Chemical profiling and standardization of the defatted methanol extract of the leaves of Vitex negundo L. were carried out using 13C nuclear magnetic resonance (NMR) analysis followed by chemometric analysis of the chemical shift data. Chemical profile was obtained using a k-means cluster profile and chemical standardization which was achieved using a multivariate control chart. The V. negundo samples were made up of four groups: the training set, submitted samples from production farms, commercial samples, such as tablets, capsules and teas, and experimental samples (samples which were allowed to degrade). Four groups were generated in k-means cluster, which generally corresponded to the four types of samples. The multivariate control chart identified samples whose quality exceeded the upper control limit, all of which were commercial samples and experimental samples. The samples were also analyzed by quantitative thin layer chromatography (qTLC) using agnuside as marker compound. Comparison of the qTLC results with the k-means cluster and the multivariate control chart showed poor correspondence. This means that a univariate analysis of a plant sample using a marker compound is useful only for quantification of the target compound. On the other hand, chemical profiling and standardization of medicinal plants should use a multivariate method.
Virgin coconut oil (VCO) is produced from fresh mature coconut meat without the use of chemicals or high heat. VCO can be made using three processes: fermentation, centrifuge, and expeller. To determine quality, it is important to be able to differentiate control VCO (fresh) from old VCO, refined bleached and deodorized coconut oil (RBDCO), and VCO which has been adulterated with RBDCO. Differentiating these types of samples has remained a challenge because of their chemical similarity. This study investigated the ability of 13C NMR and multivariate analysis to differentiate these different coconut oil samples. The methodology used the standard 13C NMR pulse sequence with broadband 1H decoupling with dioxane as the internal standard (IS). After pre-processing of the spectra (alignment, bucketing/binning, normalization with respect to dioxane IS peak), untargeted multivariate analyses, both unsupervised and supervised, were done on the bins of the 13C peaks. Principal components analysis (PCA), a linear unsupervised method, was able to differentiate control VCO (n = 57) from RBDCO (n = 21), adulterated VCO (n = 9), and old VCO (n = 11). Partial least squares–discriminant analysis (PLS–DA) was used as the supervised linear binary classifier. Using overall accuracy and AUC-ROC curves (by 100 cross validation and single validation using manual holdout), the supervised dataset with an optimized model gave performances that were 99%, 95%, and 80% improved in differentiating control VCO vs. RBDCO, old VCO, and adulterated VCO (one vs. one), respectively. Predictive ability (Q2 < 0.20) and overall accuracy (<0.80) were poor compared to the previous models for binary classifier models (one vs. rest) to differentiate among the three VCO processes. This may be due to the variations in production conditions and methods that different VCO producers use. We conclude that 13C NMR combined with linear techniques can be used to accurately differentiate fresh VCO from RBDCO, old VCO, and adulterated VCO.
Vitex negundo has been known since ancient times as a medicinal plant. The objective of this study is to investigate the effect of methanol and ethanol extracts, and ethyl acetate, chloroform and aqueous fractions of Vitex negundo using an in vitro model to test glucose diffusion and to determine the phytochemical profile of the extracts and fractions using 13C nuclear magnetic resonance (NMR) spectroscopy. The chloroform fractions, ethyl acetate-EtOH and ethyl acetate-MeOH gave the highest inhibitory effect on both the diffusion activities in vitro. Retardation of glucose diffusion suggests that
negundo has the potential to lower postprandial glucose. Correlation analysis of the 13C NMR profile with retardation activity suggests that compounds containing glycosidic residues may be responsible for the glucose retardation activity. This is the first example where activity has been correlated with specific structural features of compounds from a crude extract using 13C NMR chemical shifts to assist in the identification of active compounds.
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