Comparison of Metabolomics Approaches for Evaluating the Variability of Complex Botanical Preparations: Green Tea (Camellia sinensis) as a Case Study

Abstract

A challenge that must be addressed when conducting studies with complex natural products is how to evaluate their complexity and variability. Traditional methods of quantifying a single or a small range of metabolites may not capture the full chemical complexity of multiple samples. Different metabolomics approaches were evaluated to discern how they facilitated comparison of the chemical composition of commercial green tea [Camellia sinensis (L.) Kuntze] products, with the goal of capturing the variability of commercially used products and selecting representative products for in vitro or clinical evaluation. Three metabolomic-related methods—untargeted ultraperformance liquid chromatography–mass spectrometry (UPLC-MS), targeted UPLC-MS, and untargeted, quantitative 1HNMR—were employed to characterize 34 commercially available green tea samples. Of these methods, untargeted UPLC-MS was most effective at discriminating between green tea, green tea supplement, and non-green-tea products. A method using reproduced correlation coefficients calculated from principal component analysis models was developed to quantitatively compare differences among samples. The obtained results demonstrated the utility of metabolomics employing UPLC-MS data for evaluating similarities and differences between complex botanical products.

Conventional and accelerated-solvent extractions of green tea (camellia sinensis) for metabolomics-based chemometrics

“…This resulted in a set of 12 experiments, with three center points for replication (Table 1). The reproduced correlation coefficient incorporates the untargeted metabolome profile of each sample into a multivariate statistical model and yields a single quantitative metric of similarity [16]. Three central replicates, representing the variables’ parameter mid-points (oven temperature, solvent ratio, and cycle time), were run using identical conditions, and were evaluated as part of the experimental design to provide information on reproducibility (experiments 13–15, Table 1).…”

“…The D-optimal design was not sufficient to optimize the extraction factors; however, the responses provided insight into the key parameters that affected extraction. Based upon the D-optimal design and the authors’ previous experience with green tea extraction [16], the final factors selected for the green tea screening were: oven temperature 75 °C; cycle time 5 minutes, and a 100% MeOH solvent composition.…”

“…Extraction conditions were evaluated using three independent variables: extraction temperature (X 1 ), cycle time (X 2 ), and solvent ratio (X 3 ). Variations in extraction temperature and solvent ratio were straight forward, and based upon previous extraction experience with green tea samples [16]; cycle time represents the contact time between the solvent and sample, and levels of this parameter were altered based upon manufacturer recommendations. The factors were studied at three different levels (coded as -1, 0, and +1), and the D-optimal design resulted in a set of 15 experiments, with three center points for replication (Table 1).…”

“…Reproduced correlation coefficients (RCC) were calculated from principal component model loadings as described elsewhere to provide a quantitative value for similarity between samples [16]. The correlation coefficient describes the extent to which a given sample (in our case a green tea extract) correlates with any other sample in the dataset after removing noise and other sources of small variation from the data.…”

Metabolomic variability of four macroalgal species of the genus Lobophora using diverse approaches

“…The choice of the technique to accurately distinguish species is pivotal and only few studies comparing these techniques have been published to date, most of them being applied to plants. For example, untargeted LC-MS was the most effective to discriminate several green tea (Kellogg et al, 2017). Other authors used multiple approaches to study the metabolomic fingerprint in zoanthids (Costa-Lotufo et al, 2018) or in the plant kingdom (Agnolet et al, 2010;Farag et al, 2012b).…”