EXPERIMENTAL APPROACH: Rhizome and leaves of C. caesia were dried with oven (OD) and freeze (FD)-drying methods, and extracted with different Φ(ethanol,water)=100:0, 80:20, 50:50 and 0:100. The bioactivities of C. caesia extracts were evaluated using in vitro tests; total phenolic content (TPC), antioxidant (DPPH and FRAP) and α-glucosidase inhibitory activity. Proton nuclear magnetic resonance (1H NMR)-based metabolomics approach was employed to differentiate the most active extracts based on their metabolite profiles and correlation with bioactivities.
RESULTS AND CONCLUSIONS: The FD rhizome extracted with Φ(ethanol,water)=100:0 was observed to have potent TPC expressed as gallic acid equivalents, FRAP expressed as Trolox equivalents and α-glucosidase inhibitory activity with values of (45.4±2.1) mg/g extract, (147.7±8.3) mg/g extract and (265.5±38.6) µg/mL (IC50), respectively. Meanwhile, for DPPH scavenging activity, the Φ(ethanol,water)=80:20 and 100:0 extracts of FD rhizome showed the highest activity with no significant difference between them. Hence, the FD rhizome extracts were selected for further metabolomics analysis. Principal component analysis (PCA) showed clear discrimination among the different extracts. Partial least square (PLS) analysis showed positive correlations of the metabolites, including xanthorrhizol derivative, 1-hydroxy-1,7-bis(4-hydroxy-3-methoxyphenyl)-(6E)-6-heptene-3,4-dione, valine, luteolin, zedoardiol, β-turmerone, selina-4(15),7(11)-dien-8-one, zedoalactone B and germacrone, with the antioxidant and α-glucosidase inhibition activities, whereas curdione and 1-(4-hydroxy-3,5-dimethoxyphenyl)-7-(4-hydroxy-3-methoxyphenyl)-(lE,6E)-1,6-heptadiene3,4-dione were correlated with α-glucosidase inhibitory activity.
NOVELTY AND SCIENTIFIC CONTRIBUTION: C. caesia rhizome and leaf extracts contained phenolic compounds and had varies antioxidant and α-glucosidase inhibitory capacities. These findings strongly suggest that the rhizomes of C. caesia are an invaluable natural source of active ingredients for applications in pharmaceutical and food industries.
METHODS: The N. oleracea fractions were obtained using solid phase extraction (SPE). A metabolomics approach that coupled the use of proton nuclear magnetic resonance (1H NMR) with multivariate data analysis (MVDA) was applied to distinguish the metabolite variations among the N. oleracea fractions, as well as to assess the correlation between metabolite variation and the studied bioactivities (DPPH free radical scavenging and α-glucosidase inhibitory activities). The bioactive fractions were then subjected to ultra-high performance liquid chromatography tandem mass spectrometry (UHPLC-MS/MS) analysis to profile and identify the potential bioactive constituents.
RESULTS: The principal component analysis (PCA) discriminated EF and MF from the other fractions with the higher distributions of phenolics. Partial least squares (PLS) analysis revealed a strong correlation between the phenolics and the studied bioactivities in the EF and the MF. The UHPLC-MS/MS profiling of EF and MF had tentatively identified the phenolics present. Together with some non-phenolic metabolites, a total of 37 metabolites were tentatively assigned.
CONCLUSIONS: The findings of this work supported that N. oleracea is a rich source of phenolics that can be potential antioxidants and α-glucosidase inhibitors for the management of diabetes. To our knowledge, this study is the first report on the metabolite-bioactivity correlation and UHPLC-MS/MS analysis of N. oleracea fractions.
RESULTS: Comparison of the PLS and RF showed that RF exhibited poorer generalization and hence poorer predictive performance. Both the regression coefficient of PLS and the variable importance of RF revealed that quercetin and kaempferol derivatives, caffeic acid and vitexin-2-O-rhamnoside were significant towards the tested bioactivities. Furthermore, principal component analysis (PCA) and partial least squares-discriminant analysis (PLS-DA) results showed that sonication and absolute ethanol are the preferable extraction method and ethanol ratio, respectively, to produce N. oleracea extracts with high phenolic levels and therefore high DPPH scavenging and α-glucosidase inhibitory activities.
CONCLUSION: Both PLS and RF are useful regression models in metabolomics studies. This work provides insight into the performance of different multivariate data analysis tools and the effects of different extraction conditions on the extraction of desired phenolics from plants. © 2017 Society of Chemical Industry.