## 4 Metabolomics  
Primary and secondary plant metabolites are final receptors of cell biological information flow and their levels effect protein stability and gene expression [25]. Measurements of these metabolites reflect the plant cellular state and produce critical insights into cellular processes that control biochemical phenotypes of the cell, tissue, or even whole organism. Metabolomics allows evaluation of medicinal plants, not only on the basis of pharmacologically important metabolites, but also based on the fingerprints of minor metabolites and bioactive molecules [26; 27]. Further, metabolomicscan also be used for a better characterization and on quality control of plant extracts, timetures, and phytotherapeutic products [26]. In fact, metabolomics is conceived as the comprehensive, qualitative, and quantitative study of all the small molecules in an organism [28]. The four conceptual approaches in metabolomics are:  
1. Target analysis;
2. Metabolite profiling;
3. Metabolomics; and
4. Metabolic fingerprinting [29]. Target analysis covers the identification and quantification of a small set of known metabolites (targets) using a particular analytical technique, with best performance for the chemicals of interest. Metabolomics employs complementary analytical methods to determine and quantify the largest possible number of metabolites, either identified or unknown. Metabolic fingerprinting is applied to generate a metabolic signature or mass profile of the sample of interest for comparison with a large sample population, to track differences between samples. Metabolomics discipline allows one to identify the complete set of metabolites in the cell [12]. Medicinal plant metabolomes are of particular interest as a valuable natural resource for evidence-based development of new phytotherapeutic agents and nutraceuticalids [30; 31; 32; 33; 34]. Platforms dedicated to compare metabolomics are evolving into new technologies to monitor drug metabolism, chemical toxicology, and disease development. Non-target metabolomics is a useful method for the simultaneous analysis of many compounds present in herbal products. LC-MS can determine presence, amount, and sometimes structure of plant metabolites in complex plant mixtures. Nuclear magnetic resonance (NMR) is a common method used in metabolomics, and in contrast to MS-based approaches, in most cases an analytic separation is not required [13]. Pattern recognition, such as principal component analysis (PCA), partial least squares discriminant analysis (PLS-DA), and batch-learning self-organizing map (BL-SOM) analysis are usually employed to reduce a metabolite fingerprint and to classify the analyzed samples [27]. PCA and PLS-DA are latent variable methods, where new components are computed as linear combinations of the original variables. PCA is an unsupervised method and PLS-DA is a supervised method, where the information about each sample's group is defined. Metabolomics typically use multivariate analysis to statistically process the huge amount of analytical chemistry data. PCA and hierarchical cluster analysis allow differentiation of green tea samples from black tea samples [35]. The study of the chemical composition of a typical tea beverage shows that the primary metabolite content is similar (% wt/wt solids) in green and black tea, with 3% amino acids, 6% peptides and proteins, 2% organic acids, 7% sugars, 4% other carbohydrates, and 3% lipids in both [36]. _C. sinensis_ leaf contains a very high level of polyphenols, especially flavonoids, an important characteristic that distinguishes it from other plants [37]. These flavonoids and their oxidative products, formed through fermentation and drying processes, largely produces the color and taste of black tea. The flavonoids content of various tea clones correlates with chalcone synthase activity in tea leaf. [PERSON] et al. [38] collected _C.