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Qualitative and Quantitative Determination of Bioactive Phytochemicals in Selected Cassia Species Using HPLC-ESI-QTOF-MS and UPLC-ESI-QqQLIT-MS/MS
Published in Brijesh Kumar, Vikas Bajpai, Vikaskumar Gond, Subhashis Pal, Naibedya Chattopadhyay, Phytochemistry of Plants of Genus Cassia, 2021
Brijesh Kumar, Vikas Bajpai, Vikaskumar Gond, Subhashis Pal, Naibedya Chattopadhyay
The PCA was studied on the basis of total matrix data of 18 compounds collectively in all plant parts of five species. The PCA shows that the data matrix reduced to 5 PCs explaining 86% variation. Jointly the first two PCs explain 54.76% variation of data where the 18 compounds were distributed in 3 clusters in the loading biplot as shown in Figure 2.5A. This explained similarity in the pattern of quantities among plant parts of various species. However, score plot by PC1 vs PC2 as shown in Figure 2.5B indicated distribution of investigated samples in four clusters. From the loading scatter plot, it was observed that different variables show different contributions in discrimination of Cassia species. However quantitative analysis showed that leaf, stem and root of C. siamea, with leaf and seed of C. uniflora species were represented the characteristic pattern. The C. siamea root is placed in an extreme of the biplot indicating high quantity levels of most of the compounds.
Targeted and Untargeted Metabolomics for Specific Food Intake Assessment
Published in Dale A. Schoeller, Margriet S. Westerterp-Plantenga, Advances in the Assessment of Dietary Intake, 2017
Carl Brunius, Huaxing Wu, Rikard Landberg
In untargeted LC–MS metabolomics experiments, multivariate data are generated. Typically, the number of variables (metabolic features) counts in the thousands to tens of thousands and thus far outweighs the number of samples. It is therefore often considered appropriate to analyze such data using multivariate modeling (Trygg et al. 2007). Among multivariate methods, a distinction is made between unsupervised and supervised methods. Unsupervised methods, such as principal component analysis (PCA) work by giving a representation of the measured, independent data (an X matrix; metabolomics data in this case) without the use of information from a dependent variable (normally a Y vector; such as dietary exposure in this case, although multiple responses are also possible). In supervised methods on the other hand, information from the Y variable(s) is used to guide the analysis of the X data, effectively resulting in a model relating them to each other (i.e., Y = f(X)). In the field of metabolomics, the partial least squares (PLS) family of methods, that is, regression and discriminant analysis using either PLS, OPLS, or sparse PLS, has become the de facto standard for supervised analysis (Trygg et al. 2007; Gromski et al. 2015). PLS and OPLS share the same analytical solutions and thus produce identical predictions, but in OPLS, the principal components are rotated to facilitate interpretation (Trygg et al. 2007). In sparse PLS modeling, on the other hand, a soft thresholding is employed to reduce the number of variables in the model, which often increases model performance (Lê Cao et al. 2008; Chung and Keles 2010). The overwhelming popularity of PLS methods is presumably related to the advantages they bring in terms of interpretation of the data. In fact, PCA and PLS share similarities in that they work by dimensionality reduction of the original data into sample scores, based on maintained variance in X for PCA and covariance between X and Y for PLS (Figure 18.1). The dimensionality-reduced scores can then easily be superimposed with variable loadings, and the scores and loadings together constitute the model components, where each component successively adds more explanatory power to the model. The combined biplot of scores and loadings from PCA and PLS analyses offers the possibility of simultaneous interpretation: The scores will reveal the structure or pattern in the data, whereas the loadings will give information as to which are the variables accounting for or driving the observed structure. In metabolomics, a combination of these strategies is often employed: Unsupervised PCA is performed initially to get a graphical overview of sample variability and identify potential outliers. Supervised PLS analysis is then performed to identify patterns in the metabolome corresponding to the research question at hand (Xia et al. 2012).
Extension of biplot methodology to multivariate regression analysis
Published in Journal of Applied Statistics, 2021
The biplot, often referred to as the multivariate version of a scatterplot, allows for the graphical display of rows (samples) as points and each column (variable) by an axis on the same plot. As a result, the structure as well as the revelation of the association between the samples (rows) and/or variables (columns) of a (large) data set can easily be explored.