QSPR Prediction of Chromatographic Retention Times of Tea Compounds by Bioplastic Evolution

*Francisco Torrens and Gloria Castellano*

## **Abstract**

Structure-property relationships model the ultrahigh-performance liquid chromatographic retention times of tea compounds. *Bioplastic evolution* presents a viewpoint in evolutionary science. It conjugates the result of acquired characters and associations rising between three rules: *evolutionary indeterminacy*, *morphological determination*, and *natural selection*. It is used to propose the co-ordination index, which is utilized to describe the retentions of tea constituents. In molecules, three properties allow computing the co-ordination descriptor: the molar formation enthalpy, molecular weight, and surface area. The result of dissimilar kinds of characteristics is examined: thermodynamic, *steric*, geometric, lipophilic, etc. The features are molar formation enthalpy, molecular weight, hydrophobic solvent-accessible surface area, decimal logarithm of the 1-octanol/water partition coefficient, etc. in linear and quadratic associations. The formation enthalpy, molecular weight, hydrophobic surface, partition, etc. differentiate the molecular structures of tea components. Feeble quadratic associations result between partition, hydrophobic surface and retention. The morphological and co-ordination descriptors complete the associations.

**Keywords:** biological plastic evolution, morphological index, co-ordination index, formation enthalpy, lipophilicity, solvent-accessible surface, solvation parameter model, metabolomics, metabolic profiling, catechin derivative, polyphenol, green tea, black tea

## **1. Introduction**

Fast separation of complex samples, via high-resolution (HR) chromatography and mass spectrometry (MS), requires meeting the simultaneous need of high sample throughput and high-quality (HQ) data in metabolomics. Hyphenation of ultrahigh-performance liquid chromatography (LC) (UHPLC) and maXis ultra-HR time-of-flight (UHR-TOF)-MS delivers speed without compromising performance factors, e.g., sensitivity, mass accuracy, and resolution. Black tea (BT) and green tea (GT), *Camellia sinensis* L. (Theaceae), account for 95% of the world tea consumption [1]. The health benefits of BT and GT are hypothesized. Understanding the potential health-promoting effects and improvement in quality/taste is interesting. In BT production, GT leaf catechin (GTC) (glycosylated) flavan-3-ol flavonoids are enzymatically oxidized (*fermented*) to yield a complex mixture of products, e.g., theaflavins (TFs) and thearubigins (TRs). Despite the importance of tea beverages, most chemical constituents were not confirmed because of mixture complexity. Antioxidant activity (AOA) of standard (gallated) GTCs decays as follows: () epigallocatechin (EGC) 3*-O-*gallate (EGCg) > (+)-gallocatechin (GC) 3*-O-*gallate (GCg) > ()-epicatechin (EC) 3*-O-*gallate (ECg) > EGC > GC > EC > (+)-catechin (C) [2]. The contents of *cis-*GTCs are the key factors affecting GT AOA. GT, *oolong* (blue) tea (OT), and BT are unoxidized, semi-oxidized, and oxidized, respectively, during production. Darjeeling tea is sold as *BT* but it belongs to OTs. The oxidation grade of tealeaves rises GT < OT < BT. GTCs are excellent electron donors (EDs) and effective traps (*scavengers*) of physiologically relevant *in vitro* reactive oxygen species (ROSs).

(MDs) for tea components. The goal is the corroboration of the values of MDs via their ability to distinguish tea phytochemicals, and their advantage as prognostic MDs for retention, contrasted with formation enthalpy, molecular weight, hydrophobic accessible surface (HBAS) area and partition. Section 2 describes the method. Sections 3 and 4 illustrate and discuss the results. Finally, the last section

*QSPR Prediction of Chromatographic Retention Times of Tea Compounds by Bioplastic Evolution*

Biology presents an important idea ever elucidated in 400 years of experimental science: biological evolution (the other is the existence and organization of the periodic table of the elements). In *allometry* (biological scaling), *biological plastic* (*bioplastic*) *evolution* presents a viewpoint in evolutionary science. It conjugates the result of (1) the acquired characters and (2) associations rising between three rules: *evolutionary indeterminacy*, *morphological determination*, and *natural selection*. The association between morphology and functionality in the living forms stretches out in that the former is the substance foundation of the latter, which is the dynamic result of the former in the background of the relationship between the substantial setting and living substance. Morphology, functionality, energy cost, and vital viability are jointly affected: When a morphology is useful, it achieves its effort with least power charge, and the fundamental feasibility of the organ/organism is the utmost. Counting ideas engage describing *functional co-ordination index I*c: the relationship between the work achieved by morphology *T* and the corresponding *morphological index I*m:

The greater the work *T* attained by a specific morphology *I*m, the greater the *I*c. For an organism, Ruiz-Bustos suggested *I*<sup>m</sup> as the relationship between morpholog-

The equation of *T* by its correspondence in classical mechanics provides

*<sup>T</sup>* <sup>¼</sup> *<sup>W</sup>* � *<sup>x</sup>* � *<sup>d</sup>*<sup>2</sup>

The *I*<sup>c</sup> rises as follows. (1) The greater the body weight at the same journeyed time/space, the greater the *I*c. (2) The *I*<sup>c</sup> is proportional to the gap journeyed in the shortest achievable time. (3) The smaller the body surface, the greater the *I*<sup>c</sup> and

Code SCAP is founded on an algorithm by Hopfinger, parametrized for 1 octanol and water solvents. One can center a *solvation sphere* on every group of the molecule [42, 43]. The intersecting volume *V*<sup>o</sup> between the solvation and the van der Waals (VDW) spheres of the other atoms is computed. The SCAP handles four parameters for a solvent: (1) *n*: utmost number of solvent molecules filling the

*<sup>I</sup>*<sup>c</sup> <sup>¼</sup> *<sup>W</sup>*<sup>2</sup> � *<sup>x</sup>* � *<sup>d</sup>*<sup>2</sup>

function-morphology co-ordination needs lesser power charge.

*I*<sup>c</sup> ¼ *T=I*<sup>m</sup> (1)

*I*<sup>m</sup> ¼ *S=W* (2)

*x=dt*<sup>2</sup> (4)

*<sup>x</sup><sup>=</sup> <sup>S</sup>* � *dt*<sup>2</sup> (5)

*I*<sup>c</sup> ¼ *T=*ð Þ¼ *S=W W* � *T=S* (3)

summarizes our conclusions.

*DOI: http://dx.doi.org/10.5772/intechopen.81735*

**2. Computational method**

ical surface area *S* and body weight *W* [41]:

Replacing Eq. (4) in Eq. (3) gives

**103**

The replacement of Eq. (2) in Eq. (1) turns out to be

Data generated from BT, GT, and Darjeeling tea extracts were analyzed via UHR-TOF-MS, with electrospray ionization (ESI) in negative ion (NI) mode [3]. Mass data and isotopic pattern information in MS/MS-MS spectra enable the sum formula generation. Combining the formulae with database (DB) queries facilitates the identification of unknown compounds. Some tea polyphenolic compounds and metabolites penetrate the blood-brain barrier (BBB) into brain regions, which mediates cognition. In rats, trihydroxybenzoic acid glycoside theogallin or its metabolite cyclitol, cyclic polyol, *cyclo*hexanecarboxylic quinic acid moved via BBB and presented cognition-enhancing activities [4]. The effects of flavonoids on the central nervous system (CNS) were reviewed [5]. Flavan derivative, flavan-3-ol EC, is able to cross BBB more efficiently than stilbenoid resveratrol, which is more hydrophilic. Polyphenols entering the brain were revised [6]. The potential role of GTCs in the prevention of the metabolic syndrome was re-examined [7]. The clinical evidence of GT effects was discussed [8]. The GTCs and caffeine (Caff) and their synergism in body weight regulation were reviewed [9]. The antiobesity effects of GTCs were revised [10]. The chemistry of low-molecular-weight BT polyphenols [11], and secondary ones produced during tea processing [12], was reexamined. The content of Caff decayed during GT oxidation [13–15]. The changes of GT secondary metabolites [14] and phenolics/quality potential of crush, tear, and curl BT [15] were reported during oxidation. The EGCg attenuated lipopolysaccharide (LPS)-induced nitric oxide (nitrogen monoxide, NO) production in cells [16]. The antiviral role of GTCs was reviewed [17]. The EGCg was identified as an inhibitor of phosphoglycerate mutase 1 (PGAM1) [18]. Quantitative analysis of GTCs from GT extract in human plasma was performed via UHPLC-MS [19].

The model is an expansion of solvent-dependent conformational analysis program (SCAP) from 1-octanol/water to other organic solvents [20]. In earlier publications, SCAP was used to compute the partition coefficients of porphyrins, phthalocyanines, benzobisthiazoles, fullerenes, acetanilides, local anesthetics (procaine analogues) [21], enzyme lysozyme [22], barbiturates, hydrocarbons (HCs) [23], polystyrene (PS) [24], Fe/S proteins [25], C-nanotubes (CNTs) [26], Dglucopyranoses, polyiodides, polyiodines, and crown ethers [27]. *Bioplastic evolution* (BPE) and quantitative structure-property relationships (QSPRs) were used for phenylalcohols, 4-alkylanilines [28], aromatics [29], phenylureas [30], pesticides [31], flavonoids [32], isoflavonoids [33], natural sesquiterpene lactones (STLs) [34], coffee chlorogenic acids (CGAs) [35], purine derivative alkaloid methylxanthines (Caff and its metabolites), alkaloid and predominant nicotine metabolite cotinine [36, 37], and tea leaf infusions [38]. Mucoadhesive polymer hyaluronan (HA) favors transdermal penetration absorption of model drug Caff [39, 40]. The present report explains QSPR examination and calculation of the retentions of tea compounds. The aim of this work is to discover features that differentiate tea components consistent with retentions. This study uses molecular descriptors

*QSPR Prediction of Chromatographic Retention Times of Tea Compounds by Bioplastic Evolution DOI: http://dx.doi.org/10.5772/intechopen.81735*

(MDs) for tea components. The goal is the corroboration of the values of MDs via their ability to distinguish tea phytochemicals, and their advantage as prognostic MDs for retention, contrasted with formation enthalpy, molecular weight, hydrophobic accessible surface (HBAS) area and partition. Section 2 describes the method. Sections 3 and 4 illustrate and discuss the results. Finally, the last section summarizes our conclusions.

### **2. Computational method**

enzymatically oxidized (*fermented*) to yield a complex mixture of products, e.g., theaflavins (TFs) and thearubigins (TRs). Despite the importance of tea beverages, most chemical constituents were not confirmed because of mixture complexity. Antioxidant activity (AOA) of standard (gallated) GTCs decays as follows: () epigallocatechin (EGC) 3*-O-*gallate (EGCg) > (+)-gallocatechin (GC) 3*-O-*gallate (GCg) > ()-epicatechin (EC) 3*-O-*gallate (ECg) > EGC > GC > EC > (+)-catechin (C) [2]. The contents of *cis-*GTCs are the key factors affecting GT AOA. GT, *oolong* (blue) tea (OT), and BT are unoxidized, semi-oxidized, and oxidized, respectively, during production. Darjeeling tea is sold as *BT* but it belongs to OTs. The oxidation grade of tealeaves rises GT < OT < BT. GTCs are excellent electron donors (EDs) and effective traps (*scavengers*) of physiologically relevant *in vitro* reactive oxygen

Data generated from BT, GT, and Darjeeling tea extracts were analyzed via UHR-TOF-MS, with electrospray ionization (ESI) in negative ion (NI) mode [3]. Mass data and isotopic pattern information in MS/MS-MS spectra enable the sum formula generation. Combining the formulae with database (DB) queries facilitates the identification of unknown compounds. Some tea polyphenolic compounds and metabolites penetrate the blood-brain barrier (BBB) into brain regions, which mediates cognition. In rats, trihydroxybenzoic acid glycoside theogallin or its metabolite cyclitol, cyclic polyol, *cyclo*hexanecarboxylic quinic acid moved via BBB and presented cognition-enhancing activities [4]. The effects of flavonoids on the central nervous system (CNS) were reviewed [5]. Flavan derivative, flavan-3-ol EC, is able to cross BBB more efficiently than stilbenoid resveratrol, which is more hydrophilic. Polyphenols entering the brain were revised [6]. The potential role of GTCs in the prevention of the metabolic syndrome was re-examined [7]. The clinical evidence of GT effects was discussed [8]. The GTCs and caffeine (Caff) and their synergism in body weight regulation were reviewed [9]. The antiobesity effects of GTCs were revised [10]. The chemistry of low-molecular-weight BT polyphenols [11], and secondary ones produced during tea processing [12], was reexamined. The content of Caff decayed during GT oxidation [13–15]. The changes of GT secondary metabolites [14] and phenolics/quality potential of crush, tear, and curl BT [15] were reported during oxidation. The EGCg attenuated lipopolysaccharide (LPS)-induced nitric oxide (nitrogen monoxide, NO) production in cells [16]. The antiviral role of GTCs was reviewed [17]. The EGCg was identified as an inhibitor of phosphoglycerate mutase 1 (PGAM1) [18]. Quantitative analysis of GTCs from GT extract in human plasma was performed via UHPLC-MS [19]. The model is an expansion of solvent-dependent conformational analysis program (SCAP) from 1-octanol/water to other organic solvents [20]. In earlier publications, SCAP was used to compute the partition coefficients of porphyrins, phthalocyanines, benzobisthiazoles, fullerenes, acetanilides, local anesthetics (procaine analogues) [21], enzyme lysozyme [22], barbiturates, hydrocarbons (HCs) [23], polystyrene (PS) [24], Fe/S proteins [25], C-nanotubes (CNTs) [26], Dglucopyranoses, polyiodides, polyiodines, and crown ethers [27]. *Bioplastic evolution* (BPE) and quantitative structure-property relationships (QSPRs) were used for phenylalcohols, 4-alkylanilines [28], aromatics [29], phenylureas [30], pesticides [31], flavonoids [32], isoflavonoids [33], natural sesquiterpene lactones (STLs) [34], coffee chlorogenic acids (CGAs) [35], purine derivative alkaloid methylxanthines (Caff and its metabolites), alkaloid and predominant nicotine metabolite cotinine [36, 37], and tea leaf infusions [38]. Mucoadhesive polymer hyaluronan (HA) favors transdermal penetration absorption of model drug Caff [39, 40]. The present report explains QSPR examination and calculation of the retentions of tea compounds. The aim of this work is to discover features that differentiate tea components consistent with retentions. This study uses molecular descriptors

species (ROSs).

*Tea - Chemistry and Pharmacology*

**102**

Biology presents an important idea ever elucidated in 400 years of experimental science: biological evolution (the other is the existence and organization of the periodic table of the elements). In *allometry* (biological scaling), *biological plastic* (*bioplastic*) *evolution* presents a viewpoint in evolutionary science. It conjugates the result of (1) the acquired characters and (2) associations rising between three rules: *evolutionary indeterminacy*, *morphological determination*, and *natural selection*. The association between morphology and functionality in the living forms stretches out in that the former is the substance foundation of the latter, which is the dynamic result of the former in the background of the relationship between the substantial setting and living substance. Morphology, functionality, energy cost, and vital viability are jointly affected: When a morphology is useful, it achieves its effort with least power charge, and the fundamental feasibility of the organ/organism is the utmost. Counting ideas engage describing *functional co-ordination index I*c: the relationship between the work achieved by morphology *T* and the corresponding *morphological index I*m:

$$I\_{\mathbf{c}} = T/I\_{\mathbf{m}} \tag{1}$$

The greater the work *T* attained by a specific morphology *I*m, the greater the *I*c. For an organism, Ruiz-Bustos suggested *I*<sup>m</sup> as the relationship between morphological surface area *S* and body weight *W* [41]:

$$I\_{\mathbf{m}} = \mathbb{S}/\mathcal{W} \tag{2}$$

The replacement of Eq. (2) in Eq. (1) turns out to be

$$I\_{\mathbf{c}} = T/(\mathbf{S}/\mathbf{W}) = \mathbf{W} \cdot T/\mathbf{S} \tag{3}$$

The equation of *T* by its correspondence in classical mechanics provides

$$T = W \cdot \propto d^2 \propto /dt^2\tag{4}$$

Replacing Eq. (4) in Eq. (3) gives

$$I\_{\mathbb{C}} = W^2 \cdot \mathbf{x} \cdot d^2 \mathbf{x} / \left(\mathbb{S} \cdot dt^2\right) \tag{5}$$

The *I*<sup>c</sup> rises as follows. (1) The greater the body weight at the same journeyed time/space, the greater the *I*c. (2) The *I*<sup>c</sup> is proportional to the gap journeyed in the shortest achievable time. (3) The smaller the body surface, the greater the *I*<sup>c</sup> and function-morphology co-ordination needs lesser power charge.

Code SCAP is founded on an algorithm by Hopfinger, parametrized for 1 octanol and water solvents. One can center a *solvation sphere* on every group of the molecule [42, 43]. The intersecting volume *V*<sup>o</sup> between the solvation and the van der Waals (VDW) spheres of the other atoms is computed. The SCAP handles four parameters for a solvent: (1) *n*: utmost number of solvent molecules filling the

solvation sphere; (2) Δ*g* o : change of the Gibbs free energy connected with the removal of one solvent molecule out of the solvation sphere [44, 45]; (3) *R*v: radius of the solvation sphere; (4) *V*f: *free volume* available for a solvent molecule in the solvation sphere. In this, part of the volume keeps out the solvent molecules. The volume contains the VDW volume of the group at which the sphere is centered and a volume on behalf of the groups bonded to the central one. The latter is modeled by a set of cylinders. The dissimilarity between the total volume of the solvation sphere and that excluded to the solvent molecules stands for volume *V*<sup>0</sup> , which is accessible for *n* solvent molecules. The *V*<sup>f</sup> is computed as *V*<sup>f</sup> = *V*<sup>0</sup> /*n* � *V*s. Variation of free energy, connected with the removal of all solvent molecules out of the solvation sphere of a group *R*, results in Δ*G*<sup>R</sup> <sup>o</sup> = *n*Δ*g* <sup>o</sup> (1�*V*<sup>o</sup> /*V*<sup>0</sup> ) and the solvation free energy of a molecule <sup>Δ</sup>*G*solv<sup>o</sup> <sup>=</sup> �Σ*R*=1NΔ*G*<sup>R</sup> o . The partition coefficient *P* between 1-octanol and water results in

$$RT\ln P = \Delta G\_{\text{solv}}^{o}(\text{water}) - \Delta G\_{\text{solv}}^{o}(\text{1-octanol}) \tag{6}$$

at a given temperature *T* taken as 298 K, where *R* is the gas constant and Δ*G*solv<sup>o</sup> (1-octanol) and Δ*G*solv<sup>o</sup> (water) the standard-state Gibbs free energies of solvation in kJ�mol�<sup>1</sup> . Extending SCAP for dissimilar solvents, the parameters were adapted, considering the result of relative permittivity and molecular volume on 1-octanol properties. For a general solvent, the utmost number of solvent molecules, which permitted packing the solvation sphere, is connected with the molecular volume of the solvent as follows:

$$n\_s = n\_o (V\_s / V\_o)^{\log \frac{n\_o}{n\_W} / \log \frac{V\_o}{V\_w}} \tag{7}$$

where *V*o, *V*w, and *V*<sup>s</sup> are the molecular volumes of 1-octanol, water, and general solvent, respectively. The *n*o, *n*w, and *n*<sup>s</sup> are the utmost numbers of molecules of 1 octanol, water, and general solvent, respectively, which allowed packing the solvation sphere. The change in the standard Gibbs free energy is connected with the removal of one solvent molecule out of the solvation sphere, Δ*G*<sup>S</sup> o , which is computed via the generalized Born equation

$$
\Delta \mathbf{g}\_s^o = \Delta \mathbf{g}\_o^o (\mathbf{1} - \mathbf{1}/\varepsilon\_s) / (\mathbf{1} - \mathbf{1}/\varepsilon\_o) = \Delta \mathbf{g}\_o^o \varepsilon\_o (\varepsilon\_s - \mathbf{1}) / [\varepsilon\_s (\varepsilon\_o - \mathbf{1})] \tag{8}
$$

where Δ*g*<sup>o</sup> <sup>o</sup> denotes Δ*g* <sup>o</sup> for 1-octanol, and ε<sup>o</sup> and ε<sup>s</sup> are the relative permittivities of 1-octanol and general solvent. The radius of the solvation sphere is connected with the molecular volume of the solvent molecule as follows:

$$R\_{v,s} = R\_{v,o} (V\_s / V\_o)^{1/3} \tag{9}$$

(**Figure 1b**) and corilagin], glycosides [theogallin (**Figure 1c**), digalloyl glucose, and trigalloyl glucose] and GTCs [GC (**Figure 1d**), EGC (**Figure 1e**), EGCg

all the components. The molar formation enthalpy was calculated with code MOPAC-AM1 [47]. The diastereoisomers GC and EGC show similar formation enthalpy and HBAS. Decaffeination does not alter the metabolite composition extensively. Caffeine does not differentiate the samples since the data were

*T*, *S*, and *W* (Eq. (3)): *T* is redescribed as minus standard formation enthalpy

owing to its least *R*<sup>t</sup> (*cf*. **Table 1**). Relative changes (*R*t–*R*<sup>t</sup>

acquired in ESI NI mode where Caff does not ionize.

); *S*, molecular surface area (Å2

(kJmol<sup>1</sup>

**105**

**Figure 1.**

*acid.*

(**Figure 1f**), EC (**Figure 1g**), and ECg]}, UHPLC retention times, *R*t, were obtained by Barsch et al. *Epi*-diastereoisomers show the gallate, etc. residues in *cis-*position. The chromatographic analysis is in accord with the technical literature [46]. Quinic acid was taken as the reference molecule for the retention time *R*<sup>t</sup>

*(a) Quinic acid, (b) gallic acid, (c) theogallin, (d) GC, (e) EGC, (f) EGCg, (g) EC, and (h) coumaroylquinic*

*QSPR Prediction of Chromatographic Retention Times of Tea Compounds by Bioplastic Evolution*

*DOI: http://dx.doi.org/10.5772/intechopen.81735*

In molecular structures, the use of co-ordination MDs needs adapting variables

MDs of the tea components (*cf*. **Table 2**) illustrate that *I*<sup>m</sup> is constant, while *I*<sup>c</sup> rises

o )/*R*<sup>t</sup>

); and *<sup>W</sup>*, molecular weight (gmol<sup>1</sup>

o ,

). The

<sup>o</sup> were computed for

where *R*v,o denotes *R*<sup>v</sup> for 1-octanol. The free volume accessible for a solvent molecule in the solvation sphere is as follows:

$$\mathbf{V}\_{f,\mathbf{s}} = \mathbf{V}\_{f,\mathbf{o}} \mathbf{V}\_{\mathbf{s}} / \mathbf{V}\_{\mathbf{o}} \tag{10}$$

where *V*f,o denotes *V*<sup>f</sup> for 1-octanol.

#### **3. Calculation results**

For the 12 tea components {polyol acids [quinic (*cf*. **Figure 1a**) and coumaroylquinic acids (**Figure 1h**)], non-flavonoid polyphenols [gallic acid *QSPR Prediction of Chromatographic Retention Times of Tea Compounds by Bioplastic Evolution DOI: http://dx.doi.org/10.5772/intechopen.81735*

#### **Figure 1.**

solvation sphere; (2) Δ*g*

*Tea - Chemistry and Pharmacology*

o

and that excluded to the solvent molecules stands for volume *V*<sup>0</sup>

for *n* solvent molecules. The *V*<sup>f</sup> is computed as *V*<sup>f</sup> = *V*<sup>0</sup>

*RT*ln *<sup>P</sup>* <sup>¼</sup> <sup>Δ</sup>*G<sup>o</sup>*

sphere of a group *R*, results in Δ*G*<sup>R</sup>

of a molecule <sup>Δ</sup>*G*solv<sup>o</sup> <sup>=</sup> �Σ*R*=1NΔ*G*<sup>R</sup>

and water results in

the solvent as follows:

in kJ�mol�<sup>1</sup>

: change of the Gibbs free energy connected with the

, which is accessible

/*n* � *V*s. Variation of free

solvð Þ <sup>1</sup>‐octanol (6)

*<sup>V</sup>*<sup>w</sup> (7)

o

<sup>o</sup>*ε*oð Þ *ε*<sup>s</sup> � 1 *=*½ � *ε*sð Þ *ε*<sup>o</sup> � 1 (8)

, which is com-

. The partition coefficient *P* between 1-octanol

) and the solvation free energy

removal of one solvent molecule out of the solvation sphere [44, 45]; (3) *R*v: radius of the solvation sphere; (4) *V*f: *free volume* available for a solvent molecule in the solvation sphere. In this, part of the volume keeps out the solvent molecules. The volume contains the VDW volume of the group at which the sphere is centered and a volume on behalf of the groups bonded to the central one. The latter is modeled by a set of cylinders. The dissimilarity between the total volume of the solvation sphere

energy, connected with the removal of all solvent molecules out of the solvation

solvð Þ� water <sup>Δ</sup>*G<sup>o</sup>*

considering the result of relative permittivity and molecular volume on 1-octanol properties. For a general solvent, the utmost number of solvent molecules, which permitted packing the solvation sphere, is connected with the molecular volume of

*<sup>n</sup>*<sup>s</sup> <sup>¼</sup> *<sup>n</sup>*oð Þ *<sup>V</sup>*s*=V*<sup>o</sup> log *<sup>n</sup>*<sup>o</sup>

removal of one solvent molecule out of the solvation sphere, Δ*G*<sup>S</sup>

with the molecular volume of the solvent molecule as follows:

<sup>o</sup>ð Þ <sup>1</sup> � <sup>1</sup>*=ε*<sup>s</sup> *<sup>=</sup>*ð Þ¼ <sup>1</sup> � <sup>1</sup>*=ε*<sup>o</sup> <sup>Δ</sup>*g<sup>o</sup>*

of 1-octanol and general solvent. The radius of the solvation sphere is connected

where *R*v,o denotes *R*<sup>v</sup> for 1-octanol. The free volume accessible for a solvent

For the 12 tea components {polyol acids [quinic (*cf*. **Figure 1a**) and coumaroylquinic acids (**Figure 1h**)], non-flavonoid polyphenols [gallic acid

puted via the generalized Born equation

<sup>s</sup> <sup>¼</sup> <sup>Δ</sup>*g<sup>o</sup>*

<sup>o</sup> denotes Δ*g*

molecule in the solvation sphere is as follows:

where *V*f,o denotes *V*<sup>f</sup> for 1-octanol.

Δ*g<sup>o</sup>*

**3. Calculation results**

**104**

where Δ*g*<sup>o</sup>

where *V*o, *V*w, and *V*<sup>s</sup> are the molecular volumes of 1-octanol, water, and general solvent, respectively. The *n*o, *n*w, and *n*<sup>s</sup> are the utmost numbers of molecules of 1 octanol, water, and general solvent, respectively, which allowed packing the solvation sphere. The change in the standard Gibbs free energy is connected with the

at a given temperature *T* taken as 298 K, where *R* is the gas constant and Δ*G*solv<sup>o</sup> (1-octanol) and Δ*G*solv<sup>o</sup> (water) the standard-state Gibbs free energies of solvation

. Extending SCAP for dissimilar solvents, the parameters were adapted,

*<sup>n</sup>*w*<sup>=</sup>* log *<sup>V</sup>*<sup>o</sup>

<sup>o</sup> for 1-octanol, and ε<sup>o</sup> and ε<sup>s</sup> are the relative permittivities

*Rv,*<sup>s</sup> <sup>¼</sup> *Rv,*oð Þ *<sup>V</sup>*s*=V*<sup>o</sup> <sup>1</sup>*=*<sup>3</sup> (9)

*Vf,*<sup>s</sup> ¼ *Vf,*o*V*s*=V*<sup>o</sup> (10)

<sup>o</sup> (1�*V*<sup>o</sup>

/*V*<sup>0</sup>

<sup>o</sup> = *n*Δ*g*

o

*(a) Quinic acid, (b) gallic acid, (c) theogallin, (d) GC, (e) EGC, (f) EGCg, (g) EC, and (h) coumaroylquinic acid.*

(**Figure 1b**) and corilagin], glycosides [theogallin (**Figure 1c**), digalloyl glucose, and trigalloyl glucose] and GTCs [GC (**Figure 1d**), EGC (**Figure 1e**), EGCg (**Figure 1f**), EC (**Figure 1g**), and ECg]}, UHPLC retention times, *R*t, were obtained by Barsch et al. *Epi*-diastereoisomers show the gallate, etc. residues in *cis-*position. The chromatographic analysis is in accord with the technical literature [46].

Quinic acid was taken as the reference molecule for the retention time *R*<sup>t</sup> o , owing to its least *R*<sup>t</sup> (*cf*. **Table 1**). Relative changes (*R*t–*R*<sup>t</sup> o )/*R*<sup>t</sup> <sup>o</sup> were computed for all the components. The molar formation enthalpy was calculated with code MOPAC-AM1 [47]. The diastereoisomers GC and EGC show similar formation enthalpy and HBAS. Decaffeination does not alter the metabolite composition extensively. Caffeine does not differentiate the samples since the data were acquired in ESI NI mode where Caff does not ionize.

In molecular structures, the use of co-ordination MDs needs adapting variables *T*, *S*, and *W* (Eq. (3)): *T* is redescribed as minus standard formation enthalpy (kJmol<sup>1</sup> ); *S*, molecular surface area (Å2 ); and *<sup>W</sup>*, molecular weight (gmol<sup>1</sup> ). The MDs of the tea components (*cf*. **Table 2**) illustrate that *I*<sup>m</sup> is constant, while *I*<sup>c</sup> rises


with *W*. The molecular surface and HBAS areas were computed with our code TOPO [48]. The diastereoisomers, GC and EGC, show similar physico/ physiochemical features and BPE MDs.

In the plot of MDs vs. molecular weight *W* (*cf*. **Figure 2**), some points collapse, especially diastereoisomers GC and EGC with similar BPE MDs. The only index that

*QSPR Prediction of Chromatographic Retention Times of Tea Compounds by Bioplastic Evolution*

<sup>o</sup> vs. molar formation enthalpy Δ*H*<sup>f</sup>

where *r* is the correlation coefficient, *s*, the standard deviation, and *F*, the Fisher ratio. The mean absolute percentage error (MAPE) is 21.66% and the approximation error variance (AEV) is 0.3064. The addition of the co-ordination MD *I*<sup>c</sup> betters

Adding the quadratic hydrophobic solvent-accessible surface area betters the fit

*n* ¼ 12 *r* ¼ 0*:*954 *s* ¼ 0*:*887 *F* ¼ 26*:*9

and AEV decays by 70%. The integration of the molar formation enthalpy improves the fit, according to lesser standard deviation, greater Fisher statistic, and

*Variation of chemical indices for tea compounds vs. molecular weight:* y *=* �*300 + 5.42*x*;* y *=* �*10.3 + 4.21*x*;*

*s* ¼ 1*:*540 *F* ¼ 10*:*2 MAPE ¼ 21*:*66% AEV ¼ 0*:*3064 (11)

*<sup>t</sup>* ¼ �0*:*218 þ 0*:*0348*Mw* � 0*:*00456*Ic, n* ¼ 12 *r* ¼ 0*:*864 *s* ¼ 1*:*403 *F* ¼ 13*:*2 MAPE ¼ 20*:*47% AEV ¼ 0*:*2543 (12)

*<sup>t</sup>* <sup>¼</sup> <sup>0</sup>*:*<sup>654</sup> <sup>þ</sup> <sup>0</sup>*:*0153*Mw* � <sup>0</sup>*:*00260*Ic* <sup>þ</sup> <sup>0</sup>*:*0000738HBAS<sup>2</sup>

MAPE ¼ 12*:*33% AEV ¼ 0*:*0922 (13)

<sup>o</sup> and molecular

*,*

*<sup>f</sup>* þ 0*:*0304*Mw, n* ¼ 12 *r* ¼ 0*:*833

is constant is *I*m. The MDs are more responsive to *W* decay: *I*<sup>c</sup> > *T* > *S* > *I*m.

*<sup>t</sup>* <sup>¼</sup> <sup>1</sup>*:*<sup>10</sup> <sup>þ</sup> <sup>0</sup>*:*00484Δ*H<sup>o</sup>*

Changes in (*R*t–*R*<sup>t</sup>

*Rt* � *<sup>R</sup><sup>o</sup> t =R<sup>o</sup>*

*Rt* � *<sup>R</sup><sup>o</sup> t =R<sup>o</sup>*

*Rt* � *Ro t =R<sup>o</sup>*

lesser AEV:

**Figure 2.**

**107**

y *= 51.2 + 0.773*x*;* y *= 1.06–0.000352*x*.*

and AEV decays by 17%.

the fit

o )/*R*<sup>t</sup>

*DOI: http://dx.doi.org/10.5772/intechopen.81735*

weight *M*<sup>w</sup> present correlation. The model is

*a Molar formation enthalpy calculated with MOPAC–AM1. <sup>b</sup> ).*

*HBAS: hydrophobic solvent-accessible surface area (Å2*

#### **Table 1.**

*Retention, formation enthalpy, and hydrophobic-accessible surface area for tea components.*


*a W: molecular weight (gmol<sup>1</sup> ). <sup>b</sup>*

*T: minus standard formation enthalpy (kJmol<sup>1</sup> ). <sup>c</sup>*

*S: molecular surface area (Å2 ). <sup>d</sup>*

*Im: morphological index (molÅ2 g 1 ). <sup>e</sup>*

*Ic: co-ordination index (kJ<sup>g</sup>mol<sup>2</sup> <sup>Å</sup><sup>2</sup> ).*

#### **Table 2.**

*BPE indices for the compounds of tea extracts.*

*QSPR Prediction of Chromatographic Retention Times of Tea Compounds by Bioplastic Evolution DOI: http://dx.doi.org/10.5772/intechopen.81735*

In the plot of MDs vs. molecular weight *W* (*cf*. **Figure 2**), some points collapse, especially diastereoisomers GC and EGC with similar BPE MDs. The only index that is constant is *I*m. The MDs are more responsive to *W* decay: *I*<sup>c</sup> > *T* > *S* > *I*m.

Changes in (*R*t–*R*<sup>t</sup> o )/*R*<sup>t</sup> <sup>o</sup> vs. molar formation enthalpy Δ*H*<sup>f</sup> <sup>o</sup> and molecular weight *M*<sup>w</sup> present correlation. The model is

$$\left(R\_l - R\_t^o\right) / R\_t^o = \textbf{1.10} + \textbf{0.00484} \Delta H\_f^o + \textbf{0.0304} M\_w, n = \textbf{12} \, r = \textbf{0.833}$$

$$s = \textbf{1.540} \, F = \textbf{10.2} \text{ MAPE} = \textbf{21.66\%} \text{ AEV} = \textbf{0.3064} \tag{11}$$

where *r* is the correlation coefficient, *s*, the standard deviation, and *F*, the Fisher ratio. The mean absolute percentage error (MAPE) is 21.66% and the approximation error variance (AEV) is 0.3064. The addition of the co-ordination MD *I*<sup>c</sup> betters the fit

$$\left(R\_l - R\_t^\rho\right) / R\_t^\rho = -0.218 + 0.0348M\_w - 0.00456I\_c, n = 12\text{ }r = 0.864$$

$$s = 1.403 \quad F = 13.2 \quad \text{MAPE} = 20.4796 \text{ AEV} = 0.2543\tag{12}$$

and AEV decays by 17%.

with *W*. The molecular surface and HBAS areas were computed with our code TOPO [48]. The diastereoisomers, GC and EGC, show similar physico/

> *R***<sup>t</sup>** *R***t° (min)**

Quinic acid 0.8 0.0 0.000 1239.5 89.68 Gallic acid 2.4 1.6 2.000 836.0 85.88 Theogallin 2.8 2.0 2.500 1773.2 130.20 Gallocatechin (GC) 3.5 2.7 3.375 1078.1 173.11 Corilagin 4.9 4.1 5.125 2722.5 233.83 Epigallocatechin (EGC) 5.0 4.2 5.250 1063.5 174.31 Digalloyl glucose 5.3 4.5 5.625 2362.3 223.46

Epicatechin (EC) 6.4 5.6 7.000 880.4 202.04 Coumaroylquinic acid 6.7 5.9 7.375 1372.0 265.79 Trigalloyl glucose 6.9 6.1 7.625 2908.9 291.39 Epicatechin gallate (ECg) 7.0 6.2 7.750 1434.9 260.13

*).*

*T* **[kJmol<sup>1</sup> ] b**

Quinic acid 192 1239.5 196.08 1.021 1213.7 Gallic acid 170 836.0 168.23 0.990 844.8 Theogallin 344 1773.2 322.48 0.937 1891.5 Gallocatechin (GC) 306 1078.1 285.27 0.932 1156.4 Corilagin 634 2722.5 518.71 0.818 3327.6 Epigallocatechin (EGC) 306 1063.5 286.51 0.936 1135.8 Digalloyl glucose 484 2362.3 421.88 0.872 2710.1

Epicatechin (EC) 290 880.4 274.36 0.946 930.6 Coumaroylquinic acid 338 1372.0 332.68 0.984 1393.9 Trigalloyl glucose 636 2908.9 550.91 0.866 3358.2 Epicatechin gallate (ECg) 442 1434.9 401.04 0.907 1581.5

*S* **[Å<sup>2</sup> ] c**

458 1590.7 410.89 0.897 1773.1

*I***m [molÅ2 <sup>g</sup><sup>1</sup> ] d**

*I***c [kJ<sup>g</sup>mol<sup>2</sup>**

**<sup>Å</sup><sup>2</sup> ] e**

*Retention, formation enthalpy, and hydrophobic-accessible surface area for tea components.*

**[gmol<sup>1</sup> ] a** **(***R***<sup>t</sup>** *R***t°)/** *R***t°**

6.0 5.2 6.500 1590.7 209.50

**Δ***H***<sup>f</sup> o (kJmol<sup>1</sup> )a**

**HBAS (Å<sup>2</sup> )b**

physiochemical features and BPE MDs.

*Molar formation enthalpy calculated with MOPAC–AM1. <sup>b</sup> HBAS: hydrophobic solvent-accessible surface area (Å2*

**Molecule** *W*

Epigallocatechin gallate

*W: molecular weight (gmol<sup>1</sup> ). <sup>b</sup>*

*S: molecular surface area (Å2 ). <sup>d</sup> Im: morphological index (molÅ2*

*). <sup>e</sup> Ic: co-ordination index (kJ<sup>g</sup>mol<sup>2</sup>*

*T: minus standard formation enthalpy (kJmol<sup>1</sup> ). <sup>c</sup>*

*BPE indices for the compounds of tea extracts.*

*g 1*

*<sup>Å</sup><sup>2</sup> ).*

(EGCg)

*a*

**Table 2.**

**106**

**(min)**

**Molecule** *R***<sup>t</sup>**

*Tea - Chemistry and Pharmacology*

Epigallocatechin gallate

(EGCg)

*a*

**Table 1.**

Adding the quadratic hydrophobic solvent-accessible surface area betters the fit

$$(R\_t - R\_t^\rho) / R\_t^\rho = 0.654 + 0.0153 M\_w - 0.00260 I\_c + 0.0000738 \text{HBAS}^2,$$

$$n = 12 \text{ } r = 0.954 \text{ } s = 0.887 \text{ } F = 26.9$$

$$\text{MAPE} = 12.33\% \text{ } \text{AEV} = 0.0922\tag{13}$$

and AEV decays by 70%. The integration of the molar formation enthalpy improves the fit, according to lesser standard deviation, greater Fisher statistic, and lesser AEV:

**Figure 2.**

*Variation of chemical indices for tea compounds vs. molecular weight:* y *=* �*300 + 5.42*x*;* y *=* �*10.3 + 4.21*x*;* y *= 51.2 + 0.773*x*;* y *= 1.06–0.000352*x*.*

$$\left(R\_t - R\_t^o\right) / R\_t^o = 1.45 + 0.00279 \Delta H\_f^o + 0.0121 M\_w + 0.0000806 \text{HBAS}^2,$$

$$n = 12 \text{ } r = 0.954 \text{ } s = 0.881 \text{ } F = 27.2 \text{ } \text{MAPE} = 12.57\%$$

$$\text{AEV} = 0.0912 \tag{14}$$

analysis of the molecular functions showed a forecast of the experimental elution sequence for the tea components. In order to predict the sequence in tea substances, two- or three-variable models were used in which the appearance of the co-ordination index, molar formation enthalpy, molecular weight, HBAS, or 1-octanol/water partition coefficient reveals the importance of thermodynamic, *steric*, geometric, and lipophilic analysis in retention, allowing the use of such equations in predicting its value. Molecular structures may be differentiated even in other derivatives of tea components not included in the series. Weak MVQR relationships appeared between

*QSPR Prediction of Chromatographic Retention Times of Tea Compounds by Bioplastic Evolution*

The reason why plants accumulate polyphenols is related to their defense system, and their functions depend on chemical reactivity and physico/physiochemical properties. The structural diversity of plant polyphenols in nature indicates that they present different and wide-ranging functions. Some polyphenols, e.g., GTCs and proanthocyanidins, are susceptible to enzymatic and nonenzymatic oxidation depending on the plant. Polyphenol oxidation in plant tissues, e.g., BT production, proceeds with a reduction in oxygen molecules or polyphenol quinines, in which reactivity with proteins and other co-existing compounds plays a role during postharvesting. The secondary polyphenols, produced in plants after physical tissue damage, relate to the plant defense system though many products were not characterized chemically. Artificial processing, e.g., drying, oxidation, and roasting, is different from the natural reactions, e.g., insolubilization and polymerization, occurring in living plants and produces different compounds. Scientific studies indicated that polyphenols in foods present health benefits. Identifying the mechanisms of their production and chemical structures is important. The GT presents the greatest variability in physico/physiochemical properties. Many beneficial effects of GT are related to GTCs, particularly ECg and, especially, EGCg content. The BBB permeability, easy access via the diet, and low toxicity show them as promising molecules, for prevention and treatment of chronic neurodegenerative diseases.

physico/physiochemical properties (log*P* and HBAS) and retention.

*DOI: http://dx.doi.org/10.5772/intechopen.81735*

From the discussion of the present results, the conclusions follow.

components in dissimilar tea samples.

and functional groups.

**109**

1. The object of this work was to build up structure-property relationships for the qualitative and quantitative calculation of the ultrahigh-performance liquid chromatographic retention times of tea components. The outcomes add an augmented scientific knowledge in the field of association calculation of

2. Structure-property relationships result as expected for predicting retention times, for the elucidation of unknown components in metabolomics studies. Code SCAP permits the hydration and solvation free energies, and partition coefficients, which show that for a given atom, energies and partition coefficients are responsive to the occurrence in the molecule of other atoms

3. The parameters needed to compute the co-ordination descriptor are the molar formation enthalpy, molecular weight, and surface area. Linear and quadratic correlation models were obtained for the chromatographic retention time.

4.A benefit of our structure-activity relationships is that they discover feeble quadratic relationships, occurring between the partition coefficient,

**5. Conclusion**

and AEV decays by 70.2%. The formation enthalpy and hydrophobic-accessible surface better the fit

$$\left(R\_t - R\_t^\rho\right) / R\_t^\rho = -1.24 + 0.00111 \Delta H\_f^\rho + 0.0412 \text{HBAS}, n = 12$$

$$r = 0.956 \,\text{s} = 0.820 \, F = 47.3 \,\text{MAPE} = 11.979\,\text{\AA}$$

$$\text{AEV} = 0.0868 \,\text{\AA} \tag{15}$$

and AEV decays by 72%. The quadratic logarithm of the 1-octanol/water partition coefficient improves the fit

$$(R\_l - R\_t^o) / R\_t^o = -1.44 + 0.00187 \Delta H\_f^o + 0.0452 \text{HBS} + 0.0149 (\text{LogP})^2,$$

$$n = 12 \text{ } r = 0.959 \text{ s} = 0.836 \text{ } F = 30.6 \text{ MAPE} = 11.64\% \text{ }$$

$$\text{AEV} = 0.0807 \tag{16}$$

and AEV decays by 74%. However, this development should be taken with care because though the correlation coefficient, MAPE, and AEV enhance (greater *r*, and lesser MAPE and AEV), the standard deviation and Fisher statistic deteriorate (greater *s* and lesser *F*) because of one less degree of freedom in the model: notice three vs. two variables in Eqs. (16) and (15), respectively. Linear equations (11), (12), and (15) are more satisfactory for extrapolation than quadratic equations (13), (14), and (16), which go better with intrapolation. Extra fitting parameters were tested: molecular dipole moment, organic solvent/water partition coefficients, free energies of solvation and transfer from water to organic solvents, molecular volume, surface area, globularity, rugosity, hydrophilic (HLAS) and total solvent-accessible surface (AS) areas, molecular fractal dimension, and fractal dimension averaged for external atoms. Notwithstanding, the results do not better Eqs. (11)–(16).

#### **4. Discussion**

Molecular studies allowed predicting parameters related to phytochemicals, drugs, and metabolite bioactivities. Direct correlation of MDs with activity was obtained. The chromatographic behavior of drugs in phases of different polarity contains information about their pharmacological performance, e.g., barbiturates and neuroleptics. Chromatographic parameters in a polar stationary phase system correlate better with some MDs, whereas Kováts parameters, obtained from the apolar phase interaction, correlate the best with some others. The MDs predict chromatographic parameters, e.g., retention times in gas chromatography (GC)/LC and retention factor *R*<sup>f</sup> in thin-layer chromatography (TLC). Topological MDs (TDs) were used in chromatographic chiral separations. The chromatographic properties of natural phenol/sugar derivatives were predicted by molecular topology (MT). The properties of chiral quinic acid, theogallin, (+)-GC, (�)-EGC, digalloyl glucose, (�)-EGCg, (�)-EC, trigalloyl glucose, and (�)-ECg were forecasted by MT.

This study related LC-MS retentions for tea compounds to MDs. Molecular functions were obtained through multivariate linear (MVLR) and quadratic (MVQR) regressions, which were selected based on their statistical parameters. Regression

#### *QSPR Prediction of Chromatographic Retention Times of Tea Compounds by Bioplastic Evolution DOI: http://dx.doi.org/10.5772/intechopen.81735*

analysis of the molecular functions showed a forecast of the experimental elution sequence for the tea components. In order to predict the sequence in tea substances, two- or three-variable models were used in which the appearance of the co-ordination index, molar formation enthalpy, molecular weight, HBAS, or 1-octanol/water partition coefficient reveals the importance of thermodynamic, *steric*, geometric, and lipophilic analysis in retention, allowing the use of such equations in predicting its value. Molecular structures may be differentiated even in other derivatives of tea components not included in the series. Weak MVQR relationships appeared between physico/physiochemical properties (log*P* and HBAS) and retention.

The reason why plants accumulate polyphenols is related to their defense system, and their functions depend on chemical reactivity and physico/physiochemical properties. The structural diversity of plant polyphenols in nature indicates that they present different and wide-ranging functions. Some polyphenols, e.g., GTCs and proanthocyanidins, are susceptible to enzymatic and nonenzymatic oxidation depending on the plant. Polyphenol oxidation in plant tissues, e.g., BT production, proceeds with a reduction in oxygen molecules or polyphenol quinines, in which reactivity with proteins and other co-existing compounds plays a role during postharvesting. The secondary polyphenols, produced in plants after physical tissue damage, relate to the plant defense system though many products were not characterized chemically. Artificial processing, e.g., drying, oxidation, and roasting, is different from the natural reactions, e.g., insolubilization and polymerization, occurring in living plants and produces different compounds. Scientific studies indicated that polyphenols in foods present health benefits. Identifying the mechanisms of their production and chemical structures is important. The GT presents the greatest variability in physico/physiochemical properties. Many beneficial effects of GT are related to GTCs, particularly ECg and, especially, EGCg content. The BBB permeability, easy access via the diet, and low toxicity show them as promising molecules, for prevention and treatment of chronic neurodegenerative diseases.

## **5. Conclusion**

*Rt* � *<sup>R</sup><sup>o</sup> t =R<sup>o</sup>*

*Tea - Chemistry and Pharmacology*

surface better the fit

*Rt* � *Ro t =R<sup>o</sup>*

**4. Discussion**

**108**

*Rt* � *<sup>R</sup><sup>o</sup> t =R<sup>o</sup>*

tion coefficient improves the fit

*<sup>t</sup>* <sup>¼</sup> <sup>1</sup>*:*<sup>45</sup> <sup>þ</sup> <sup>0</sup>*:*00279Δ*H<sup>o</sup>*

*<sup>t</sup>* ¼ �1*:*<sup>44</sup> <sup>þ</sup> <sup>0</sup>*:*00187Δ*H<sup>o</sup>*

atoms. Notwithstanding, the results do not better Eqs. (11)–(16).

*n* ¼ 12 *r* ¼ 0*:*954 *s* ¼ 0*:*881 *F* ¼ 27*:*2 MAPE ¼ 12*:*57%

and AEV decays by 70.2%. The formation enthalpy and hydrophobic-accessible

*r* ¼ 0*:*956 *s* ¼ 0*:*820 *F* ¼ 47*:*3 MAPE ¼ 11*:*97%

and AEV decays by 72%. The quadratic logarithm of the 1-octanol/water parti-

*n* ¼ 12 *r* ¼ 0*:*959 *s* ¼ 0*:*836 *F* ¼ 30*:*6 MAPE ¼ 11*:*64%

and AEV decays by 74%. However, this development should be taken with care because though the correlation coefficient, MAPE, and AEV enhance (greater *r*, and lesser MAPE and AEV), the standard deviation and Fisher statistic deteriorate (greater *s* and lesser *F*) because of one less degree of freedom in the model: notice three vs. two variables in Eqs. (16) and (15), respectively. Linear equations (11), (12), and (15) are more satisfactory for extrapolation than quadratic equations (13), (14), and (16), which go better with intrapolation. Extra fitting parameters were tested: molecular dipole moment, organic solvent/water partition coefficients, free energies of solvation and transfer from water to organic solvents, molecular volume, surface area, globularity, rugosity, hydrophilic (HLAS) and total solvent-accessible surface (AS) areas, molecular fractal dimension, and fractal dimension averaged for external

Molecular studies allowed predicting parameters related to phytochemicals, drugs, and metabolite bioactivities. Direct correlation of MDs with activity was obtained. The chromatographic behavior of drugs in phases of different polarity contains information about their pharmacological performance, e.g., barbiturates and neuroleptics. Chromatographic parameters in a polar stationary phase system correlate better with some MDs, whereas Kováts parameters, obtained from the apolar phase interaction, correlate the best with some others. The MDs predict chromatographic parameters, e.g., retention times in gas chromatography (GC)/LC and retention factor *R*<sup>f</sup> in thin-layer chromatography (TLC). Topological MDs (TDs) were used in chromatographic chiral separations. The chromatographic properties of natural phenol/sugar derivatives were predicted by molecular topology (MT). The properties of chiral quinic acid, theogallin, (+)-GC, (�)-EGC, digalloyl glucose, (�)-EGCg, (�)-EC, trigalloyl glucose, and (�)-ECg were forecasted by MT.

This study related LC-MS retentions for tea compounds to MDs. Molecular functions were obtained through multivariate linear (MVLR) and quadratic (MVQR) regressions, which were selected based on their statistical parameters. Regression

*<sup>t</sup>* ¼ �1*:*<sup>24</sup> <sup>þ</sup> <sup>0</sup>*:*00111Δ*H<sup>o</sup>*

*<sup>f</sup>* <sup>þ</sup> <sup>0</sup>*:*0121*Mw* <sup>þ</sup> <sup>0</sup>*:*0000806HBAS<sup>2</sup>

AEV ¼ 0*:*0912 (14)

*<sup>f</sup>* þ 0*:*0412HBAS*, n* ¼ 12

AEV ¼ 0*:*0868 (15)

*<sup>f</sup>* <sup>þ</sup> <sup>0</sup>*:*0452HBAS <sup>þ</sup> <sup>0</sup>*:*0149ð Þ *LogP* <sup>2</sup>

AEV ¼ 0*:*0807 (16)

*,*

*,*

From the discussion of the present results, the conclusions follow.


hydrophobic solvent-accessible surface area, and retention. The tendency between the co-ordination index and the molecular weight indicates not only a homogeneous molecular structure of tea components but also the capacity to calculate and adapt their features, which is nontrivial in metabolomics studies.

**References**

**21**:334-350

9173-9186

[1] Graham NH. Green tea composition,

*DOI: http://dx.doi.org/10.5772/intechopen.81735*

[10] Rains TM, Agarwal S, Maki KC. Antiobesity effects of green tea catechins: A mechanistic review. The Journal of Nutritional Biochemistry.

[11] Drynan JW, Clifford MN, Obuchowicz J, Kuhnert N. The

Reports. 2010;**27**:417-462

Lee SJ, Lee CH, et al. <sup>1</sup>

2013;**53**:670-677

289-294

425-428

green tea (*Camelia sinensis*) fermentation. Food Research International. 2011;**44**:597-604

chemistry of low molecular weight black tea polyphenols. Natural Product

[12] Tanaka T, Matsuo Y, Kouno I. Chemistry of secondary polyphenols produced during processing of tea and selected foods. International Journal of Molecular Sciences. 2010;**11**:

[13] Lee JE, Lee BJ, Chung JO, Shin HJ,

metabolomic characterization during

[14] Kim MJ, Maria John KM, Choi JN, Lee S, Kim AJ, Kim YM, et al. Changes in secondary metabolites of green tea during fermentation by *Aspergillus oryzae* and its effect on antioxidant potential. Food Research International.

[15] Maria John KM, Thiruvengadam M, Enkhtaivan G, Kim DH. Variation in major phenolic compounds and quality potential of CTC black tea elicited by *Saccharomyces cercevisiae* and its correlation with antioxidant potential. Industrial Crops and Products. 2014;**55**:

[16] Lee SJ, Kang HW, Lee SY, Hur SJ. Green tea polyphenol epigallocatechin-

lipopolysaccharide-induced nitric oxide production in RAW264.7 cells. Journal of Food and Nutrition Research. 2014;**2**:

3*-O-*gallate attenuates

H NMR-based

2011;**22**:1-7

*QSPR Prediction of Chromatographic Retention Times of Tea Compounds by Bioplastic Evolution*

14-40

chemistry. Preventive Medicine. 1992;

[2] Lee LS, Kim SH, Kim YB, Kim YC.

constituents in green tea with different

antioxidant activity. Molecules. 2014;**19**:

[3] Barsch A, Lohmann W, Zurek G. The need for speed in metabolomics: UHPLC with maXis UHR-Q-TOF-MS analysis of tea extracts. LC•GC Eur. 2010;**23**(The

consumption, and polyphenol

Quantitative analysis of major

plucking periods and their

Applications Book):16-17

2007;**59**:1397-1403

2012;**7**:99-109

2009;**70**:11-24

**49**:83-87

**111**

2010;**100**:42-46

[4] Dimpfel W, Kler A, Kriesl E, Lehnfeld R. Theogallin and L-theanine as active ingredients in decaffeinated green tea extract: II. Characterization in the freely moving rat by means of quantitative field potential analysis. The Journal of Pharmacy and Pharmacology.

[5] Jäger AK, Saaby L. Flavonoids and the CNS. Molecules. 2011;**16**:1471-1485

polyphenols enter the brain and does it matter? Some theoretical and practical considerations. Genes & Nutrition.

[7] Thielecke F, Boschmann M. The potential role of green tea catechins in the prevention of the metabolic syndrome—A review. Phytochemistry.

[8] Clement Y. Can green tea do that? A

[9] Westerterp-Plantenga MS. Green tea catechins, caffeine and body-weight regulation. Physiology and Behavior.

literature review of the clinical evidence. Preventive Medicine. 2009;

[6] Schaffer S, Halliwell B. Do


## **Acknowledgements**

The authors thank support from Generalitat Valenciana (Project No. PROMETEO/2016/094) and Universidad Católica de Valencia *San Vicente Mártir* (Project No. UCV.PRO.17-18.AIV.03).

## **Conflict of interest**

The authors declare no conflict of interest.

## **Author details**

Francisco Torrens<sup>1</sup> \* and Gloria Castellano<sup>2</sup>

1 Institute for Molecular Science, University of Valencia, València, Spain

2 Department of Experimental Sciences and Mathematics, Valencia Catholic University Saint Vincent Martyr, València, Spain

\*Address all correspondence to: torrens@uv.es

© 2018 The Author(s). Licensee IntechOpen. This chapter is distributed under the terms of the Creative Commons Attribution License (http://creativecommons.org/licenses/ by/3.0), which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.

*QSPR Prediction of Chromatographic Retention Times of Tea Compounds by Bioplastic Evolution DOI: http://dx.doi.org/10.5772/intechopen.81735*

## **References**

hydrophobic solvent-accessible surface area, and retention. The tendency between the co-ordination index and the molecular weight indicates not only a homogeneous molecular structure of tea components but also the capacity to calculate and adapt their features, which is nontrivial in metabolomics studies.

5. The result of dissimilar kinds of characteristics was examined: thermodynamic, *steric*, geometric, lipophilic, etc. The molar formation enthalpy, molecular weight, hydrophobic solvent-accessible surface area, partition coefficient, etc. differentiated tea components in linear and quadratic equation models.

6.The morphological and co-ordination descriptors completed multivariable

regression expressions for the chromatographic retention.

The authors thank support from Generalitat Valenciana (Project No. PROMETEO/2016/094) and Universidad Católica de Valencia *San Vicente Mártir*

**Acknowledgements**

*Tea - Chemistry and Pharmacology*

**Conflict of interest**

**Author details**

Francisco Torrens<sup>1</sup>

**110**

(Project No. UCV.PRO.17-18.AIV.03).

The authors declare no conflict of interest.

\* and Gloria Castellano<sup>2</sup>

University Saint Vincent Martyr, València, Spain

\*Address all correspondence to: torrens@uv.es

provided the original work is properly cited.

1 Institute for Molecular Science, University of Valencia, València, Spain

2 Department of Experimental Sciences and Mathematics, Valencia Catholic

© 2018 The Author(s). Licensee IntechOpen. This chapter is distributed under the terms of the Creative Commons Attribution License (http://creativecommons.org/licenses/ by/3.0), which permits unrestricted use, distribution, and reproduction in any medium,

[1] Graham NH. Green tea composition, consumption, and polyphenol chemistry. Preventive Medicine. 1992; **21**:334-350

[2] Lee LS, Kim SH, Kim YB, Kim YC. Quantitative analysis of major constituents in green tea with different plucking periods and their antioxidant activity. Molecules. 2014;**19**: 9173-9186

[3] Barsch A, Lohmann W, Zurek G. The need for speed in metabolomics: UHPLC with maXis UHR-Q-TOF-MS analysis of tea extracts. LC•GC Eur. 2010;**23**(The Applications Book):16-17

[4] Dimpfel W, Kler A, Kriesl E, Lehnfeld R. Theogallin and L-theanine as active ingredients in decaffeinated green tea extract: II. Characterization in the freely moving rat by means of quantitative field potential analysis. The Journal of Pharmacy and Pharmacology. 2007;**59**:1397-1403

[5] Jäger AK, Saaby L. Flavonoids and the CNS. Molecules. 2011;**16**:1471-1485

[6] Schaffer S, Halliwell B. Do polyphenols enter the brain and does it matter? Some theoretical and practical considerations. Genes & Nutrition. 2012;**7**:99-109

[7] Thielecke F, Boschmann M. The potential role of green tea catechins in the prevention of the metabolic syndrome—A review. Phytochemistry. 2009;**70**:11-24

[8] Clement Y. Can green tea do that? A literature review of the clinical evidence. Preventive Medicine. 2009; **49**:83-87

[9] Westerterp-Plantenga MS. Green tea catechins, caffeine and body-weight regulation. Physiology and Behavior. 2010;**100**:42-46

[10] Rains TM, Agarwal S, Maki KC. Antiobesity effects of green tea catechins: A mechanistic review. The Journal of Nutritional Biochemistry. 2011;**22**:1-7

[11] Drynan JW, Clifford MN, Obuchowicz J, Kuhnert N. The chemistry of low molecular weight black tea polyphenols. Natural Product Reports. 2010;**27**:417-462

[12] Tanaka T, Matsuo Y, Kouno I. Chemistry of secondary polyphenols produced during processing of tea and selected foods. International Journal of Molecular Sciences. 2010;**11**: 14-40

[13] Lee JE, Lee BJ, Chung JO, Shin HJ, Lee SJ, Lee CH, et al. <sup>1</sup> H NMR-based metabolomic characterization during green tea (*Camelia sinensis*) fermentation. Food Research International. 2011;**44**:597-604

[14] Kim MJ, Maria John KM, Choi JN, Lee S, Kim AJ, Kim YM, et al. Changes in secondary metabolites of green tea during fermentation by *Aspergillus oryzae* and its effect on antioxidant potential. Food Research International. 2013;**53**:670-677

[15] Maria John KM, Thiruvengadam M, Enkhtaivan G, Kim DH. Variation in major phenolic compounds and quality potential of CTC black tea elicited by *Saccharomyces cercevisiae* and its correlation with antioxidant potential. Industrial Crops and Products. 2014;**55**: 289-294

[16] Lee SJ, Kang HW, Lee SY, Hur SJ. Green tea polyphenol epigallocatechin-3*-O-*gallate attenuates lipopolysaccharide-induced nitric oxide production in RAW264.7 cells. Journal of Food and Nutrition Research. 2014;**2**: 425-428

[17] Xu J, Xu Z, Zheng W. A review of the antiviral role of green tea catechins. Molecules. 2017;**22**:1337; 1-18

[18] Li X, Tang S, Wang QQ, Leung ELH, Jin H, Huang Y, et al. Identification of epigallocatechin-3 gallate as an inhibitor of phosphoglycerate mutase 1. Frontiers in Pharmacology. 2017;**8**:325; 1-9

[19] Park JE, Kim TE, Shin KH. Quantitative analysis of four catechins from green tea extract in human plasma using ultra-performance liquid chromatography–tandem mass spectrometry for pharmacokinetic studies. Molecules. 2018;**23**:984; 1-16

[20] Torrens F, Sánchez-Marín J, Nebot-Gil I. Universal model for the calculation of all organic solvent–water partition coefficients. Journal of Chromatography, A. 1998;**827**:345-358

[21] Torrens F. Universal organic solvent-water partition coefficient model. Journal of Chemical Information and Computer Sciences. 2000;**40**: 236-240

[22] Torrens F. Calculation of partition coefficient and hydrophobic moment of the secondary structure of lysozyme. Journal of Chromatography, A. 2001; **908**:215-221

[23] Torrens F. Free energy of solvation and partition coefficients in methanol– water binary mixtures. Chromatographia. 2001;**53**:S199-S203

[24] Torrens F, Soria V. Stationarymobile phase distribution coefficient for polystyrene standards. Separation Science and Technology. 2002;**37**: 1653-1665

[25] Torrens F. Calculation of organic solvent–water partition coefficients of iron–sulfur protein models. Polyhedron. 2002;**21**:1357-1361

[26] Torrens F. Calculation of solvents and co-solvents of single-wall carbon nanotubes: *Cyclo*pyranoses. Nanotechnology. 2005;**16**:S181-S189

[34] Castellano G, Redondo L, Torrens F. QSAR of natural sesquiterpene lactones as inhibitors of Myb-dependent gene expression. Current Topics in Medicinal

*DOI: http://dx.doi.org/10.5772/intechopen.81735*

[42] Hopfinger AJ. Polymer-solvent interactions for homopolypeptides in aqueous solution. Macromolecules. 1971;

[43] Hopfinger AJ, Battershell RD. Application of SCAP to drug design: 1. Prediction of octanol–water partition coefficients using solvent-dependent conformational analyses. Journal of Medicinal Chemistry. 1976;**19**:569-573

[44] Gibson KD, Scheraga HA.

Sciences of the United States of America. 1967;**58**:420-427

[45] Rekker RF. The Hydrophobic Fragmental Constant. Amsterdam:

[46] Fujioka K, Iwamoto KT, Shima H, Tomaru K, Saito H, Ohtsuka M, et al. The powdering process with a set of ceramic mills for green tea promoted catechin extraction and the ROS inhibition effect. Molecules. 2016;**21**:

[47] Dewar MJS, Zoebisch EG, Healy EF,

[48] Torrens F. Characterizing cavitylike spaces in active-site models of zeolites. Computational Materials

Science. 2003;**27**:96-101

Stewart JJP. AM1: A new general purpose quantum mechanical model. Journal of the American Chemical Society. 1985;**107**:3902-3909

Elsevier; 1976

474; 1-12

Minimization of polypeptide energy, I. Preliminary structures of bovine pancreatic ribonuclease S-peptide. Proceedings of the National Academy of

**4**:731-737

*QSPR Prediction of Chromatographic Retention Times of Tea Compounds by Bioplastic Evolution*

Chemistry. 2017;**17**:3256-3268

[36] Torrens F, Castellano G. QSPR prediction of retention times of methylxanthines and cotinine by bioplastic evolution. International Journal of Quantitative Structure-Property Relationships. 2018;**3**:74-87

classification of caffeine, its

InTech Open; (in press)

2014;**2**:235-247

1901-1913

**113**

[39] Torrens F, Castellano G.

[40] Torrens F, Castellano G.

ethers and hyaluronan in

Ul-Haq Z, Madura JD, editors.

2017. pp. 45-61

pp. 3-51

[35] Torrens F. Castellano G. QSRP prediction of retention times of

chlorogenic acids in coffee by bioplastic evolution. In: Kandemirli F, editor. Quantitative Structure-activity Relationship. Vienna: InTech Open;

[37] Torrens F, Castellano G. Molecular

metabolites and nicotine metabolite. In:

Frontiers in Computational Chemistry. Vol. 4. Hilversum: Bentham; 2018.

[38] Torrens F, Castellano G. Elemental classification of tea leaves infusions: Principal component, cluster and metaanalyses. In: Justino J, editor. Tea: From Chemistry to Pharmacology. Vienna:

Mucoadhesive polymer hyaluronan as biodegradable cationic/zwitterionicdrug delivery vehicle. ADMET DMPK.

Computational study of nanosized drug delivery from *cyclo*dextrins, crown

pharmaceutical formulations. Current Topics in Medicinal Chemistry. 2015;**15**:

[41] Ruíz-Bustos A. La Evolución Plástica. Granada: Andalucía; 1994

[27] Torrens F, Castellano G. (Co-) solvent selection for single-wall carbon nanotubes: Best solvents, acids, superacids and guest–host inclusion complexes. Nanoscale. 2001;**3**: 2494-2510

[28] Torrens F. A new chemical index inspired by biological plastic evolution. Indian Journal of Chemistry Section A: Inorganic, Bio-inorganic, Physical, Theoretical and Analytical Chemistry. 2003;**42**:1258-1263

[29] Torrens F. A chemical index inspired by biological plastic evolution: Valence-isoelectronic series of aromatics. Journal of Chemical Information and Computer Sciences. 2004;**44**:575-581

[30] Torrens F, Castellano G. QSPR prediction of retention times of phenylurea herbicides by biological plastic evolution. Current Drug Safety. 2012;**7**:262-268

[31] Torrens F, Castellano G. QSPR prediction of chromatographic retention times of pesticides: Partition and fractal indices. Journal of Environmental Science and Health, Part B: Pesticides, Food Contaminants, and Agricultural Wastes. 2014;**49**:400-407

[32] Castellano G, González-Santander JL, Lara A, Torrens F. Classification of flavonoid compounds by using entropy of information theory. Phytochemistry. 2013;**93**:182-191

[33] Castellano G, Torrens F. Quantitative structure–antioxidant activity models of isoflavonoids: A theoretical study. International Journal of Molecular Sciences. 2015;**16**: 12891-12906

*QSPR Prediction of Chromatographic Retention Times of Tea Compounds by Bioplastic Evolution DOI: http://dx.doi.org/10.5772/intechopen.81735*

[34] Castellano G, Redondo L, Torrens F. QSAR of natural sesquiterpene lactones as inhibitors of Myb-dependent gene expression. Current Topics in Medicinal Chemistry. 2017;**17**:3256-3268

[17] Xu J, Xu Z, Zheng W. A review of the antiviral role of green tea catechins. [26] Torrens F. Calculation of solvents and co-solvents of single-wall carbon

Nanotechnology. 2005;**16**:S181-S189

[27] Torrens F, Castellano G. (Co-) solvent selection for single-wall carbon

[28] Torrens F. A new chemical index inspired by biological plastic evolution. Indian Journal of Chemistry Section A: Inorganic, Bio-inorganic, Physical, Theoretical and Analytical Chemistry.

[29] Torrens F. A chemical index inspired by biological plastic evolution:

Valence-isoelectronic series of aromatics. Journal of Chemical Information and Computer Sciences.

[30] Torrens F, Castellano G. QSPR prediction of retention times of phenylurea herbicides by biological plastic evolution. Current Drug Safety.

[31] Torrens F, Castellano G. QSPR prediction of chromatographic retention times of pesticides: Partition and fractal indices. Journal of Environmental Science and Health, Part B: Pesticides, Food Contaminants, and Agricultural

[32] Castellano G, González-Santander JL, Lara A, Torrens F. Classification of flavonoid compounds by using entropy of information theory. Phytochemistry.

Wastes. 2014;**49**:400-407

[33] Castellano G, Torrens F. Quantitative structure–antioxidant activity models of isoflavonoids: A theoretical study. International Journal

of Molecular Sciences. 2015;**16**:

2013;**93**:182-191

12891-12906

nanotubes: Best solvents, acids, superacids and guest–host inclusion complexes. Nanoscale. 2001;**3**:

2494-2510

2003;**42**:1258-1263

2004;**44**:575-581

2012;**7**:262-268

nanotubes: *Cyclo*pyranoses.

[18] Li X, Tang S, Wang QQ, Leung

Identification of epigallocatechin-3-

phosphoglycerate mutase 1. Frontiers in

Quantitative analysis of four catechins from green tea extract in human plasma

[20] Torrens F, Sánchez-Marín J, Nebot-Gil I. Universal model for the calculation of all organic solvent–water partition

Chromatography, A. 1998;**827**:345-358

[22] Torrens F. Calculation of partition coefficient and hydrophobic moment of the secondary structure of lysozyme. Journal of Chromatography, A. 2001;

[23] Torrens F. Free energy of solvation and partition coefficients in methanol–

Chromatographia. 2001;**53**:S199-S203

[24] Torrens F, Soria V. Stationarymobile phase distribution coefficient for polystyrene standards. Separation Science and Technology. 2002;**37**:

[25] Torrens F. Calculation of organic solvent–water partition coefficients of iron–sulfur protein models. Polyhedron.

[21] Torrens F. Universal organic solvent-water partition coefficient model. Journal of Chemical Information and Computer Sciences. 2000;**40**:

Molecules. 2017;**22**:1337; 1-18

*Tea - Chemistry and Pharmacology*

ELH, Jin H, Huang Y, et al.

Pharmacology. 2017;**8**:325; 1-9

[19] Park JE, Kim TE, Shin KH.

using ultra-performance liquid chromatography–tandem mass spectrometry for pharmacokinetic studies. Molecules. 2018;**23**:984; 1-16

coefficients. Journal of

236-240

**908**:215-221

1653-1665

**112**

2002;**21**:1357-1361

water binary mixtures.

gallate as an inhibitor of

[35] Torrens F. Castellano G. QSRP prediction of retention times of chlorogenic acids in coffee by bioplastic evolution. In: Kandemirli F, editor. Quantitative Structure-activity Relationship. Vienna: InTech Open; 2017. pp. 45-61

[36] Torrens F, Castellano G. QSPR prediction of retention times of methylxanthines and cotinine by bioplastic evolution. International Journal of Quantitative Structure-Property Relationships. 2018;**3**:74-87

[37] Torrens F, Castellano G. Molecular classification of caffeine, its metabolites and nicotine metabolite. In: Ul-Haq Z, Madura JD, editors. Frontiers in Computational Chemistry. Vol. 4. Hilversum: Bentham; 2018. pp. 3-51

[38] Torrens F, Castellano G. Elemental classification of tea leaves infusions: Principal component, cluster and metaanalyses. In: Justino J, editor. Tea: From Chemistry to Pharmacology. Vienna: InTech Open; (in press)

[39] Torrens F, Castellano G. Mucoadhesive polymer hyaluronan as biodegradable cationic/zwitterionicdrug delivery vehicle. ADMET DMPK. 2014;**2**:235-247

[40] Torrens F, Castellano G. Computational study of nanosized drug delivery from *cyclo*dextrins, crown ethers and hyaluronan in pharmaceutical formulations. Current Topics in Medicinal Chemistry. 2015;**15**: 1901-1913

[41] Ruíz-Bustos A. La Evolución Plástica. Granada: Andalucía; 1994 [42] Hopfinger AJ. Polymer-solvent interactions for homopolypeptides in aqueous solution. Macromolecules. 1971; **4**:731-737

[43] Hopfinger AJ, Battershell RD. Application of SCAP to drug design: 1. Prediction of octanol–water partition coefficients using solvent-dependent conformational analyses. Journal of Medicinal Chemistry. 1976;**19**:569-573

[44] Gibson KD, Scheraga HA. Minimization of polypeptide energy, I. Preliminary structures of bovine pancreatic ribonuclease S-peptide. Proceedings of the National Academy of Sciences of the United States of America. 1967;**58**:420-427

[45] Rekker RF. The Hydrophobic Fragmental Constant. Amsterdam: Elsevier; 1976

[46] Fujioka K, Iwamoto KT, Shima H, Tomaru K, Saito H, Ohtsuka M, et al. The powdering process with a set of ceramic mills for green tea promoted catechin extraction and the ROS inhibition effect. Molecules. 2016;**21**: 474; 1-12

[47] Dewar MJS, Zoebisch EG, Healy EF, Stewart JJP. AM1: A new general purpose quantum mechanical model. Journal of the American Chemical Society. 1985;**107**:3902-3909

[48] Torrens F. Characterizing cavitylike spaces in active-site models of zeolites. Computational Materials Science. 2003;**27**:96-101

**115**

oolong teas [5].

**Chapter 9**

**Abstract**

prostate, lung and breast cancer.

**1. Origin of tea leaves**

Tea Polyphenols Chemistry for

*Ponnusamy Ponmurugan, Shivaji Kavitha, Mani Suganya* 

Tea is one of the most ancient popular beverages and extensively used dietary supplement in the western world. Tea leaves are rich in polyphenols and also well known for its antioxidant properties. In addition, green tea extract contains several polyphenols with antioxidant compounds. The predominant effective antioxidant components are epigallocatechin 3-gallate and epicatechin 3-gallate (monomers). Tea polyphenols have an additional role to induce aroma and taste in beverages. Furthermore, tea polyphenols have multiple applications in food industry and biomedical applications. This chapter will summarise the origin of tea leaves and its beneficial account on antioxidant, food industry (meat products, plant products and fish products) and therapeutic applications against many diseases such as lowering of blood pressure, diabetes, Parkinson's disease and anticancer properties. Mainly tea polyphenols have potential to inhibit the cancer proliferation of skin,

**Keywords:** nanoparticles, quantum dots, tea polyphenols, tea chemistry

'*Camellia sinensis*' is the botanical name of tea plant and was originated from Southeast Asia [1]. Tea was introduced by Portuguese and Chinese during the sixteenth century [2]. During the seventeenth century, drinking of tea became popular in Britain [3]. In the current scenario, tea is one of the most ancient and popular beverages around the world followed by water. Tea is grown primarily in tropical and temperate regions which include China, India, Japan and Sri Lanka [5]. Tea plants were cultivated in several African and American countries. Primarily it has two varieties such as *Camellia sinensis* and *Camellia assamica*, and it belongs to the *Camelliaece* family. Tea plant is an evergreen shrub with optimal range from 15 to 20°C. The *sinensis* strain has originated from China and it produces different categories based on processing [4], such as black tea (wilted and fully oxidized), green tea (unwilted and unoxidized) and oolong tea (wilted, bruised and partially oxidized). Furthermore, *assamica* strain is originated from Assam region, especially in Northern India. Due to its enormous growth, it is a favour for India, Sri Lanka and African countries. But this strain is not used for producing black, white and

Pharmaceutical Applications

*and Balasubramanian Mythili Gnanamangai*

## **Chapter 9**
