Elemental Classification of Tea Leaves Infusions: Principal Component, Cluster and Meta-analyses

*Francisco Torrens and Gloria Castellano*

## **Abstract**

The elemental analysis of 11 teas consumed in Turkey is clustered by principal component analyses (PCAs) of metals and plant cluster analyses (CAs), which agree. Samples group into four classes. Elemental PCA and tea CA allow classifying them and concur. The first PCA axis explains 45%; the first two, 71%; the first three, 85% variance; etc. Different behaviours of teas depend on Cu, etc. They are considered as a good source of Mn, etc. Two elemental classes are distinguished: Cu-K-Mn and Fe-Na-Zn. Teas present adequate elemental contents, good antioxidant capacity and may be used as a functional beverage. They represent plants useful as a natural source for nutraceutical formulations.

**Keywords:** tea leaf, tea infusion, green tea, black tea, element, phytochemical, cytochemical

## **1. Introduction**

Tea is the second popular beverage and plays a role in intake of nutritional/ toxic trace elements [1]. It is used in folk medicine for headache, digestion, diuresis, enhancement of immune defence, as an energizer and to prolong life [2]. Epidemiological and pharmacological studies link its consumption to a risk reduction of cardiovascular diseases (CVDs), high cholesterol, arthritis, osteoporosis and dental caries [3]. Leaves' metallic composition is different according to the type and geological source [4]. The chemical composition of tea and its leaves is object of medical and toxicological studies [5]. Investigations were carried out to determine leaves and infusion mineral levels [6–8]. Its elemental contents were determined via analytical methods (e.g. atomic absorption spectrometry (AAS) [9], inductively coupled plasma (ICP)-atomic emission spectrometry (AES) [10], ICP-mass spectrometry (MS) [11], thermal neutron activation analysis (TNAA) [12], ion chromatography [13]). Microwave digestion less contaminates a sample, minimises volatile analyte losses, uses small acids amounts and shortens digestion times [14–16].

Tea trace elements were determined in producing countries [17–19]. Aksuner et al. reported elemental analysis of teas consumed in Turkey (cf. **Table 1**) [20]. Potassium was suggested to be incorporated within a binding ligand in tea leaves. Sodium content showed variability. Because of its biochemical importance, Mn was the most analysed element in tea leaves. Zinc is responsible for enzymatic processes and involved in the working of genetic materials, proteins and immune reactions. Copper is a micronutrient, but it is phytotoxic at high concentrations. Iron is essential, necessary for haemoglobin formation and oxidative processes of living tissues. Nickel is moderately toxic, but it leads to problems, e.g. respiratory system cancer.

**2. Computational method**

*DOI: http://dx.doi.org/10.5772/intechopen.81379*

*<sup>i</sup>*¼1ð Þ *xi* � *<sup>x</sup>* ð Þ *xi* � *<sup>x</sup>* <sup>0</sup>

loading vectors and computes co-ordinates vs. *<sup>P</sup>*~*<sup>j</sup>*

co-ordinate system, projected data point is computed fitting

*<sup>S</sup>* <sup>¼</sup> <sup>1</sup>*=*ð Þ *<sup>n</sup>* � <sup>1</sup> <sup>∑</sup>*<sup>n</sup>*

*<sup>L</sup>*<sup>~</sup> <sup>¼</sup> <sup>~</sup>*lj* � � *j*

The PCA is a dimension reduction technique [35–40]. From original variables *X*j,

. For every loading vector *<sup>P</sup>*~*<sup>j</sup>*

, yielding scores

ð Þ x*<sup>i</sup>* � x (1)

<sup>x</sup>^*<sup>i</sup>* <sup>¼</sup> <sup>x</sup> <sup>þ</sup> *<sup>P</sup>*~~t*<sup>i</sup>* (2)

≥80% (3)

(4)

, matching eigen-

� �. Loading

PCA builds orthogonal variables *<sup>P</sup>*~*<sup>j</sup>* linear combinations of mean-centred ones *<sup>X</sup>*~*<sup>j</sup>* <sup>¼</sup> *Xj* � *Xj* corresponding to eigenvectors of sample covariance matrix

vectors are sorted in decaying eigenvalues. First *k* PCs explain most variability. After selecting *k*, one projects *p*-dimensional data on to subspace spanned by *k*

<sup>~</sup>t*<sup>i</sup>* <sup>¼</sup> *<sup>P</sup>*~<sup>0</sup>

for every *i* = 1, …, *n* having trivially zero mean. With respect to original

Loading matrix *<sup>P</sup>*<sup>~</sup> (*<sup>p</sup>* � *<sup>k</sup>*) contains loadings column-wise and diagonal one

∑ *p*

*j*¼1 ~*lj* !

The CA encompasses different classification algorithms [41, 42]. The starting point is *n* � *p* data matrix **X** containing *p* components measured in *n* samples. One assumes data were preprocessed to remove artefacts and missing values imputed. The CA organises samples into small number of clusters such that samples within

*i*¼1

(e.g. Manhattan, *l*1; Euclidean, *l*<sup>2</sup> distances). Comparing samples, *Pearson's cor-*

*<sup>i</sup>*¼<sup>1</sup>ð Þ *xi* � *<sup>x</sup>*

Elemental contents of 11 teas from Aksuner et al. were used as data. The PCC matrix **R** was computed between plants, and the upper triangle turns out to be

� � � � *q* � �<sup>1</sup>*=<sup>q</sup>*

*xi* � *x*<sup>0</sup> *i*

*<sup>i</sup>*¼<sup>1</sup>ð Þ *xi* � *<sup>x</sup> <sup>x</sup>*<sup>0</sup>

2 ∑*p <sup>i</sup>*¼<sup>1</sup> *<sup>x</sup>*<sup>0</sup> *<sup>i</sup>* � *x* <sup>0</sup> � �<sup>2</sup> h i<sup>1</sup>*=*<sup>2</sup> (5)

� �*=p* is a measure of mean value for sample *x* [43–49].

*<sup>i</sup>* � *<sup>x</sup>* � �<sup>0</sup>

(*k* � *k*), eigenvalues. Loadings *k* explain variation:

∑ *k j*¼1 ~*lj* !,

cluster are similar. Distances *l*<sup>q</sup> between samples *x*,*x*<sup>0</sup> ∈ R*<sup>p</sup>* are

*<sup>x</sup>* � *<sup>x</sup>* � <sup>0</sup> � � � *<sup>q</sup>* ¼ ∑ *p*

<sup>0</sup> � � <sup>¼</sup> <sup>∑</sup>*<sup>p</sup>*

∑*p*

*relation coefficient* (PCC) is advantageous:

*<sup>i</sup>*¼<sup>1</sup>*xi*

where *<sup>x</sup>* <sup>¼</sup> <sup>∑</sup>*<sup>p</sup>*

**3. Calculation results**

**87**

*r x* � *x*

value <sup>~</sup>*lj* of *<sup>S</sup>* tells how much data variability is explained: <sup>~</sup>*lj* <sup>¼</sup> Var *<sup>P</sup>*~*<sup>j</sup>*

*Elemental Classification of Tea Leaves Infusions: Principal Component, Cluster…*

Effects of chronic ingestion of catechin-rich green tea were reported on hepatic gene expression of gluconeogenic enzymes in rats [21]. Effects of a catechin-free fraction derived from green tea on gene expression of enzymes related to lipid metabolism in the mouse liver were informed [22]. Beneficial effects of tea and green tea catechin epigallocatechin-3-gallate on obesity were published [23]. Epigallocatechin-3-gallate was identified as an inhibitor of phosphoglycerate mutase 1 (PGAM1) [24]. The relationship between the phytochemical profile of different teas with relative antioxidant and anti-inflammatory capacities was shown [25]. Antimicrobial activity of tea tree oil vs. pathogenic bacteria and comparison of its effectiveness with eucalyptus oil, lemongrass oil and conventional antibiotics were informed [26]. Earlier publications in *Nereis* classified yams [27], lactic acid bacteria (LABs) [28], fruits [29], food spices [30], chlorogenic acids (CGAs) in coffee [31], methylxanthines, cotinine [32], caffeine (caff), its metabolites, nicotine metabolite [33] and tea compounds [34] by PCA, CA and meta-analysis. The main aim of the present report is to develop code learning potentialities, and since tea elements are more naturally described via varying size-structured representation, find general approaches to information processing. In view of tea ethnomedicinal and nutritional benefits, the objective was to cluster them with PCA/CA, which differentiated metals. Section 2 describes the computational method. Sections 3 and 4 illustrate and discuss the calculation results. Finally, the last section summarises our conclusions.


#### **Table 1.**

*Elemental analysis of tea infusions (mg<sup>L</sup><sup>1</sup> ) (*n *= 3).* *Elemental Classification of Tea Leaves Infusions: Principal Component, Cluster… DOI: http://dx.doi.org/10.5772/intechopen.81379*

#### **2. Computational method**

Tea trace elements were determined in producing countries [17–19]. Aksuner et al. reported elemental analysis of teas consumed in Turkey (cf. **Table 1**) [20]. Potassium was suggested to be incorporated within a binding ligand in tea leaves. Sodium content showed variability. Because of its biochemical importance, Mn was the most analysed element in tea leaves. Zinc is responsible for enzymatic processes and involved in the working of genetic materials, proteins and immune reactions. Copper is a micronutrient, but it is phytotoxic at high concentrations. Iron is essential, necessary for haemoglobin formation and oxidative processes of living tissues. Nickel is moderately toxic, but it leads to problems, e.g. respiratory system

Effects of chronic ingestion of catechin-rich green tea were reported on hepatic gene expression of gluconeogenic enzymes in rats [21]. Effects of a catechin-free fraction derived from green tea on gene expression of enzymes related to lipid metabolism in the mouse liver were informed [22]. Beneficial effects of tea and green tea catechin epigallocatechin-3-gallate on obesity were published [23]. Epigallocatechin-3-gallate was identified as an inhibitor of phosphoglycerate mutase 1 (PGAM1) [24]. The relationship between the phytochemical profile of different teas with relative antioxidant and anti-inflammatory capacities was shown [25]. Antimicrobial activity of tea tree oil vs. pathogenic bacteria and comparison of its effectiveness with eucalyptus oil, lemongrass oil and conventional antibiotics were informed [26]. Earlier publications in *Nereis* classified yams [27], lactic acid bacteria (LABs) [28], fruits [29], food spices [30], chlorogenic acids (CGAs) in coffee [31], methylxanthines, cotinine [32], caffeine (caff), its metabolites, nicotine metabolite [33] and tea compounds [34] by PCA, CA and meta-analysis. The main aim of the present report is to develop code learning potentialities, and since tea elements are more naturally described via varying size-structured representation, find general approaches to information processing. In view of tea ethnomedicinal and nutritional benefits, the objective was to cluster them with PCA/CA, which differentiated metals. Section 2 describes the computational method. Sections 3 and 4 illustrate and discuss the calculation results.

**Sample Cua Fe K Mn Na Ni Zn** 1. Brand A black tea 0.112 0.240 198 8.17 0.322 <0.200 0.148 2. Brand C black tea 0.102 0.378 180 6.49 0.380 <0.200 0.130 3. Brand D black tea 0.143 0.460 194 6.95 0.432 <0.200 0.165 4. East Black Sea black tea 0.130 0.344 173 7.75 0.298 <0.200 0.137 5. Pure Ceylon tea 0.126 0.291 167 7.08 0.287 <0.200 0.197 6. Green tea 0.108 0.270 149 5.41 0.657 <0.200 0.152 7. Sage tea 0.078 2.85 179 0.552 1.08 <0.200 0.204 8. Herbal mixture tea 0.071 1.17 171 3.09 4.39 <0.200 0.168 9. Linden tea 0.090 1.11 185 1.10 0.575 <0.200 0.171 10. Rosehip tea <0.060 1.15 86 2.47 0.611 <0.200 0.114 11. Apple tea <0.060 0.240 188 1.28 0.598 <0.200 0.102

*) (*n *= 3).*

Finally, the last section summarises our conclusions.

*Elements:* i*1, Cu;* i*2, Fe;* i*3, K;* i*4, Mn;* i*5, Na; and* i*6, Zn.*

*Elemental analysis of tea infusions (mg<sup>L</sup><sup>1</sup>*

cancer.

*Tea - Chemistry and Pharmacology*

*a*

**86**

**Table 1.**

The PCA is a dimension reduction technique [35–40]. From original variables *X*j, PCA builds orthogonal variables *<sup>P</sup>*~*<sup>j</sup>* linear combinations of mean-centred ones *<sup>X</sup>*~*<sup>j</sup>* <sup>¼</sup> *Xj* � *Xj* corresponding to eigenvectors of sample covariance matrix *<sup>S</sup>* <sup>¼</sup> <sup>1</sup>*=*ð Þ *<sup>n</sup>* � <sup>1</sup> <sup>∑</sup>*<sup>n</sup> <sup>i</sup>*¼1ð Þ *xi* � *<sup>x</sup>* ð Þ *xi* � *<sup>x</sup>* <sup>0</sup> . For every loading vector *<sup>P</sup>*~*<sup>j</sup>* , matching eigenvalue <sup>~</sup>*lj* of *<sup>S</sup>* tells how much data variability is explained: <sup>~</sup>*lj* <sup>¼</sup> Var *<sup>P</sup>*~*<sup>j</sup>* � �. Loading vectors are sorted in decaying eigenvalues. First *k* PCs explain most variability. After selecting *k*, one projects *p*-dimensional data on to subspace spanned by *k* loading vectors and computes co-ordinates vs. *<sup>P</sup>*~*<sup>j</sup>* , yielding scores

$$
\tilde{\mathbf{t}}\_i = \tilde{P}'(\mathbf{x}\_i - \overline{\mathbf{x}}) \tag{1}
$$

for every *i* = 1, …, *n* having trivially zero mean. With respect to original co-ordinate system, projected data point is computed fitting

$$
\hat{\mathbf{x}}\_i = \overline{\mathbf{x}} + \tilde{P}\overline{\mathbf{f}}\_i \tag{2}
$$

Loading matrix *<sup>P</sup>*<sup>~</sup> (*<sup>p</sup>* � *<sup>k</sup>*) contains loadings column-wise and diagonal one *<sup>L</sup>*<sup>~</sup> <sup>¼</sup> <sup>~</sup>*lj* � � *j* (*k* � *k*), eigenvalues. Loadings *k* explain variation:

$$
\left(\left(\sum\_{j=1}^{k}\tilde{l}\_{j}\right)\right)\bigg/\left(\left(\sum\_{j=1}^{p}\tilde{l}\_{j}\right)\geq \mathbf{80}\,\forall\,\tag{3}
$$

The CA encompasses different classification algorithms [41, 42]. The starting point is *n* � *p* data matrix **X** containing *p* components measured in *n* samples. One assumes data were preprocessed to remove artefacts and missing values imputed. The CA organises samples into small number of clusters such that samples within cluster are similar. Distances *l*<sup>q</sup> between samples *x*,*x*<sup>0</sup> ∈ R*<sup>p</sup>* are

$$\left. \right| \left| \mathbf{x} - \mathbf{x}' \right| \big|\_{q} = \left( \sum\_{i=1}^{p} \left| \mathbf{x}\_{i} - \mathbf{x}\_{i}' \right|^{q} \right)^{1/q} \tag{4}$$

(e.g. Manhattan, *l*1; Euclidean, *l*<sup>2</sup> distances). Comparing samples, *Pearson's correlation coefficient* (PCC) is advantageous:

$$r\left(\mathbf{x} - \mathbf{x}'\right) = \frac{\sum\_{i=1}^{p} (\boldsymbol{\omega}\_i - \overline{\boldsymbol{\omega}}) \left(\boldsymbol{\omega}\_i' - \overline{\boldsymbol{\omega}}'\right)}{\left[\sum\_{i=1}^{p} (\boldsymbol{\omega}\_i - \overline{\boldsymbol{\omega}})^2 \sum\_{i=1}^{p} \left(\boldsymbol{\omega}\_i' - \overline{\boldsymbol{\omega}}'\right)^2\right]^{1/2}} \tag{5}$$

where *<sup>x</sup>* <sup>¼</sup> <sup>∑</sup>*<sup>p</sup> <sup>i</sup>*¼<sup>1</sup>*xi* � �*=p* is a measure of mean value for sample *x* [43–49].

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

Elemental contents of 11 teas from Aksuner et al. were used as data. The PCC matrix **R** was computed between plants, and the upper triangle turns out to be


Correlations are maximum between teas {1–6} and {7–11} (e.g. *R*1,2 = *R*7,8 = 1.000), slightly greater than combining both types, e.g. *R*1,7 = 0.999. All are illustrated in the partial correlation diagram (PCD) that could contain high (*r* ≥ 0.75), medium (0.50 ≤ *r* < 0.75), low (0.25 ≤ *r* < 0.50) and zero (*r* < 0.25) partial correlations. All 55 pairs of teas show high partial correlations (cf. **Figure 1**, *red*). The corresponding interpretation is that all teas present similar composition. The PCD is in qualitative agreement with previous results.

The dendrogram of teas according to elemental analysis (cf. **Figure 2**) shows different behaviour depending on metals Cu, Fe, K, Mn, Na and Zn. Four classes are clearly recognised:

(1,4,5)(2,3,6)(7,9,11)(8,10)

Plants in classes 1–3 are clearly distinguished: brand A, East Black Sea black (BTs) and Pure Ceylon teas present high contents of metals K and Mn and are grouped into class 1; brand C/D BTs and green teas (GT) show high heavy metal Cu and are

included in cluster 2; sage, Linden and apple teas have high heavy metals Fe and Zn and are taken as class 3; herbal mixture and rosehip teas present high alkaline Na and form cluster 4. Manganese level results higher in BT than herbal and GT infusions. Content of Mn is greatest for brand A BT. The plants in the same class appear highly

*Elemental Classification of Tea Leaves Infusions: Principal Component, Cluster…*

*DOI: http://dx.doi.org/10.5772/intechopen.81379*

The radial tree (cf. **Figure 3**) shows different behaviour of teas depending on Cu, etc. The same classes above are clearly recognised in agreement with PCD and dendrogram (**Figures 1** and **2**). Again, plants with high K and Mn are grouped into cluster 1, etc. The split graph for 11 teas in **Table 1** (cf. **Figure 4**) shows that teas 1, 4 and 5 as

*Principal components* (PCs). PCA allows *summarising* information contained in **X**-matrix. It decomposes **X**-matrix as product of matrices **P** and **T**. *Loading matrix* **P** with information about variables contains a few vectors: PCs that are obtained as linear combinations of original *X*-variables. In *score matrix* **T** with information about objects, every object is described by projections on to PCs instead of original variables: **X** = **TP**<sup>0</sup> + **E**, where<sup>0</sup> denotes transpose matrix. Information not contained in matrices remains *unexplained* X*-variance* in *residual matrix* **E**. Every PCi is a new

well as 2, 3 and 6 collapse. It reveals conflicting relationships between classes because of interdependences [50]. It indicates spurious relations between groupings 3 and 4 resulting from base composition effects. It illustrates different behaviours of plants depending on Cu, etc. It is in qualitative agreement with PCD and binary/

correlated in PCD (**Figure 1**).

*Dendrogram of teas according to elemental analysis.*

**Figure 2.**

**89**

radial trees (**Figures 1**–**3**).

**Figure 1.** *Partial correlation diagram showing all 55 high (*red*) partial correlations.*

*Elemental Classification of Tea Leaves Infusions: Principal Component, Cluster… DOI: http://dx.doi.org/10.5772/intechopen.81379*

included in cluster 2; sage, Linden and apple teas have high heavy metals Fe and Zn and are taken as class 3; herbal mixture and rosehip teas present high alkaline Na and form cluster 4. Manganese level results higher in BT than herbal and GT infusions. Content of Mn is greatest for brand A BT. The plants in the same class appear highly correlated in PCD (**Figure 1**).

The radial tree (cf. **Figure 3**) shows different behaviour of teas depending on Cu, etc. The same classes above are clearly recognised in agreement with PCD and dendrogram (**Figures 1** and **2**). Again, plants with high K and Mn are grouped into cluster 1, etc.

The split graph for 11 teas in **Table 1** (cf. **Figure 4**) shows that teas 1, 4 and 5 as well as 2, 3 and 6 collapse. It reveals conflicting relationships between classes because of interdependences [50]. It indicates spurious relations between groupings 3 and 4 resulting from base composition effects. It illustrates different behaviours of plants depending on Cu, etc. It is in qualitative agreement with PCD and binary/ radial trees (**Figures 1**–**3**).

*Principal components* (PCs). PCA allows *summarising* information contained in **X**-matrix. It decomposes **X**-matrix as product of matrices **P** and **T**. *Loading matrix* **P** with information about variables contains a few vectors: PCs that are obtained as linear combinations of original *X*-variables. In *score matrix* **T** with information about objects, every object is described by projections on to PCs instead of original variables: **X** = **TP**<sup>0</sup> + **E**, where<sup>0</sup> denotes transpose matrix. Information not contained in matrices remains *unexplained* X*-variance* in *residual matrix* **E**. Every PCi is a new

**R** ¼

0

*Tea - Chemistry and Pharmacology*

BBBBBBBBBBBBBBBBBBBBB@

agreement with previous results.

(1,4,5)(2,3,6)(7,9,11)(8,10)

clearly recognised:

**Figure 1.**

**88**

*Partial correlation diagram showing all 55 high (*red*) partial correlations.*

*:*000 1*:*000 1*:*000 1*:*000 1*:*000 1*:*000 0*:*999 0*:*999 0*:*999 1*:*000 0*:*999 *:*000 1*:*000 1*:*000 1*:*000 1*:*000 0*:*999 0*:*999 1*:*000 1*:*000 1*:*000 *:*000 1*:*000 1*:*000 1*:*000 0*:*999 0*:*999 1*:*000 1*:*000 1*:*000 *:*000 1*:*000 1*:*000 0*:*999 0*:*999 0*:*999 1*:*000 0*:*999 *:*000 1*:*000 0*:*999 0*:*999 0*:*999 1*:*000 0*:*999 *:*000 0*:*999 1*:*000 1*:*000 1*:*000 1*:*000 *:*000 1*:*000 1*:*000 1*:*000 1*:*000 *:*000 1*:*000 1*:*000 1*:*000 *:*000 1*:*000 1*:*000 *:*000 1*:*000 1*:*000

Correlations are maximum between teas {1–6} and {7–11} (e.g. *R*1,2 = *R*7,8 = 1.000), slightly greater than combining both types, e.g. *R*1,7 = 0.999. All are illustrated in the partial correlation diagram (PCD) that could contain high (*r* ≥ 0.75), medium (0.50 ≤ *r* < 0.75), low (0.25 ≤ *r* < 0.50) and zero (*r* < 0.25) partial correlations. All 55 pairs of teas show high partial correlations (cf. **Figure 1**, *red*). The corresponding interpretation is that all teas present similar composition. The PCD is in qualitative

The dendrogram of teas according to elemental analysis (cf. **Figure 2**) shows different behaviour depending on metals Cu, Fe, K, Mn, Na and Zn. Four classes are

Plants in classes 1–3 are clearly distinguished: brand A, East Black Sea black (BTs) and Pure Ceylon teas present high contents of metals K and Mn and are grouped into class 1; brand C/D BTs and green teas (GT) show high heavy metal Cu and are

1

CCCCCCCCCCCCCCCCCCCCCA

co-ordinate expressed as linear combination of old *x*j: PCi = Σj*b*ij*x*j. New co-ordinates

**Factor Eigenvalue Percentage of variance Cumulative percentage of variance**

*F*<sup>1</sup> 2.67023632 44.50 44.50 *F*<sup>2</sup> 1.60820005 26.80 71.31 *F*<sup>3</sup> 0.81249949 13.54 84.85 *F*<sup>4</sup> 0.68779941 11.46 96.31 *F*<sup>5</sup> 0.15511961 2.59 98.90 *F*<sup>6</sup> 0.06614511 1.10 100.00

*Importance of PCA factors for the elemental analysis of tea infusions.*

**Table 2.**

**Figure 5.**

**91**

*PCA scores plot of teas according to elemental analysis.*

PCi are *scores* (*factors*), while coefficients *b*ij are *loadings*. Scores are ordered according to information content vs. total variance among objects. *Score*-*score plots* show compound positions in new co-ordinate system, while *loading*-*loading plots* show location of features that represent compounds in new coordination. Properties of PCs follow: (1) they are extracted by decaying importance; (2) every PC is orthogonal to each other. A PCA was performed for teas. Importance of PCA factors *F*1–<sup>6</sup> for elements (cf. **Table 2**) shows that both *F*<sup>1</sup> and *F*<sup>2</sup> present the corresponding eigenvalue greater than one. Factor *F*<sup>1</sup> explains 45% variance (55% error); *F*1/2, 71% variance (29% error); *F*1–3, 85% variance (15% error); etc. Nickel was not included because it cannot distinguish teas. For *F*1, variable *i*<sup>4</sup> shows greatest weight; however, *F*<sup>1</sup> cannot be reduced to two variables {*i*1,*i*4} without a 40% error. For *F*2, variable *i*<sup>6</sup> presents greatest weight; notwithstanding, *F*<sup>2</sup> cannot be reduced to two variables {*i*3,*i*6} without a 26% error. For *F*3, variable *i*<sup>5</sup> assigns greatest weight; nevertheless, *F*<sup>3</sup> cannot be reduced to two variables {*i*3,*i*5} without 23% error, etc. Scores plot of PCA *F*2–*F*<sup>1</sup> for teas (cf. **Figure 5**) illustrates different behaviours depending on Cu, etc. The four clusters above are clearly distinguished: class 1 with

*Elemental Classification of Tea Leaves Infusions: Principal Component, Cluster…*

*DOI: http://dx.doi.org/10.5772/intechopen.81379*

**Figure 3.** *Radial tree of teas according to elemental analysis.*

**Figure 4.** *Split graph of teas according to elemental analysis.*

*Elemental Classification of Tea Leaves Infusions: Principal Component, Cluster… DOI: http://dx.doi.org/10.5772/intechopen.81379*

co-ordinate expressed as linear combination of old *x*j: PCi = Σj*b*ij*x*j. New co-ordinates PCi are *scores* (*factors*), while coefficients *b*ij are *loadings*. Scores are ordered according to information content vs. total variance among objects. *Score*-*score plots* show compound positions in new co-ordinate system, while *loading*-*loading plots* show location of features that represent compounds in new coordination. Properties of PCs follow: (1) they are extracted by decaying importance; (2) every PC is orthogonal to each other. A PCA was performed for teas. Importance of PCA factors *F*1–<sup>6</sup> for elements (cf. **Table 2**) shows that both *F*<sup>1</sup> and *F*<sup>2</sup> present the corresponding eigenvalue greater than one. Factor *F*<sup>1</sup> explains 45% variance (55% error); *F*1/2, 71% variance (29% error); *F*1–3, 85% variance (15% error); etc. Nickel was not included because it cannot distinguish teas. For *F*1, variable *i*<sup>4</sup> shows greatest weight; however, *F*<sup>1</sup> cannot be reduced to two variables {*i*1,*i*4} without a 40% error. For *F*2, variable *i*<sup>6</sup> presents greatest weight; notwithstanding, *F*<sup>2</sup> cannot be reduced to two variables {*i*3,*i*6} without a 26% error. For *F*3, variable *i*<sup>5</sup> assigns greatest weight; nevertheless, *F*<sup>3</sup> cannot be reduced to two variables {*i*3,*i*5} without 23% error, etc.

Scores plot of PCA *F*2–*F*<sup>1</sup> for teas (cf. **Figure 5**) illustrates different behaviours depending on Cu, etc. The four clusters above are clearly distinguished: class 1 with


**Table 2.**

**Figure 3.**

**Figure 4.**

**90**

*Radial tree of teas according to elemental analysis.*

*Tea - Chemistry and Pharmacology*

*Split graph of teas according to elemental analysis.*

*Importance of PCA factors for the elemental analysis of tea infusions.*

**Figure 5.** *PCA scores plot of teas according to elemental analysis.*

three teas (*F*<sup>1</sup> < < *F*<sup>2</sup> ≈ 0, *left*), grouping 2 with three plants (*F*<sup>1</sup> < *F*<sup>2</sup> ≈ 0, *middle*), cluster 3 with three samples (*F*<sup>1</sup> > *F*<sup>2</sup> ≈ 0, *bottom right*) and class 4 with two teas (*F*<sup>1</sup> > > *F*<sup>2</sup> ≈ 0, *top*). The diagram is in qualitative agreement with PCD, binary/radial trees and split graph (**Figures 1**–**4**).

Correlation between Fe and Zn is greater than between Fe and Mn. Correlation between Fe and Na is greater than between K and Na. The dendrogram for six elements of teas (cf. **Figure 7**) separates the same two clusters above in agreement with PCA loadings plot (**Figure 6**). Again, pairs of metals Cu/Zn, Fe/Mn and K/Na split into classes 1 and 2. Copper is closer to Mn than Zn, Fe is closer to Zn than Mn

The radial tree for six elements of teas (cf. **Figure 8**) separates the same two

Split graph for six elements of teas (cf. **Figure 9**) reveals conflicting relationships between classes. It separates both clusters above in agreement with PCA loadings plot and binary/radial trees (**Figures 6**–**8**). Once more, pairs of metals Cu/Zn, Fe/Mn and K/Na split into classes 1 and 2. Copper is closer to Mn than Zn,

A PCA was performed for elements. Factor *F*<sup>1</sup> explains 99.97% variance (0.03% error) and *F*1/2 100% variance (0% error). In PCA *F*2-*F*<sup>1</sup> score plot for metals (cf. **Figure 10**), Cu and Zn as well as Fe and Na collapse. Two clusters are clearly distinguished: class 1 with three elements (*F*<sup>1</sup> > > *F*2, *bottom*) and grouping 2 with three metals (*F*<sup>1</sup> < < *F*2, *top*). The diagram separates both classes above in qualitative agreement with PCA loadings plot, binary/radial trees and split graph (**Figures 6**–**9**).

(**Figures 6** and **7**). One more time, pairs of metals Cu/Zn, Fe/Mn and K/Na split into classes 1 and 2. Copper is closer to Mn than Zn, Fe is closer to Zn than Mn, and

clusters above in agreement with PCA loadings plot and dendrogram

*Elemental Classification of Tea Leaves Infusions: Principal Component, Cluster…*

Fe is closer to Zn than Mn, and Na is closer to Fe than K.

and Na is closer to Fe than K.

*DOI: http://dx.doi.org/10.5772/intechopen.81379*

Na is closer to Fe than K.

**Figure 7.**

**93**

*Dendrogram of elemental analysis for teas.*

From PCA factors loading of teas, *F*2–*F*<sup>1</sup> loadings plot depicts six elements (**Table 1**). Two clusters are clearly distinguished: class 1 with three metals {1,3,4} (*F*<sup>1</sup> < *F*<sup>2</sup> < 0, cf. **Figure 6** *left*) and grouping 2 with three elements {2,5,6} (*F*<sup>1</sup> > > *F*2, *bottom right*). Heavy metals such as Cu and Zn, Fe and Mn and alkalines K and Na split into classes 1 and 2. Copper is closer to Mn than Zn; Fe is closer to Zn than Mn; and Na is closer to Fe than K. In addition, as a complement to scores diagram for loadings, it is confirmed that teas in class 1, located in the left side, present a more pronounced contribution from elements in grouping 1 situated in the same side of **Figure 5**. Metals in cluster 3 in the bottom right show a contribution from contents in class 2 found in the same side of **Figure 5**. The plot agrees qualitatively with PCD, binary/radial trees and split graph (**Figures 1**–**4**).

Instead of 11 teas in the space R<sup>6</sup> of six elements, consider six components in the space R<sup>11</sup> of 11 teas. Matrix **R** upper triangle results

$$R = \begin{pmatrix} 1.000 & -0.462 & 0.393 & 0.835 & -0.419 & 0.321 \\ & 1.000 & -0.134 & -0.677 & 0.324 & 0.496 \\ & & 1.000 & 0.209 & -0.023 & 0.290 \\ & & & 1.000 & -0.318 & -0.016 \\ & & & & 1.000 & 0.197 \\ & & & & & 1.000 \end{pmatrix}$$

High correlations appear between pairs of heavy metals Cu–Mn *R*1,4 = 0.835. Correlation between heavy metals Cu–Zn *R*1,6 = 0.321 is low. Correlation between heavy metals Fe and Mn *R*2,4 = �0.677 and alkalines K and Na *R*3,5 = �0.023 is negative. Correlation between Cu and Mn is greater than between Cu and Zn.

**Figure 6.** *PCA loadings plot of teas according to elemental analysis.*

*Elemental Classification of Tea Leaves Infusions: Principal Component, Cluster… DOI: http://dx.doi.org/10.5772/intechopen.81379*

Correlation between Fe and Zn is greater than between Fe and Mn. Correlation between Fe and Na is greater than between K and Na. The dendrogram for six elements of teas (cf. **Figure 7**) separates the same two clusters above in agreement with PCA loadings plot (**Figure 6**). Again, pairs of metals Cu/Zn, Fe/Mn and K/Na split into classes 1 and 2. Copper is closer to Mn than Zn, Fe is closer to Zn than Mn and Na is closer to Fe than K.

The radial tree for six elements of teas (cf. **Figure 8**) separates the same two clusters above in agreement with PCA loadings plot and dendrogram (**Figures 6** and **7**). One more time, pairs of metals Cu/Zn, Fe/Mn and K/Na split into classes 1 and 2. Copper is closer to Mn than Zn, Fe is closer to Zn than Mn, and Na is closer to Fe than K.

Split graph for six elements of teas (cf. **Figure 9**) reveals conflicting relationships between classes. It separates both clusters above in agreement with PCA loadings plot and binary/radial trees (**Figures 6**–**8**). Once more, pairs of metals Cu/Zn, Fe/Mn and K/Na split into classes 1 and 2. Copper is closer to Mn than Zn, Fe is closer to Zn than Mn, and Na is closer to Fe than K.

A PCA was performed for elements. Factor *F*<sup>1</sup> explains 99.97% variance (0.03% error) and *F*1/2 100% variance (0% error). In PCA *F*2-*F*<sup>1</sup> score plot for metals (cf. **Figure 10**), Cu and Zn as well as Fe and Na collapse. Two clusters are clearly distinguished: class 1 with three elements (*F*<sup>1</sup> > > *F*2, *bottom*) and grouping 2 with three metals (*F*<sup>1</sup> < < *F*2, *top*). The diagram separates both classes above in qualitative agreement with PCA loadings plot, binary/radial trees and split graph (**Figures 6**–**9**).

**Figure 7.** *Dendrogram of elemental analysis for teas.*

three teas (*F*<sup>1</sup> < < *F*<sup>2</sup> ≈ 0, *left*), grouping 2 with three plants (*F*<sup>1</sup> < *F*<sup>2</sup> ≈ 0, *middle*),

From PCA factors loading of teas, *F*2–*F*<sup>1</sup> loadings plot depicts six elements (**Table 1**). Two clusters are clearly distinguished: class 1 with three metals {1,3,4} (*F*<sup>1</sup> < *F*<sup>2</sup> < 0, cf. **Figure 6** *left*) and grouping 2 with three elements {2,5,6} (*F*<sup>1</sup> > > *F*2, *bottom right*). Heavy metals such as Cu and Zn, Fe and Mn and alkalines K and Na split into classes 1 and 2. Copper is closer to Mn than Zn; Fe is closer to Zn than Mn; and Na is closer to Fe than K. In addition, as a complement to scores diagram for loadings, it is confirmed that teas in class 1, located in the left side, present a more pronounced contribution from elements in grouping 1 situated in the same side of **Figure 5**. Metals in cluster 3 in the bottom right show a contribution from contents in class 2 found in the same side of **Figure 5**. The plot agrees qualitatively with PCD,

Instead of 11 teas in the space R<sup>6</sup> of six elements, consider six components in the

1*:*000 �0*:*462 0*:*393 0*:*835 �0*:*419 0*:*321

High correlations appear between pairs of heavy metals Cu–Mn *R*1,4 = 0.835. Correlation between heavy metals Cu–Zn *R*1,6 = 0.321 is low. Correlation between heavy metals Fe and Mn *R*2,4 = �0.677 and alkalines K and Na *R*3,5 = �0.023 is negative. Correlation between Cu and Mn is greater than between Cu and Zn.

1*:*000 �0*:*134 �0*:*677 0*:*324 0*:*496

1*:*000 0*:*209 �0*:*023 0*:*290

1*:*000 �0*:*318 �0*:*016

1*:*000 0*:*197

1*:*000

1

CCCCCCCCA

cluster 3 with three samples (*F*<sup>1</sup> > *F*<sup>2</sup> ≈ 0, *bottom right*) and class 4 with two teas (*F*<sup>1</sup> > > *F*<sup>2</sup> ≈ 0, *top*). The diagram is in qualitative agreement with PCD,

binary/radial trees and split graph (**Figures 1**–**4**).

binary/radial trees and split graph (**Figures 1**–**4**).

space R<sup>11</sup> of 11 teas. Matrix **R** upper triangle results

*R* ¼

**Figure 6.**

**92**

*PCA loadings plot of teas according to elemental analysis.*

0

*Tea - Chemistry and Pharmacology*

BBBBBBBB@

Again, pairs of elements Cu/Zn, Fe/Mn and K/Na split into classes 1 and 2. Copper is closer to Mn than Zn, Fe is closer to Zn than Mn, and Na is closer to Fe than K.

*Elemental Classification of Tea Leaves Infusions: Principal Component, Cluster…*

*DOI: http://dx.doi.org/10.5772/intechopen.81379*

Tea is the second popular beverage. Its chemical components are of interest, especially in relation to health. Flavonoids beneficial effects are vasodilator, antilipemic, antiatherogenic, antithrombotic, anti-inflammatory, apoptotic, antiapoptotic and antioxidant improving health and decreasing CVD. Alkaloid methylxanthines theobromine, theophylline and caff pass via the placental barrier. The GT contains higher amounts of catechins (GTCs) ()-epigallocatechin 3*-O*gallate (EGCg) and ()-epicatechin (EC) than BT. The total contents of Fe, Zn, Cu, Mn, Ni, Na and K in tea leaves and their amounts, available in the corresponding tea infusions, were analysed depending on tea type. Drinking tea impact on the uptake of these metals was examined. The total elemental content of tea leaves differs according to tea type. Tea infusions result a dietary source of essential trace elements, especially Mn, which is the only metal with a dietary amount. The mineral content of the infusions is not related directly to dry tea. The elements in tea leaves were K > Mn > Fe > Na and infusions K > Mn > Na > Fe > Zn > Cu > Ni. Zinc is responsible for enzymatic processes and involved in the working of genetic materials, proteins and immune reactions. It influences maintenance of cell membrane stability and immune system function. It is involved in pathologies (e.g. Alzheimer's disease, epilepsy, ischemia, infantile diarrhoea). Micronutrient Cu is phytotoxic at high concentrations. Its overconsumption is detrimental to health. Change of teas' Cu content was because of different types, grades and producing areas. Its pollution

Maceration of GT causes GTC oxidation producing pigmented theaflavins (TFs) and thearubigins (TRs), both 30% dry weight of BT, which affect tea infusion quality.

originates from rolling machines and fungicides.

**4. Discussion**

**95**

*PCA scores plot of elemental analysis for teas.*

**Figure 10.**

**Figure 8.** *Radial tree of elemental analysis for teas.*

**Figure 9.** *Split graph of elemental analysis for teas.*

*Elemental Classification of Tea Leaves Infusions: Principal Component, Cluster… DOI: http://dx.doi.org/10.5772/intechopen.81379*

**Figure 10.** *PCA scores plot of elemental analysis for teas.*

Again, pairs of elements Cu/Zn, Fe/Mn and K/Na split into classes 1 and 2. Copper is closer to Mn than Zn, Fe is closer to Zn than Mn, and Na is closer to Fe than K.

### **4. Discussion**

**Figure 8.**

**Figure 9.**

**94**

*Split graph of elemental analysis for teas.*

*Radial tree of elemental analysis for teas.*

*Tea - Chemistry and Pharmacology*

Tea is the second popular beverage. Its chemical components are of interest, especially in relation to health. Flavonoids beneficial effects are vasodilator, antilipemic, antiatherogenic, antithrombotic, anti-inflammatory, apoptotic, antiapoptotic and antioxidant improving health and decreasing CVD. Alkaloid methylxanthines theobromine, theophylline and caff pass via the placental barrier. The GT contains higher amounts of catechins (GTCs) ()-epigallocatechin 3*-O*gallate (EGCg) and ()-epicatechin (EC) than BT. The total contents of Fe, Zn, Cu, Mn, Ni, Na and K in tea leaves and their amounts, available in the corresponding tea infusions, were analysed depending on tea type. Drinking tea impact on the uptake of these metals was examined. The total elemental content of tea leaves differs according to tea type. Tea infusions result a dietary source of essential trace elements, especially Mn, which is the only metal with a dietary amount. The mineral content of the infusions is not related directly to dry tea. The elements in tea leaves were K > Mn > Fe > Na and infusions K > Mn > Na > Fe > Zn > Cu > Ni. Zinc is responsible for enzymatic processes and involved in the working of genetic materials, proteins and immune reactions. It influences maintenance of cell membrane stability and immune system function. It is involved in pathologies (e.g. Alzheimer's disease, epilepsy, ischemia, infantile diarrhoea). Micronutrient Cu is phytotoxic at high concentrations. Its overconsumption is detrimental to health. Change of teas' Cu content was because of different types, grades and producing areas. Its pollution originates from rolling machines and fungicides.

Maceration of GT causes GTC oxidation producing pigmented theaflavins (TFs) and thearubigins (TRs), both 30% dry weight of BT, which affect tea infusion quality.

Authors know that no other similar classification studies grouping teas by their metal content, either computational or experimental.

## **5. Conclusion**

From the present results and discussion, the following conclusions can be drawn.


## **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).

**Author details**

Francisco Torrens<sup>1</sup>

**97**

\* 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

*Elemental Classification of Tea Leaves Infusions: Principal Component, Cluster…*

*DOI: http://dx.doi.org/10.5772/intechopen.81379*

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,

## **Conflict of interest**

The authors declare no conflict of interest.

*Elemental Classification of Tea Leaves Infusions: Principal Component, Cluster… DOI: http://dx.doi.org/10.5772/intechopen.81379*

## **Author details**

Authors know that no other similar classification studies grouping teas by their

From the present results and discussion, the following conclusions can be drawn.

1. Criteria reduced analysis to a manageable quantity from enormous set of tea metals: they refer to the elemental analysis of tea leaf infusions. Meta-analysis was useful to rise numbers of samples and variety of analysed data. Different behaviours of teas depend on Cu, Fe, K, Mn, Na and Zn. They are considered as a good source of Mn, etc. Two elemental classes are clearly distinguished: Cu-K-Mn and Fe-Na-Zn. With regard to components, heavy metals such as Cu and Zn as well as Fe and Mn and alkalines such as K and Na classed separately. Copper is closer to Mn than Zn, Fe is closer to Zn than Mn, and Na is closer to Fe than K. Heavy metals Fe and Mn as well as K and Na correlate negatively. Teas present adequate elemental contents, good antioxidant capacity and may be used as a functional beverage. They represent plants useful as a natural

2. Principal components analyses of elements and teas cluster analyses allowed

understanding of computational methods are essential for tackling associated

3.More studies are needed contributing more scientific evidence on the benefits

classifying them and agreed. Phytochemistry, cytochemistry and

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

metal content, either computational or experimental.

source for nutraceutical formulations.

data mining tasks.

above.

**Acknowledgements**

**Conflict of interest**

**96**

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

The authors declare no conflict of interest.

**5. Conclusion**

*Tea - Chemistry and Pharmacology*

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.

## **References**

[1] Harbowy ME, Balentine DA. Tea chemistry. Critical Reviews in Plant Sciences. 1997;**16**:415-480

[2] Ferrara L, Montesano D, Senatore A. The distribution of minerals and flavonoids in tea plant. Farmaco. 2001; **56**:397-401

[3] MacKay DL, Blumberg JB. The role of tea in human health: An update. Journal of the American College of Nutrition. 2002;**21**:1-13

[4] Marcos A, Fisher A, Ree J, Hill SJ. Preliminary study using trace element concentrations and a chemometrics approach to determine the geological origin of tea. Journal of Analytical Atomic Spectrometry. 1998;**113**:521-525

[5] Powell JJ, Burden TJ, Thompson RPH. *In vitr*o mineral availability from digested tea: A rich dietary source of manganese. The Analyst. 1998;**123**: 1721-1724

[6] Herrador MA, Gonzalez AG. Pattern recognition procedures for differentiation of green, black and oolong teas according to their metal content from inductively coupled plasma atomic emission spectrometry. Talanta. 2001;**53**:1249-1257

[7] Feng H, Wang T, Li SFY. Sensitive determination of trace-metal elements in tea with capillary electrophoresis by using chelating agent 4-(2-pyridylazo) resorcinol (PAR). Food Chemistry. 2003;**81**:607-611

[8] Malik J, Szakova J, Dabrek O, Balik J, Kokoska L. Determination of certain micro and macroelements in plant stimulants and their infusions. Food Chemistry. 2008;**111**:520-525

[9] Pohl P, Prusisz B. Fractionation and analysis of manganese and zinc in tea

infusions by two-column solid phase extraction and flame atomic absorption spectrometry. Food Chemistry. 2007; **102**:1415-1424

Iranian consumed tea. Environmental Monitoring and Assessment. 2008;**144**:

*DOI: http://dx.doi.org/10.5772/intechopen.81379*

[25] Chen XM, Ma Z, Kitts DD.

[26] Mumu SK, Hossain MM. Antimicrobial activity of tea tree oil against pathogenic bacteria and comparison of its effectiveness with eucalyptus oil, lemongrass oil and conventional antibiotics. American Journal of Microbiological Research.

[27] Torrens-Zaragozá F. Molecular categorization of yams by principal component and cluster analyses. Nereis.

[28] Torrens-Zaragozá F. Classification of lactic acid bacteria against cytokine immune modulation. Nereis. 2014;

[29] Torrens-Zaragozá F. Classification of fruits proximate and mineral content: Principal component, cluster, metaanalyses. Nereis. 2015;**2015**(7):39-50

[30] Torrens-Zaragozá F. Classification of food spices by proximate content: Principal component, cluster, metaanalyses. Nereis. 2016;**2016**(8):23-33

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

[32] 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

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

375-395

*Elemental Classification of Tea Leaves Infusions: Principal Component, Cluster…*

2018;**6**:73-78

2013;**2013**(5):41-51

**2014**(6):27-37

2017. pp. 45-61

Demonstrating the relationship between the phytochemical profile of different teas with relative antioxidant and antiinflammatory capacities. The Functional Foods in Health and Disease. 2017;**7**:

[18] Seenivasan S, Manikandan N, Muraleedharan NN, Selvasundaram R. Heavy metal content of black teas from south India. Food Control. 2008;**19**:

[19] Yemane M, Chandravanshi BS, Wondimu T. Levels of essential and non-essential metals in leaves of the tea plant (*Camellia sinensis* L.) and soil of Wushwush farms, Ethiopia. Food Chemistry. 2008;**107**:1236-1243

[20] Aksuner N, Henden E, Aker Z, Engin E, Satik S. Determination of essential and non-essential elements in various tea leaves and tea infusions consumed in Turkey. Food Additives & Contaminants: Part B. 2012;**5**:126-132

[21] Abe K, Okada N, Tanabe H, Fukutomi R, Yasui K, Isemura M, et al. Effects of chronic ingestion of catechin-

rich green tea on hepatic gene

[22] Yasui K, Paeng N, Miyoshi N, Suzuki T, Taguchi K, Ishigami Y, et al. Effects of a catechin-free fraction derived from green tea on gene expression of enzymes related to lipid metabolism in the mouse liver. Biomedical Research. 2012;**33**:9-13

[23] Suzuki T, Pervin M, Goto S, Isemura M, Nakamura Y. Beneficial effects of tea and the green tea catechin epigallocatechin-3-gallate on obesity. Molecules. 2016;**21**:1305-1-

[24] 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**:325-1-325-9

expression of gluconeogenic enzymes in rats. Biomedical Research. 2009;**30**:

13-30

746-749

25-29

1305-130513

**99**

[10] Fernandez PL, Pablos F, Martin MJ, Gonzalez AG. Multi-element analysis of tea beverages by inductively coupled plasma atomic emission spectrometry. Food Chemistry. 2002;**76**:483-489

[11] Cao X, Zhao G, Yin M, Li J. Determination of ultratrace rare earth elements by inductively coupled plasma mass spectrometry with microwave digestion and AG50W-x8 cation exchange chromatography. The Analyst. 1998;**123**:1115-1119

[12] Kumar A, Nair AGC, Reddy AVR, Garg AN. Availability of essential elements in Indian and US tea brands. Food Chemistry. 2005;**89**:441-448

[13] Lu HT, Mou SF. Simultaneous determination of copper, nickel, zinc, cadmium, cobalt, manganese and lead in tea by ion chromatography. Lihua Jianyan. Huaxue Fence. 2000;**36**:51-54

[14] Smith FE, Arsenault EA. Microwave-assisted sample preparation in analytical chemistry. Talanta. 1996; **43**:1207-1268

[15] McGrath D. Use of microwave digestion for estimation of heavy metal content of soils in a geochemical survey. Talanta. 1998;**46**:439-448

[16] Demirel S, Tuzen M, Saracoglu S, Soylak M. Evaluation of various digestion procedures for trace element contents of some food materials. Journal of Hazardous Materials. 2008;**152**: 1020-1026

[17] Moghaddam MA, Mahvi AH, Asgari AR, Yonesian M, Jahed G, Nazmara S. Determination of aluminum and zinc in

*Elemental Classification of Tea Leaves Infusions: Principal Component, Cluster… DOI: http://dx.doi.org/10.5772/intechopen.81379*

Iranian consumed tea. Environmental Monitoring and Assessment. 2008;**144**: 13-30

**References**

**56**:397-401

2002;**21**:1-13

1721-1724

[1] Harbowy ME, Balentine DA. Tea chemistry. Critical Reviews in Plant

*Tea - Chemistry and Pharmacology*

infusions by two-column solid phase extraction and flame atomic absorption spectrometry. Food Chemistry. 2007;

[10] Fernandez PL, Pablos F, Martin MJ, Gonzalez AG. Multi-element analysis of tea beverages by inductively coupled plasma atomic emission spectrometry. Food Chemistry. 2002;**76**:483-489

exchange chromatography. The Analyst.

[12] Kumar A, Nair AGC, Reddy AVR, Garg AN. Availability of essential elements in Indian and US tea brands. Food Chemistry. 2005;**89**:441-448

[13] Lu HT, Mou SF. Simultaneous determination of copper, nickel, zinc, cadmium, cobalt, manganese and lead in tea by ion chromatography. Lihua Jianyan. Huaxue Fence. 2000;**36**:51-54

[14] Smith FE, Arsenault EA.

Talanta. 1998;**46**:439-448

**43**:1207-1268

1020-1026

Microwave-assisted sample preparation in analytical chemistry. Talanta. 1996;

[15] McGrath D. Use of microwave digestion for estimation of heavy metal content of soils in a geochemical survey.

[16] Demirel S, Tuzen M, Saracoglu S, Soylak M. Evaluation of various digestion procedures for trace element contents of some food materials. Journal of Hazardous Materials. 2008;**152**:

[17] Moghaddam MA, Mahvi AH, Asgari AR, Yonesian M, Jahed G, Nazmara S. Determination of aluminum and zinc in

[11] Cao X, Zhao G, Yin M, Li J. Determination of ultratrace rare earth elements by inductively coupled plasma mass spectrometry with microwave digestion and AG50W-x8 cation

1998;**123**:1115-1119

**102**:1415-1424

[2] Ferrara L, Montesano D, Senatore A. The distribution of minerals and flavonoids in tea plant. Farmaco. 2001;

[3] MacKay DL, Blumberg JB. The role of tea in human health: An update. Journal of the American College of Nutrition.

[4] Marcos A, Fisher A, Ree J, Hill SJ. Preliminary study using trace element concentrations and a chemometrics approach to determine the geological origin of tea. Journal of Analytical Atomic Spectrometry. 1998;**113**:521-525

[5] Powell JJ, Burden TJ, Thompson RPH. *In vitr*o mineral availability from digested tea: A rich dietary source of manganese. The Analyst. 1998;**123**:

[6] Herrador MA, Gonzalez AG. Pattern

differentiation of green, black and oolong teas according to their metal content from inductively coupled plasma atomic emission spectrometry.

[7] Feng H, Wang T, Li SFY. Sensitive determination of trace-metal elements in tea with capillary electrophoresis by using chelating agent 4-(2-pyridylazo) resorcinol (PAR). Food Chemistry.

[8] Malik J, Szakova J, Dabrek O, Balik J, Kokoska L. Determination of certain micro and macroelements in plant stimulants and their infusions. Food Chemistry. 2008;**111**:520-525

[9] Pohl P, Prusisz B. Fractionation and analysis of manganese and zinc in tea

recognition procedures for

Talanta. 2001;**53**:1249-1257

2003;**81**:607-611

**98**

Sciences. 1997;**16**:415-480

[18] Seenivasan S, Manikandan N, Muraleedharan NN, Selvasundaram R. Heavy metal content of black teas from south India. Food Control. 2008;**19**: 746-749

[19] Yemane M, Chandravanshi BS, Wondimu T. Levels of essential and non-essential metals in leaves of the tea plant (*Camellia sinensis* L.) and soil of Wushwush farms, Ethiopia. Food Chemistry. 2008;**107**:1236-1243

[20] Aksuner N, Henden E, Aker Z, Engin E, Satik S. Determination of essential and non-essential elements in various tea leaves and tea infusions consumed in Turkey. Food Additives & Contaminants: Part B. 2012;**5**:126-132

[21] Abe K, Okada N, Tanabe H, Fukutomi R, Yasui K, Isemura M, et al. Effects of chronic ingestion of catechinrich green tea on hepatic gene expression of gluconeogenic enzymes in rats. Biomedical Research. 2009;**30**: 25-29

[22] Yasui K, Paeng N, Miyoshi N, Suzuki T, Taguchi K, Ishigami Y, et al. Effects of a catechin-free fraction derived from green tea on gene expression of enzymes related to lipid metabolism in the mouse liver. Biomedical Research. 2012;**33**:9-13

[23] Suzuki T, Pervin M, Goto S, Isemura M, Nakamura Y. Beneficial effects of tea and the green tea catechin epigallocatechin-3-gallate on obesity. Molecules. 2016;**21**:1305-1- 1305-130513

[24] 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**:325-1-325-9

[25] Chen XM, Ma Z, Kitts DD. Demonstrating the relationship between the phytochemical profile of different teas with relative antioxidant and antiinflammatory capacities. The Functional Foods in Health and Disease. 2017;**7**: 375-395

[26] Mumu SK, Hossain MM. Antimicrobial activity of tea tree oil against pathogenic bacteria and comparison of its effectiveness with eucalyptus oil, lemongrass oil and conventional antibiotics. American Journal of Microbiological Research. 2018;**6**:73-78

[27] Torrens-Zaragozá F. Molecular categorization of yams by principal component and cluster analyses. Nereis. 2013;**2013**(5):41-51

[28] Torrens-Zaragozá F. Classification of lactic acid bacteria against cytokine immune modulation. Nereis. 2014; **2014**(6):27-37

[29] Torrens-Zaragozá F. Classification of fruits proximate and mineral content: Principal component, cluster, metaanalyses. Nereis. 2015;**2015**(7):39-50

[30] Torrens-Zaragozá F. Classification of food spices by proximate content: Principal component, cluster, metaanalyses. Nereis. 2016;**2016**(8):23-33

[31] 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: InTechOpen; 2017. pp. 45-61

[32] 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 [33] Torrens F, Castellano G. Molecular classification of caffeine, its metabolites and nicotine metabolite. In: Ul-Haq Z, Madura JD, editors. Frontiers in Computational Chemistry. Hilversum: Bentham; 2018. Vol. 4, p. 3–51.

[34] Torrens F, Castellano G. QSPR prediction of chromatographic retention times of tea compounds by bioplastic evolution. In: Latosinska JN, editor. The Dual Nature of Caffeine. Vienna: InTechOpen. submitted for publication

[35] Hotelling H. Analysis of a complex of statistical variables into principal components. Journal of Education & Psychology. 1933;**24**:417-441

[36] Kramer R. Chemometric Techniques for Quantitative Analysis. New York: Marcel Dekker; 1998

[37] Patra SK, Mandal AK, Pal MK. State of aggregation of bilirubin in aqueous solution: Principal component analysis approach. Photochemical & Photobiological Sciences, Section A. 1999;**122**:23-31

[38] Jolliffe IT. Principal Component Analysis. New York: Springer; 2002

[39] Xu J, Hagler A. Chemoinformatics and drug discovery. Molecules. 2002;**7**: 566-600

[40] Shaw PJA. Multivariate Statistics for the Environmental Sciences. New York: Hodder-Arnold; 2003

[41] IMSL. Integrated Mathematical Statistical Library (IMSL). Houston: IMSL; 1989

[42] Tryon RCJ. A multivariate analysis of the risk of coronary heart disease in Framingham. Journal of Chronic Diseases. 1939;**20**:511-524

[43] Priness I, Maimon O, Ben-Gal I. Evaluation of gene-expression clustering via mutual information

distance measure. BMC Bioinformatics. 2007;**8**:111-1-111-12

**Chapter 8**

**Abstract**

green tea, black tea

**1. Introduction**

**101**

Evolution

QSPR Prediction of

*Francisco Torrens and Gloria Castellano*

Chromatographic Retention Times

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,

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

of Tea Compounds by Bioplastic

[44] Steuer R, Kurths J, Daub CO, Weise J, Selbig J. The mutual information: Detecting and evaluating dependencies between variables. Bioinformatics. 2002;**18**(Suppl. 2):S231-S240

[45] D'Haeseleer P, Liang S, Somogyi R. Genetic network inference: From coexpression clustering to reverse engineering. Bioinformatics. 2000;**16**: 707-726

[46] Perou CM, Sørlie T, van Eisen MB, de Rijn M, Jeffrey SS, Rees CA, et al. Molecular portraits of human breast tumours. Nature (London). 2000;**406**: 747-752

[47] Jarvis RA, Patrick EA. Clustering using a similarity measure based on shared nearest neighbors. IEEE Transactions on Computers. 1973;**C22**: 1025-1034

[48] Page RDM. Program TreeView. UK: Universiy of Glasgow; 2000

[49] Eisen MB, Spellman PT, Brown PO, Botstein D. Cluster analysis and display of genome-wide expression patterns. Proceedings of the National Academy of Sciences of the United States of America. 1998;**95**:14863-14868

[50] Huson DH. SplitsTree: Analyzing and visualizing evolutionary data. Bioinformatics. 1998;**14**:68-73

## **Chapter 8**

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

*Tea - Chemistry and Pharmacology*

distance measure. BMC Bioinformatics.

[44] Steuer R, Kurths J, Daub CO, Weise J, Selbig J. The mutual information: Detecting and evaluating dependencies between variables. Bioinformatics. 2002;**18**(Suppl. 2):S231-S240

[45] D'Haeseleer P, Liang S, Somogyi R. Genetic network inference: From coexpression clustering to reverse engineering. Bioinformatics. 2000;**16**:

[46] Perou CM, Sørlie T, van Eisen MB, de Rijn M, Jeffrey SS, Rees CA, et al. Molecular portraits of human breast tumours. Nature (London). 2000;**406**:

[47] Jarvis RA, Patrick EA. Clustering using a similarity measure based on shared nearest neighbors. IEEE Transactions on Computers. 1973;**C22**:

[48] Page RDM. Program TreeView. UK:

[49] Eisen MB, Spellman PT, Brown PO, Botstein D. Cluster analysis and display of genome-wide expression patterns. Proceedings of the National Academy of

Universiy of Glasgow; 2000

Sciences of the United States of America. 1998;**95**:14863-14868

[50] Huson DH. SplitsTree: Analyzing and visualizing evolutionary data. Bioinformatics. 1998;**14**:68-73

2007;**8**:111-1-111-12

707-726

747-752

1025-1034

[34] Torrens F, Castellano G. QSPR prediction of chromatographic retention times of tea compounds by bioplastic evolution. In: Latosinska JN, editor. The Dual Nature of Caffeine. Vienna: InTechOpen. submitted for publication

[35] Hotelling H. Analysis of a complex of statistical variables into principal components. Journal of Education &

Techniques for Quantitative Analysis. New York: Marcel Dekker; 1998

[37] Patra SK, Mandal AK, Pal MK. State of aggregation of bilirubin in aqueous solution: Principal component analysis

Photobiological Sciences, Section A.

[38] Jolliffe IT. Principal Component Analysis. New York: Springer; 2002

[39] Xu J, Hagler A. Chemoinformatics and drug discovery. Molecules. 2002;**7**:

[40] Shaw PJA. Multivariate Statistics for the Environmental Sciences. New York:

[41] IMSL. Integrated Mathematical Statistical Library (IMSL). Houston:

[42] Tryon RCJ. A multivariate analysis of the risk of coronary heart disease in Framingham. Journal of Chronic Diseases. 1939;**20**:511-524

[43] Priness I, Maimon O, Ben-Gal I. Evaluation of gene-expression clustering via mutual information

Psychology. 1933;**24**:417-441

[36] Kramer R. Chemometric

approach. Photochemical &

1999;**122**:23-31

566-600

IMSL; 1989

**100**

Hodder-Arnold; 2003
