Proximate Polyphenolic Composition, Phytochemical Components and Bioactivity Evaluation of Twelve Strawberry (Arbutus unedo L.) Genotypes Grown Under Moroccan Ecological Conditions

There are not many exhaustive works emphasizing the amount of genetic diversity among the strawberry tree (Arbutus unedo L.) genotypes in Morocco. This work aims to assess the biochemical composition of strawberry tree fruits, as well as to establish the variation of this composition among them. In this study, total phenols, total flavonoids, condensed and hydrolyzable tannins, total anthocyanins and free radical scavenging activity through ABTS were investigated in strawberry tree fruits. Furthermore, qualitative and quantitative analyses of individual phenolic compounds by high-performance liquid chromatography (HPLC) were carried out. Color parameters such as lightness (L*), Chroma (c*) and hue angle (h°) were also investigated. All studied variables showed highly significant differences among all samples with the exception of hydrolyzable tannins and chromatic coordinates. Total phenolics varied from 22.63 ± 1.74 to 39.06 ± 2.44 mg GAE/g dry wt, total flavonoids varied from 3.30 ± 0.60 to 8.62 ± 1.10 mg RE/g dry wt and total anthocyanins ranged between 0.12 ± 0.06 and 0.66 ± 0.15 mg cya-3-glu/100g dry wt. In addition, condensed and hydrolyzable tannins amounts were in the range of 10.41 ± 1.07 16.08 ± 1.50 mg TAE/g dry wt and 4.08 ± 2.43 6.34 ± 3.47 respectively. Moreover, the IC50 value (ABTS) ranged between 1.75 and 19.58 mg AAE/g dry wt. 17 phenolic compounds were detected in strawberry tree fruits. Gallocatechol and catechin were the most abundant phenolic compound. Matrix of correlations revealed significant positive and negative correlations among variables particularly c*, a* and b*. Principal component analysis showed that the first three components formed than 68% of the total inertia. The following variables gallic acid, protocatechuic, gallocatechin, gallic acid derivative, chlorogenic acid, syringic acid, ellagic acid derivative II, L* and h* were the most involved in the total variance explained. Hierarchical clustering classified samples into one main cluster, with a single branch. The results highlight a high biochemical diversity within studied strawberry genotypes, which is probably more genetically related.


INTRODUCTION
The strawberry tree (Arbutus unedo L.) is a wild fruit tree belonging to the Ericaceae family and the genus Arbutus. It is an evergreen fruit tree distributed in the Atlantic-Mediterranean region mainly in southern Europe, North Africa, Ireland, Palestine and Macaronesia [1]. This plant can grow at different altitudes, from sea level to 1200 m, in various types of soils, but preferably acidic soils [2]. Strawberry tree is frequently used in traditional medicine in some countries such as Spain and Morocco |3,4]. It is known for its diuretic, antiseptic and laxative effects as well as for its uses in the treatment of cardiovascular pathologies such as hypertension, atherosclerosis and thrombosis [5][6][7]The potential health-promoting properties are mainly related to the antioxidant capacity provided by phenolic compounds such as flavonoids, tanins, vitamins (C and E) and carotenoids [8][9][10][11][12][13]. Fruits of strawberry tree contain different phenolic compounds, namely gallic acid [14,15], protocatechuique acid, gentitic acid, phydroxybenzoic acid, vanillic acid, m-anisic acid, arbutin, ß-D-glucogallin, gallic acid 4-O-ß-Dglucopyranoside, 3-Ogalloylquinic acid, 5-Ogalloylquinic acid, 3-O-galloylshikimic acid and 5-Ogalloylshikimic acid. In the past, a few studies were conducted to demonstrate the genetic diversity among strawberry tree genotypes from Turkey, Spain and a few other countries [16][17][18]. Morphological and biochemical markers have been widely used in breeding studies and in the investigations into diversity of species and the relationship between genotypes, cultivars and their wild parents. More recently, biochemical content, in particular, bioactive content of fruits has been widely searched in terms of their human health benefits. The breeders are now searching to find genotypes that have higher bioactive content in order to use them in cross breeding activities for the purpose of obtaining new cultivars that possess high nutrient value for health [19].
In Morocco, strawberry tree fruits remain underexploited and their consumption lasts seasonal. To our knowledge, there are no scientific studies yet studying biochemical variability among strawberry tree genotypes under Moroccan ecological conditions. Moreover, phenolic compounds and fruit skin color measurements were rarely included in previous works on strawberry tree characterization. In the present work twelve strawberry tree genotypes, belonging to several areas in Morocco, were characterized according to their biochemical markers and skin coordinates color. The main objectives of this study were : (1) to assess the biochemical composition and colorimetric characteristics of strawberry tree fruits ; (2) to determine the correlations between all parameters in order to provide information about the ones that are potentially important in assessing strawberry tree genotypes and (3) to evaluate the biochemical diversity among the strawberry tree genotypes belonging to several areas in Morocco. The genetic variability determined in this study will facilitate strawberry tree breeding and identification of genetic determinants of trait variability.

Plant material
Fruits of strawberry tree (Arbutus unedo L.) were harvested during the period between October and November of 2019 from several regions of Morocco where they grow naturally (Table 1). At each site, random samples of fruits were harvested at their full maturity. All selected berries had no diseases and visual blemishes. The samples were frozen at -20 °C, freeze-dried and ground prior to the analyses.

Extraction procedure
1g of powder from each sample was mixed with 25 mL of ethanol (1:25, w/v) at 25°C for 15 min using an IKA T-18 digital Ultra-Turrax homogenizer. The homogenate was then centrifuged for 10 minutes at 6,000 rpm and the supernatant was removed from the residue. The latter was homogenized and the supernatant removed as above. The supernatants are then combined and filtered.

Total phenols (TP)
TP was determined by using the Folin-Ciocalteu method described by Ben Salem et al., (2018) [20]. Briefly, 100 µ L of diluted sample (1/100) with ethanol was added to 400 µ L of 1/10 diluted Folin Ciocalteu reagent. After 5 minutes, 500 µ L of 10% (w/v) sodium carbonate solution was added. After 1 hour of incubation at room temperature, absorbance at 765 nm was measured in triplicate. The TP is expressed as gallic acid equivalent per dry weight of strawberry tree fruit (mg GAE/g DW).

Total flavonoids (TF)
TF was measured using the colorimetric method with aluminum chloride (Lamaison and Carnat., 1990) [21]. 1 mL of the sample was diluted separately then mixed with 1 mL of a 2% aluminum chloride solution. The mixture was incubated at room temperature for 15 minutes. Rutin is used to develop the calibration curve. The absorbance is measured at 430 nm with a spectrophotometer. The results were expressed as rutin equivalent per dry weight of strawberry tree fruit (mg RE/g DW).

Condensed tannins (CT)
The condensed tannins are determined according to the colorimetric method of Folin Denis described by (Joslyn., 1970) [22]. Briefly, 75 mL of distilled water, 1 mL of diluted extract, 5 mL of Folin Denis reagent and 10 mL of saturated solution (CO3Na2) were introduced into 100 mL vial. (The saturated solution (CO3Na2) was prepared from 43.75 g of sodium carbonate dissolved in 100 mL of hot water (70° to 80°C) and after cooling, the solution was filtered and adjusted to 125 mL). After mechanical stirring, the preparation is left to stand for 30 minutes and the optical density is measured at 760 nm. A tannic acid standard range was prepared under the same conditions. The results were expressed as tannic acid equivalent per dry weight of strawberry tree fruit (mg TAE/g DW).

Hydrolyzable tannins (HT)
Hydrolyzable tannins are determined according to the method described by (Willis and Allen., 1998) [23]. Brief, 5 mL of (2.5%) KIO3 were placed in test tubes, which were then placed in a water bath at 25°C. 1 mL of diluted extract or standard was added and vortexed for 10 seconds then the tubes were returned to the water bath. After the optimum time (4 min) had elapsed, the absorbance was measured at 550 nm using a spectrophotometer. A tannic acid standard range was prepared under the same conditions. The results were expressed as tannic acid equivalent per dry weight of strawberry tree fruit (mg TAE/g DW).

Total anthocyanins (TA)
TA content was quantified according to the pH differential method using two buffer systems : potassium chloride buffer pH 1.0 (25 mM) and sodium acetate buffer

Determination of antioxidant capacity
The antioxidant activity was evaluated using ABTS [2,2'-azinobis-(3-ethylbenzothiazoline-6sulfonic acid)] assay and the results were presented as a mean ± standard deviation. The method used was described by (Dorman et Hiltunen., 2004) [26]. The ABTS cation radical was prepared by mixing an equal volume of potassium persulfate solution (2.45 mM) with stock solution of ABTS (7mM). After 16 hours of incubation, the solution was diluted with ethanol to give 0.7 to 0.8 absorbance at 734 nm. 10 µ L of this freshly prepared solution were added to 990 µ L of extract and absorbance was measured at 734 nm after 6 min of incubation. The results were expressed as mg Ascobic Acid Equivalent /g dry weight.

Extraction method
Samples (1 g) were mixed with 10 mL of methanol: water (80:20, v/v) and then, the mixtures were sonicated during 30 min, and macerated one hour in refrigeration (4 ºC). After the time, the samples were centrifuged for 10 min, 8000 g at 4 ºC. The supernatants were collected and the pellets were mixed with 10 mL of acetone: water (70:30, v/v) and the same steps were repeated (sonication, maceration and centrifugation). Then, the supernatants were combined and evaporated to dryness using a rotary evaporator R-205 under reduced pressure, at 40 ºC. 5 mL of methanol were added to the residue, and the mixture was well shaken in a Vortex for 2 min. Due to the high sugar content present in the samples, which could interfere with the HPLC column, the samples were loaded onto a C18 Sep-Pak cartridge, previously conditioned with 5mL of methanol, 5 mL of pure water, and then with 5 mL of 0.01 mol/L HCl. The cartridge was washed with 5 mL of pure water and then eluted with acidified methanol (0.1 g/L HCl). The collected fractions were stored at -20 ºC until further use.

Determination of polyphenolic compounds
Polyphenolic profiles of all samples obtained in each phase of in vitro GID were determined by High Performance Liquid Chromatography (HPLC) following the methodology described by (Genskowsky et al., 2016) [27]. A volume of 20 µ L of the samples were injected into a Hewlett-Packard HPLC series 1200 instrument equipped with C18 column (Mediterranea sea 18, 25 × 0.4 cm, 5 cm particle size) from Teknokroma, (Barcelona, Spain). Polyphenolic compounds were analyzed in standard and sample solutions using a gradient elution at 1 mL/min. The mobile phases were composed by formic acid in water (1:99, v/v) as solvent A and acetonitrile as solvent B. The chromatograms were recorded at 280, 320, 360 and 520 nm. Polyphenolic compounds identification was carried out by comparing UV absorption spectra and retention times of each compound with those of pure standards injected in the same conditions.

Skin color
Color determinations were made on fresh RO and TO, at 25 ± 1 °C, using a using a NH310 colorimeter (Shenzhen 3NH Technology, China). This spectrophotometer uses an illuminant D65 and a 10° observer as references. Color data are provided as CIE L*a*b* coordinates, which define the color in a three-dimensional space. L* indicates lightness, taking values within the range of 0−100, and a* and b* are the chromatic coordinates, green−red and blue−yellow coordinates, respectively. Parameter a* takes positives values for reddish colors and negative values for the greenish colors, whereas b* takes positive values for yellowish colors and negative values for bluish colors. Color analyses were run in 25 replicates for each block, which means 10 strawberry fruit per treatment. Each measure was examined with three replications

Statistical analysis
The means were evaluated according to descriptive statistics represented as Mean ± SE. Data analysis was performed using IBM SPSS v22. Analysis of variance (ANOVA) was performed to test significant differences among the samples. The differences in studied variables were estimated with Duncan new multiple range (DMRT) test. Correlation coefficients and their levels of significance were calculated using Pearson correlation. Principal Component Analysis was carried out using correlation matrix. In addition, a scatter plot was created according to the first three principal components (PC1, PC2 and PC3). A distance matrix generated from biochemical data was used for cluster analysis based on Euclidian distance to better understand the patterns of variability among the samples.

RESULTS AND DISCUSSION
All studied variables showed highly significant differences among all samples (p<0.05), with the exception of hydrolyzable tannins and chromatic coordinates.

Total Phenols (TPC)
The total phenols content of strawberry tree fruits are presented in Table 2. Significant differences (p=0.004) were observed among the genotypes studied. The total phenols ranged from 22.63 to 39.06 mg GAE/g DW, with an average of 30.20 mg/g DW. The highest value was recorded in "LAN" (39.06 mg/g DW) while the lowest value was observed in "OUA" (22.63 mg/g DW). The TPC of strawberry tree fruits reported in this study is higher than those found by other authors ; Doukani and Tabak., (2015) [28] reported a range of 14.74 to 7.025 mg GAE/g in Algerian strawberry tree cultivars. In an other study, Seker and Toplu (2010) [29] reported a TPC ranging from 17.7 to 25.8 mg GAE/g). Also, Colak (2019) ; Ruiz-Rodríguez et al., (2011) [30,13] recorded TPV values raging from 483 and 627 mg GAE/100 g and from 951 to 1973 mg/100g in Turkish and spanish genotypes respectively. while Vidrih et al., (2013) [19] reported an average of 590 mg/100g in Croatian fruits.

Total flavonoids
The results of the total flavonoids content are presented in Table 2. A significant variation in total flavonoids was observed at (p <0.001) among genotypes. The total flavonoids content ranged from 3.30 to 8.62 mg GAE/g DW, with an average of 6.44 mg GAE/g DW. The highest flavonoids content was observed in "KHN" (8.62 mg/g DW) followed by "TAM" (8.26 mg/g DW) and the lowest value was observed in "KSB" (3.30 mg/g DW). These concentrations are higher than those recorded by Jurica

Condensed and hydrolysable tannins
Condensed and hydrolyzable tannins results data are presented in Table 2. A significant variation of condensed tannins was found at (p=0.027) among genotypes. However, there was no statistical difference for hydrolyzable tannins among genotypes (p=0.998). On the one hand, The condensed tannins content ranged from 10.41 to 16.08 mg TAE/g DW, with an overall mean of 13.03 mg TAE/g DW. The highest condensed tannins content was observed in "LAN" (16.08 mg TAE/g DW), while the lowest was observed in "BNO" (10.41 mg TAE/g DW). On the other hand, hydrolyzable tannins ranged from 4.08 to 6.34 mg TAE/g DW, with an overall average of 5.37 mg TAE/g DW. The highest value was found in "CHF" (6.34 mg AT/g DW) while the lowest was recorded in "TAH" (4.08 mg AT/g DW). These values were approximately similar with those revealed by (Jurica et al., 2017) [31] who found (16.75-18.92 mg GAE/g) for total tannins.

Antioxidant activity
The results obtained for antioxidant activity based on the radical scavenging capacity (ABTS) were reported in Table 2. Significant differences (p˂0.001) were observed among the genotypes studied. The value of ABTS assay ranged from 1.75 to 19.58 mg ascorbic acid equivalent/g DW, with an overall mean of 7.49 mg ascorbic acid equivalent/g DW. Gündoğdu et al, (2018) (33) analysed the antioxidant capacity (ABTS) of Turkish strawberry tree fruits. They found values ranged between 17.51 and 30.06 µ mol TE/g. In other study, Colak, (2019) [30] analysed the antioxidant capacity (ABTS) of Turkish strawberry tree fruits. They found values comprissed between 18.07 and 33.41μmol TE/g.

Skin Color
Color measurements data are reported in Table 4, there were no statistical differences between strawberry tree genotypes for all color indices L*, a*, b*, c* and h°. Data showed that Lightening (L*) values ranged from 25.83 to 50.78. The genotypes ''LAN'' and ''BMR'' had the brightest skin color (50.78 and 39.09, respectively). Whereas, ''BNO'' and ''OUA'' recorded the lowest values of L* (25.83 and 26.27, respectively). a* and b* values ranged from 28.93 to 58.91 and from 70.85 to 93.73, respectively. "TAM" showed the highest a* value while "LAN" showed the least value. The b* value was higher (93.73) in "TAM", while the lowest b* value was found in "OUL" (70.85). According to positive values of a* and b*, strawberry tree fruits included reddish orange to deep crimson red fruit colors. The Chroma (c*) was higher in genotypes with clear and bright fruit skin color, where it varied generally between 78.30 and 110.17. The highest and the least red colour intensity were found in "TAM" and "LAN" genotypes, respectively. The hue angle (h°), ranged between 54.70° and 66.45°. The highest h° value was observed for "LAN" (66.45°), while the lowest value was observed for "OUA" (54.70°). All strawberry tree genotypes were lighter (higher L* values) and tended to be more red (higher a* values) and yellower (higher b* values). Furthermore, the genotypes showed higher values of chroma (c*) and hue angle (h°) corresponding to a lighter color. Therefore, skin color evaluation using these coordinates is of great importance in characterization and assessment of fruits quality and maturity. These results are globally, in accordance with several studies. Islam

Principal components analysis
Principal component analysis (PCA) based on correlation coefficients was used to discriminate between variables in the datasets. The aim of this analysis was to determine the main factors to reduce the number of effective parameters to use in classification of the strawberry tree genotypes based on their biochemical parameters. In our study, only a principal component loading of more than |0.5| was considered as being significant for each factor. Total variance of 93.19% was explained by seven components ( Table 6). The first three components consisted of 26 variables, which explained 68.77% of the total variability observed, which means that these caracters had the highest variation between the genotypes and had the highest impact on discrimination of them. The first component accounted for 36.90 % of the total variance, which is strongly influenced by the protocatechuic (0.97), gallic acid (0.  [33,30]. They have reported that the biochemical attributes are important in order to evaluate the variation in traits of strawberry tree genotypes. These parameters can be used as a useful tool for selecting genotypes for breeding programs or to recommend new cultivars with superior traits. Scatter plot was prepared according to the first three principal components : PC1, PC2 and PC3, (respectively 36.90, 18 and 13.87 % of total variance) that discriminate between the genotypes according to their chromatic coordinates and biochemical characteristics ( Figure 1). Starting from negative to positive values of PC1, the distribution of genotypes indicated an decrease in the peel lightness, total phenols and condensed tannins. Whereas, starting from negative to positive values of PC2, the most of phenolic compound increased in their values. However, it showed a decrease in the skin coordinates color a*, b* and c*. Starting from negative to positive values of PC3, the distribution of genotypes indicated an increase in the total anthocyanins, total flavonoids, hydrolyzable tannins and ABTS. Our results are in agreement with several studies (Gündoğdu et al., 2018 ; Colak., 2019) (33,30). These studies indicated that high diversity in biochemical traits could be used as an efficient marker system to discriminate between strawberry tree genotypes

Cluster analysis
Multivariate analysis based on bioactive compounds and antioxidant activity showed high polymorphism among the studied strawberry tree genotypes. Unweighted pair group method (UPGMA) cluster analysis using Euclidean distance coefficient was performed to highlight the similarities among and differences between these genotypes. The genotypes were divided into one main cluster, with a single branch (Figure 2). The genotype ''OUZ'' was totally discriminated from the cluster.

Conclusion
This study proved a high variability among the genotypes studied. The results obtained showed that the strawberry tree fruits are an important source of bioactive compound. Seventeen phenolic compound were identified by HPLC, of which gallocatechol and catechin were the most abundant ones. According to the results obtained, the fruits of strawberry tree can be considered as a very rich source of health-promoting compounds, the fact that may encourage a lot of people to consume them as an alternative source of bioactive compounds. The biochemical composition of the fruits of strawberry tree could also be useful to improve their future pharmacological and cosmetic usages. Besides, The findings confirmed the usefulness and the importance of biochemical parameters and their complementary information to study diversity within the wild inheritance of strawberry tree. Therefore, the results found in this study may be useful to promote the cultivation of species so as to maintain its longevity and diversity as well as to facilitate its use in breeding programs and industrial valorization. The high variability in biochemical composition observed among genotypes could be attributed to genetic factors. Therefore, it will be important to study and identify the genes responsible for the biochemical properties in order to understand the pattern of variation in the biochemical composition of strawberry tree genotypes.