**4. Statistical analysis of data**

Statistics have, through the descriptive methods of data analysis, powerful multidimensional analysis tools that can be used to design important information for fundamental research, applied research, market research, economic analysis, etc. Information can be hierarchized in terms of intensity of influence and can be analyzed as a whole and not independently [78].

One-way Analysis of Variance (ANOVA) was applied to the data set related to the ten mushrooms species, in order to observe whether there are any significant differences (Sig. < 0.05) between the means of the independent groups of variables. The produced F-statistic was higher for phenolics, flavonoids respectively antioxidant activity determined in aqueous extracts. Contrary, for the same parameters whose values were associated to hydroalcoholic extracts, the Sig. value higher than 0.05 indicated that there are no differences between groups in function of mushroom species.

In order to test the hypotheses of association between enzymatic and nonenzymatic antioxidants, the Bivariate (Pearson) Correlation was applied. From the large amount of information, the data shown in **Table 3** pointed out only the significant correlations, both for mushrooms' cap and stipe.

A strong positive relationship was observed between phenolics and flavonoids determined in aqueous extracts, regardless of the anatomic part of the mushrooms species. The strength of association was large but downhill between antioxidant activity determined in aqueous extracts and mushrooms species, if the last variable was defined in the next order: *Agaricus bisporus white*, *Agaricus bisporus brown*, *Pleurotus ostreatus* cultivated*, Russula alutacea*, *Chantarellus cibarius*, *Russula vesca*, *Boletus edulis*, *Agaricus campestris*, *Macrolepiota procera* and *Pleurotus ostreatus* wild respectively. Only for the stipe of the analyzed species, a strong relationship between the content of phenolics (in hydroalcoholic extracts) and the catalasic activity was determined. The relationship between catalase at least and mushroom species should be deeply analyzed, taking into account as much as possible types of potential linked variables, because in the last years the role of catalase (CAT), together with those of superoxide dismutase (SOD), was largely discussed in the context of the bioremediation biotechnologies.

No correlation could be observed for peroxidase, phenolics and antioxidant activity, irrespective of the extractant used and the anatomic part of the mushrooms. The Boxplot method was applied for graphically depicting groups of data related to phenolics, flavonoids and antioxidant activity, both in aqueous and hydroalcoholic extracts of mushrooms species. The quartiles of data represented in relationship


**31**

**Figure 10.**

*(b) hydroalcoholic extracts.*

*Correlation between Enzymatic and Non-Enzymatic Antioxidants in Several Edible Mushrooms…*

with the anatomic part of the 10 species of mushrooms highlighted those species located far from the group in terms of their content in antioxidant compounds, respectively antioxidant capacity. The degree of dispersion and skewness in each category of data is indicated by the spaces between the different parts of the boxes. Variability outside the upper and lower quartiles of different variables was indicated

by lines extending vertically from the boxes of the box plots (**Figures 10**–**12**). Thus, both for cap and stipe, *Boletus edulis* remarked through a higher content of phenolic compounds in aqueous extract (**Figure 10a**). The median values for this parameter are close, independent of the anatomic part, a relative higher degree of dispersion being observed in the case of the mushrooms' cap than in the stipe. The variability of the data outside the upper quartile is however obvious for stipe. Also for the mushrooms' stipe, it cannot be about the scattering of the data if the phenolics in hydroalcoholic extracts are taken into account (**Figure 10b**). Based on the comparative analysis of the graphical representations (**Figure 10a** and **b**) it can be observed that the median value is superior for the group consisting of the caps of mushrooms in terms of phenolics in the case of the hydroalcoholic extracts than in the aqueous ones. If the descriptive statistics was applied to content in flavonoids of the aqueous extracts of the mushrooms species—cap and stipe (**Figure 11a** and **b**), the second quartile for cap was close by the corresponding value for stipe, as it was observed for phenolics determined from aqueous extracts. The data sets show different trends for the other descriptive indicators. *Boletus edulis* (cap) and *Chantarellus cibarius* (stipe) are the mushroom species which detached from groups in terms of their

content in flavonoids (in aqueous, respectively hydroalcoholic extracts).

are not associated to a certain mushroom species.

from the combination of the initial variables (**Figure 13**).

Boxes and whisker plots quartiles cannot be overlapped if the antioxidant activity (both in aqueous and hydroalcoholic extracts) is analyzed **Figure 12**(**a** and **b**). The values determined for cap and stipe are higher in the case of the aqueous extracts of the mushrooms species, regardless of the quartiles displayed. The plotted outliers

Factorial analysis was applied to the enzymatic and non-enzymatic antioxidants of the mushroom species data sets. First of all, the matrix of the data correlation was developed and analyzed. The second phase of the study was based on the high values observed between some analyzed variables. Values of the total explained variance and Eigen values of the correlations matrix were generated. Two components were retained, the variables being represented on two factorial axis resulted

After factors rotation (in order to obtain a better "angle" of view), PC1 explained 46.72% from the total variance, while PC2 explained 84.41%. It is thus possible to represent in the main plan the cloud of points. Two principal components were

*Box plots of phenolics in relationship with anatomic part of mushroom species: (a) aqueous extracts and* 

*DOI: http://dx.doi.org/10.5772/intechopen.82578*

#### **Table 3.**

*Pearson correlation coefficients for the analyzed variables.*

#### *Correlation between Enzymatic and Non-Enzymatic Antioxidants in Several Edible Mushrooms… DOI: http://dx.doi.org/10.5772/intechopen.82578*

with the anatomic part of the 10 species of mushrooms highlighted those species located far from the group in terms of their content in antioxidant compounds, respectively antioxidant capacity. The degree of dispersion and skewness in each category of data is indicated by the spaces between the different parts of the boxes. Variability outside the upper and lower quartiles of different variables was indicated by lines extending vertically from the boxes of the box plots (**Figures 10**–**12**).

Thus, both for cap and stipe, *Boletus edulis* remarked through a higher content of phenolic compounds in aqueous extract (**Figure 10a**). The median values for this parameter are close, independent of the anatomic part, a relative higher degree of dispersion being observed in the case of the mushrooms' cap than in the stipe. The variability of the data outside the upper quartile is however obvious for stipe. Also for the mushrooms' stipe, it cannot be about the scattering of the data if the phenolics in hydroalcoholic extracts are taken into account (**Figure 10b**). Based on the comparative analysis of the graphical representations (**Figure 10a** and **b**) it can be observed that the median value is superior for the group consisting of the caps of mushrooms in terms of phenolics in the case of the hydroalcoholic extracts than in the aqueous ones.

If the descriptive statistics was applied to content in flavonoids of the aqueous extracts of the mushrooms species—cap and stipe (**Figure 11a** and **b**), the second quartile for cap was close by the corresponding value for stipe, as it was observed for phenolics determined from aqueous extracts. The data sets show different trends for the other descriptive indicators. *Boletus edulis* (cap) and *Chantarellus cibarius* (stipe) are the mushroom species which detached from groups in terms of their content in flavonoids (in aqueous, respectively hydroalcoholic extracts).

Boxes and whisker plots quartiles cannot be overlapped if the antioxidant activity (both in aqueous and hydroalcoholic extracts) is analyzed **Figure 12**(**a** and **b**). The values determined for cap and stipe are higher in the case of the aqueous extracts of the mushrooms species, regardless of the quartiles displayed. The plotted outliers are not associated to a certain mushroom species.

Factorial analysis was applied to the enzymatic and non-enzymatic antioxidants of the mushroom species data sets. First of all, the matrix of the data correlation was developed and analyzed. The second phase of the study was based on the high values observed between some analyzed variables. Values of the total explained variance and Eigen values of the correlations matrix were generated. Two components were retained, the variables being represented on two factorial axis resulted from the combination of the initial variables (**Figure 13**).

After factors rotation (in order to obtain a better "angle" of view), PC1 explained 46.72% from the total variance, while PC2 explained 84.41%. It is thus possible to represent in the main plan the cloud of points. Two principal components were

#### **Figure 10.**

*Box plots of phenolics in relationship with anatomic part of mushroom species: (a) aqueous extracts and (b) hydroalcoholic extracts.*

*Food Engineering*

**4. Statistical analysis of data**

analyzed as a whole and not independently [78].

significant correlations, both for mushrooms' cap and stipe.

largely discussed in the context of the bioremediation biotechnologies.

No correlation could be observed for peroxidase, phenolics and antioxidant activity, irrespective of the extractant used and the anatomic part of the mushrooms. The Boxplot method was applied for graphically depicting groups of data related to phenolics, flavonoids and antioxidant activity, both in aqueous and hydroalcoholic extracts of mushrooms species. The quartiles of data represented in relationship

Cap Phenolics—flavonoids (both in aqueous extracts) 0.857\*\*

Stipe Catalase—phenolics in hydroalcoholic extracts 0.700\*

Antioxidant activity in aqueous extracts—flavonoids in hydroalcoholic extracts

Antioxidant activity in hydroalcoholic extracts—flavonoids in aqueous extracts

Phenolics—antioxidant activity (both in hydroalcoholic extracts) 0.725\* Antioxidant activity in aqueous extracts—mushrooms species −0.643\*

Phenolics—flavonoids (both in aqueous extracts) 0.934\*\*

**Variables Pearson's correlation** 

**coefficient**

−0.658\*

0.679\*

Statistics have, through the descriptive methods of data analysis, powerful multidimensional analysis tools that can be used to design important information for fundamental research, applied research, market research, economic analysis, etc. Information can be hierarchized in terms of intensity of influence and can be

One-way Analysis of Variance (ANOVA) was applied to the data set related to the ten mushrooms species, in order to observe whether there are any significant differences (Sig. < 0.05) between the means of the independent groups of variables. The produced F-statistic was higher for phenolics, flavonoids respectively antioxidant activity determined in aqueous extracts. Contrary, for the same parameters whose values were associated to hydroalcoholic extracts, the Sig. value higher than 0.05 indicated that there are no differences between groups in function of mushroom species. In order to test the hypotheses of association between enzymatic and nonenzymatic antioxidants, the Bivariate (Pearson) Correlation was applied. From the large amount of information, the data shown in **Table 3** pointed out only the

A strong positive relationship was observed between phenolics and flavonoids determined in aqueous extracts, regardless of the anatomic part of the mushrooms species. The strength of association was large but downhill between antioxidant activity determined in aqueous extracts and mushrooms species, if the last variable was defined in the next order: *Agaricus bisporus white*, *Agaricus bisporus brown*, *Pleurotus ostreatus* cultivated*, Russula alutacea*, *Chantarellus cibarius*, *Russula vesca*, *Boletus edulis*, *Agaricus campestris*, *Macrolepiota procera* and *Pleurotus ostreatus* wild respectively. Only for the stipe of the analyzed species, a strong relationship between the content of phenolics (in hydroalcoholic extracts) and the catalasic activity was determined. The relationship between catalase at least and mushroom species should be deeply analyzed, taking into account as much as possible types of potential linked variables, because in the last years the role of catalase (CAT), together with those of superoxide dismutase (SOD), was

**30**

**Table 3.**

*\**

*Correlation is significant at the 0.05 level. \*\*Correlation is significant at the 0.01 level.*

*Pearson correlation coefficients for the analyzed variables.*

**Anatomic part**

#### **Figure 11.**

*Box plots of flavonoids in relationship with anatomic part of mushroom species: (a) aqueous extracts and (b) hydroalcoholic extracts.*

#### **Figure 12.**

*Box plots of antioxidant activity in relationship with anatomic part of mushroom species: (a) aqueous extracts and (b) hydroalcoholic extracts.*

**33**

**Figure 14.**

*Correlation between Enzymatic and Non-Enzymatic Antioxidants in Several Edible Mushrooms…*

confirmed through PCA typical graphic representation, respectively the Screeplot (the Graphic of the eigenvalues). These two components (PC1 and PC2) obtained through axis rotation by Varimax method is represented in **Figure 13**. The values of the correlation coefficients (from matrix generated in the first step) are coordinates

*Hierarchical cluster of the extracts: 1—A. bisporus white; 2—A. bisporus brown; 3—P. ostreatus cultivated; 4—R. alutacea; 5—C. cibarius; 6—R. vesca; 7—B. edulis; 8—A. campestris; 9—M. procera; 10—P. ostreatus wild (cap); 11—A. bisporus white; 12—A. bisporus brown; 13—P. ostreatus cultivated; 14—R. alutacea; 15—C. cibarius; 16—R. vesca; 17—B. edulis; 18—A. campestris; 19—M. procera; 20—P. ostreatus wild (stipe).*

of the initial variables in the vectorial plan of the two principal components. Concentration in phenolics and antioxidant activity (both in hydroalcoholic extract) were the major contributors to PC1, while the antioxidant activity of the mushroom species, determined in aqueous extracts, was the major contributor to PC2. The two factors can separate the area of antioxidants correlated with the anatomic part of the mushrooms by this one dominated by the same variables species dependent. The Screeplot and PC loadings suggests that the mushroom species affect mainly the antioxidant activity, determined in aqueous extract, while according to the contributors to PC1 is obvious that the anatomic part of the mushrooms influences the non-enzymatic antioxidants (phenolics, flavonoids in aqueous extracts) and antioxidant activity determined in hydroalcoholic extracts too. A linkage between the enzymatic antioxidants (catalase, peroxidase) and variables such as mushroom species and anatomic part was not observed by applying factorial analysis. In order to group the datasets into similar data groups (classes, clusters), Hierarchical Cluster Analysis, who applies to small sets of data, was taken into account. The question arises as to whether in the set of variables there are identifiable groups, with similar characteristics, that characterize mushrooms' species (content of enzymatic and nonenzymatic antioxidant compounds). The square of the Euclidean distance was used to construct the matrix of similarities, while as method of aggregation—the Ward method. The clusters were formed considering the analyzed cases. All fungal species with similar characteristics (in terms of variables of interest) formed together clusters (**Figure 14**). According to the antioxidants' concentration, in the initial stage of agglomeration different species of mushrooms and their anatomical parts form together three

*DOI: http://dx.doi.org/10.5772/intechopen.82578*

**Figure 13.** *Component plot in rotated space.*

#### *Correlation between Enzymatic and Non-Enzymatic Antioxidants in Several Edible Mushrooms… DOI: http://dx.doi.org/10.5772/intechopen.82578*

confirmed through PCA typical graphic representation, respectively the Screeplot (the Graphic of the eigenvalues). These two components (PC1 and PC2) obtained through axis rotation by Varimax method is represented in **Figure 13**. The values of the correlation coefficients (from matrix generated in the first step) are coordinates of the initial variables in the vectorial plan of the two principal components.

Concentration in phenolics and antioxidant activity (both in hydroalcoholic extract) were the major contributors to PC1, while the antioxidant activity of the mushroom species, determined in aqueous extracts, was the major contributor to PC2. The two factors can separate the area of antioxidants correlated with the anatomic part of the mushrooms by this one dominated by the same variables species dependent. The Screeplot and PC loadings suggests that the mushroom species affect mainly the antioxidant activity, determined in aqueous extract, while according to the contributors to PC1 is obvious that the anatomic part of the mushrooms influences the non-enzymatic antioxidants (phenolics, flavonoids in aqueous extracts) and antioxidant activity determined in hydroalcoholic extracts too. A linkage between the enzymatic antioxidants (catalase, peroxidase) and variables such as mushroom species and anatomic part was not observed by applying factorial analysis.

In order to group the datasets into similar data groups (classes, clusters), Hierarchical Cluster Analysis, who applies to small sets of data, was taken into account. The question arises as to whether in the set of variables there are identifiable groups, with similar characteristics, that characterize mushrooms' species (content of enzymatic and nonenzymatic antioxidant compounds). The square of the Euclidean distance was used to construct the matrix of similarities, while as method of aggregation—the Ward method. The clusters were formed considering the analyzed cases. All fungal species with similar characteristics (in terms of variables of interest) formed together clusters (**Figure 14**).

According to the antioxidants' concentration, in the initial stage of agglomeration different species of mushrooms and their anatomical parts form together three

#### **Figure 14.**

*Hierarchical cluster of the extracts: 1—A. bisporus white; 2—A. bisporus brown; 3—P. ostreatus cultivated; 4—R. alutacea; 5—C. cibarius; 6—R. vesca; 7—B. edulis; 8—A. campestris; 9—M. procera; 10—P. ostreatus wild (cap); 11—A. bisporus white; 12—A. bisporus brown; 13—P. ostreatus cultivated; 14—R. alutacea; 15—C. cibarius; 16—R. vesca; 17—B. edulis; 18—A. campestris; 19—M. procera; 20—P. ostreatus wild (stipe).*

*Food Engineering*

**Figure 11.**

**Figure 12.**

*and (b) hydroalcoholic extracts.*

*(b) hydroalcoholic extracts.*

*Box plots of flavonoids in relationship with anatomic part of mushroom species: (a) aqueous extracts and* 

*Box plots of antioxidant activity in relationship with anatomic part of mushroom species: (a) aqueous extracts* 

**32**

**Figure 13.**

*Component plot in rotated space.*

clusters. *B. edulis* (cap) and *A. campestris* (cap) remained isolated till the end stage of clusterization, being only ones clearly defined depending on the enzymatic and non-enzymatic antioxidants content. *A. bisporus brown* is the only species who aggregated in the initial stage as cap and stipe too. Excepting it, in the intermediate stages of the process the stipe of different mushroom species formed the first clusters, after that a mushroom cap joining to the structure already built. Finally, the clustering method leads to the formation of two clusters.

Chemometrics was applied in order to evaluate the traceability of Boletaceae mushrooms samples in combination with UV-visible and Fourier transform infrared (FTIR) spectroscopy [79], respectively in combination with inductively coupled plasma atomic emission spectrophotometer (ICP-AES), ultraviolet-visible (UV-Vis) and Fourier transform mid-infrared spectroscopy (FT-MIR) [80]. Through a chemometric approach were investigated the isotopic markers of *A. bisporus* origin [81] and the geotraceability of mushrooms [82]. The Principal Component Analysis and Hierarchical Cluster Analysis were performed for fatty acids of *Ganoderma* species [83].

### **5. Conclusions**

Within the analyzed group of autochtonous mushroom species, high concentration in phenolics and flavonoids were associated with the hydroalcoholic extracts. The mushrooms' anatomic part seemed to have influence on the concentration of non-enzymatic antioxidants, but only in the case of aqueous extracts. The antioxidant activity is species dependent, regardless of the type of mushroom extract.

Higher antioxidant abilities were determined for *Boletus edulis*, *Agaricus campestris* and *Chantarellus cibarius*. A significant correlation with the activity of catalase (CAT) was also established in the case of phenolic compounds. For these reasons at least these three mushroom species are promising in terms of designing functional foods and/or bioremediation processes. Chemometrics applied to heterogeneous data sets proved to be a powerful tool for selection of information and taking real time decisions in future research.

**35**

**Author details**

Romania

and Ioana Daniela Dulama1

Science, Targoviste, Romania

provided the original work is properly cited.

Cristiana Radulescu1,2\*, Lavinia Claudia Buruleanu3

Science and Technology, Targoviste, Romania

© 2019 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,

3 Valahia University of Targoviste, Faculty of Environmental Engineering and Food

1 Valahia University of Targoviste, Institute of Multidisciplinary Research for

2 Valahia University of Targoviste, Faculty of Sciences and Arts, Targoviste,

\*Address all correspondence to: radulescucristiana@yahoo.com

, Andreea Antonia Georgescu3

*Correlation between Enzymatic and Non-Enzymatic Antioxidants in Several Edible Mushrooms…*

*DOI: http://dx.doi.org/10.5772/intechopen.82578*

*Correlation between Enzymatic and Non-Enzymatic Antioxidants in Several Edible Mushrooms… DOI: http://dx.doi.org/10.5772/intechopen.82578*
