditional Chinese medicine (TCM) injections. However, current studies informed that the fingerprint similarity evaluation was less sensitive and easily generated false positive results. For this reason, a novel and practical chromatographic “Fingerprint-ROC-SVM” strategy was established by using KuDieZi (KDZ) injection as a case study in the present article. Firstly, the chromatographic fingerprints of KDZ injection were obtained by UPLC and the common characteristic peaks were identified with UPLC/Q-TOF-MS under the same chromatographic conditions. Then, the receiver operating characteristic (ROC) curve was used to optimize common characteristic peaks by the AUCs value greater than 0.7. Finally, a support vector machine (SVM) model, with the accuracy of 97.06%, was established by the optimized characteristic peaks and applied to monitor the quality of KDZ injection. As a result, the established model could sensitively and accurately distinguish the qualified products (QPs) with the unqualified products (UPs), high-temperature processed samples (HTPs) and high-illumination processed samples (HIPs) of KDZ injection, and the prediction accuracy was 100.00%, 93.75% and 100.00%, respectively. Furthermore, through the comparison with other chemometrics methods, the superiority of the novel analytical strategy was more prominent. It indicated that the novel and practical chromatographic “Fingerprint-ROC-SVM” strategy could be further applied to facilitate the development of the quality analysis of TCM injections.