Polyphenols are plant secondary metabolites that are found in high concentrations in a wide variety of foods. More than 8000 phenol structures have been described, and several are found only in a limited number of species. Due to their beneficial health properties [1
], polyphenols have attracted a large amount of interest in recent years. Among foods, grapes are one of the fruits with the highest polyphenol content, and have been investigated by many authors [2
]. Phenolic compounds are differentially distributed in the skins, pulp, and seeds of grapes [6
]. The beneficial health effects of these compounds have been reported from the consumption of phenolic compounds from whole grapes [7
], grape juice [8
], and grape seeds [10
Red grapes are rich in both flavonoid and non-flavonoid compounds [2
]. Anthocyanins are the main group of polyphenols found in red grapes. Malvidin-3-O
-glucoside is the predominant anthocyanin in red grapes, but cyanidin-3-O
-glucoside, and peonidin-3-O
-glucoside also occur in considerable amounts [3
]. Flavanols are also present in considerable quantities, and (+)-catechin and (−)-epicatechin are the most abundant forms. Several derivatives, such as epicatechin gallate, epigallocatechin gallate, epigallocatechin, and procyanidin dimer B1 and B2 have also been reported [2
]. In red grapes, flavonols are less distributed than anthocyanins. Despite this, quercetin, kaempferol, and isorhamnetin glucosides can occur in different cultivars [2
]. The most abundant phenolic acids in grapes occur as hydroxycinnamic acids, especially as tartaric conjugates [2
]. In addition, several hydroxybenzoic acid derivates have been described in red grapes, of which gallic acid is the most abundant compound [3
]. Resveratrol and its glucoside form trans-polydatin have been identified as the most common components of the stilbene group found in red grapes. However, their levels are low, and only occur in skins [3
Phenolic compounds are plant stress metabolites [12
], and several factors have an impact on the phenolic profile of fruits [2
], including grapes [3
]. Specifically, the cultivation method can greatly modulate the phenolic content of grapes [16
]. For example, organic cultivation is known to modulate the phenolic profile of grapes [15
]. Additionally, grape varieties can present markedly different phenolic profiles [4
]. In the specific case of grapes, different phenolic profiles have been demonstrated to give rise to different biological effects [8
]. For example, the consumption of two different wines with noteworthy phenolic profiles led to different low density lipoprotein (LDL)-oxidation outcomes [9
]. Therefore, it is essential to characterize the food matrix in order to correlate it with certain beneficial health effects. To do so, specific extraction methods must be developed, as a multitude of factors can affect the phenolic extraction from food matrixes [18
]. In this sense, the extraction temperature, extraction solvent, liquid-to-solid ratio (LSR), and extraction time have been reported to have an impact on the extraction of phenolics from grape cranes, seeds, skins, and other grape by-products [19
]. Traditional optimization studies take into account only one factor at time, while other factors are kept constant. Consequently, this approach is expensive, laborious, and time-consuming [25
]. Its main disadvantage is that interactions between variables cannot be evaluated and are overlooked [25
]. In this sense, response surface methodology (RSM) is a useful strategy [18
]. RSM enables the evaluation of the effects of different independent variable interactions between themselves and between dependent variables [25
]. Indeed, RSM has been widely used in the optimization of phenolic compound extraction from several vegetal sources [19
]. However, the one-factor-at-a-time approach is useful in the selection of the experimental ranges that are used in RSM designs [27
Notably, phenolic compounds are abundant in grape seeds and skins, and many studies only focus on the characterization of these grape tissues [4
]. However, grape pulp also contains phenolic compounds [5
], with the main family being the hydroxycinnamic acids [31
]. Although some authors have evaluated the phenolic content of whole grapes [32
], the total polyphenol content in red grapes appears to be underestimated, as most studies usually do not include grape pulps on their studies [4
]. A full profile of the edible parts of grapes, which includes the pulp, is essential to link the health benefits associated with their consumption to their phenolic profile. In this sense, hydroxycinnamic acids have been reported to have diverse health functions [34
], which makes their contribution to the beneficial bioactivity of whole grape consumption plausible.
There are several studies that have optimized the methods to extract phenolics from grape seeds, skins, cranes, stalks, and other grape by-products [19
]. However, studies optimizing the extraction of the major phenolic families in red grapes as a whole (including skins, pulp, and seeds) are lacking. Given that grapes are typically consumed whole, the study of the whole matrix is of key importance when linking grape consumption and beneficial health effects. Therefore, this study aimed to develop an easy-to-perform extraction method that is capable of extracting the most representative phenolics from whole red grape varieties. Additionally, this method was used to profile the phenolic content of two whole red Grenache grape cultivars—one produced organically (OG) and the other conventionally (CG)—and a Peruvian Red Globe grape (PG).
2. Materials and Methods
2.1. Plant Material
Organic (OG) and non-ecological (conventional, CG) Grenache grapes (Vitis vinifera) were harvested at maturity in the region of Rasquera (Tarragona, Spain). To assure that the only agronomic variable influencing the phenolic profile of the red Grenache grapes was the cultivation system, both OG and CG were harvested on the same day (26 September 2015) from contiguous vineyards. Peruvian (PG) Red Globes (Vitis vinifera) were purchased from Mercabarna (Barcelona, Spain). Pedicels were manually removed, and whole grapes, which included skins, seeds, and pulp, were frozen in liquid nitrogen and later ground to homogeneity. Next, the homogenates were lyophilized for one week in a Telstar LyoQuest lyophilizer (Thermo Fisher Scientific, Madrid, Spain) at −85 °C. The lyophilized homogenates were ground to a fine powder. The grape powder was kept dry and protected from humidity and light exposure until extraction. OG was used in the optimization studies, while CG and PG were used to provide insights into the different phenolic compositions in different fruit varieties and cultivation methods.
2.2. Chemicals and Reagents
Acetonitrile, methanol, ethanol (HPLC analytical grade), and glacial acetic acid were purchased from Panreac (Barcelona, Spain). Formic acid was purchased from Scharlab (Barcelona, Spain). Ultrapure water was obtained from a Milli-Q Advantage A10 system (Madrid, Spain). The Folin–Ciocalteu and p-dimethylaminocinnamaldehyde (DMACA) reagents were purchased from Fluka/Sigma-Aldrich (Madrid, Spain). Gallic acid (GA), (−)-epicatechin (Ecat), p-coumaric acid (pCou), and (+)-catechin (Cat) were purchased from Fluka/Sigma-Aldrich; chlorogenic acid (Chl), malvidin-3-O-glucoside (Mv3G), (−)-epigallocatechin gallate (EGCG), and procyanidin dimer B2 (B2) were purchased from Extrasynthése (Lyon, France); cyanidin-3-O-rutinoside (Cy3R) was purchased from PhytoLab (Vestenbergsgreuth, Germany); resveratrol (Rvt) was purchased from Carl Roth (Karlsruhe, Germany); and rutin (Rut) was kindly provided by Nutrafur S.A. (Murcia, Spain).
Ecat, pCou, Cat, Chl, EGCG, B2, Rvt, and Rut were individually dissolved in methanol (MetOH) at 2000 mg/L, while Mv3G and Cy3R were dissolved in MetOH (0.01% HCl) at 500 mg/L. All standard stock solutions were freshly prepared every three months and stored in amber glass flasks at −20 °C. Mixed standard stock solutions of Ecat, p-Cou, Cat, Chl, Mv3G, EGCG, B2, Cy3R, Rvt, and Rut were prepared with water:acetic acid (95:5 v/v) to obtain the concentration needed to construct the calibration curves.
2.3. Polyphenol Extraction
Grape powder was weighed (0.1 g, 0.2 g, 0.4 g, and 0.8 g) to obtain the desired LSR, and was mixed with one mL of preheated extraction solvent (methanol:water, v:v). Different extraction MetOH proportions (1% formic acid), extraction temperatures, times, and extraction steps were used throughout the experiment. In all of the cases, the methanol solution included 1% formic acid. Extractions were performed at 500 rpm agitation with protection from light exposure. Once the extraction was completed, the samples were centrifuged at 9500× g for 10 min at 4 °C, and the supernatants were stored at −20 °C until further use.
2.4. Single-Factor Studies
To select the working ranges for the RSM independent variables, the effect of LSR, methanol concentration, and temperature on the extraction of grape phenolics were evaluated based on total phenolic content (TPC), total anthocyanin content (TAC), and total flavanol content (TFC) extracted from OG, as these variables represent the major phenolic families found in grapes [2
]. The LSR was evaluated at the ratios of 10 mL/g, 20 mL/g, 40 mL/g, and 80 mL/g; temperatures of 25 °C, 40 °C, 55 °C, 70 °C, and 85 °C; and methanol proportions of 30%, 50%, 60%, 70%, and 90%. All of the extractions lasted for 30 min, and the extraction variables were kept constant at 80 mL/g, 55 °C, and 50% when not evaluated.
2.5. Response Surface Design
The extraction was optimized with OG using an RSM experimental design. A face-centered central composite design with two factors was selected. It consisted of 11 randomized runs, with three center-point replicates. The independent variables used in the RSM were MetOH proportion (40–80%, Xi
) and temperature (40–85 °C, Xj
). The LSR (80 mL/g) and extraction time (30 min) were fixed as constant variables during the RSM experiment. The experimental data were fitted to a second polynomial response surface, which follows Equation (1):
is the dependent variable, β0
is the constant coefficient, and βi
, and βij
are the linear, quadratic, and interaction regression coefficients, respectively. Xi
, and Xji
represent the independent variables.
Individual phenolic compounds were quantified by the high-performance liquid chromatography with a diode array detector (HPLC-DAD) method and used in the RSM optimization study. The results of the RSM design were analyzed with Design-expert 9.0.6 software (Trial version, Stat-Ease Inc., Minneapolis, MN, USA). Single parameters that were not influenced by the extraction factors were omitted from the model.
2.6. Kinetic Study
A kinetic study was performed to evaluate the effect of time on the polyphenol extraction yield of OG. Seven extraction times, ranging from 0 to 120 min, were selected. The LSR was fixed at 80 mL/g, the MetOH percentage was fixed at 65%, and the temperature was fixed at 72 °C. TPC, TAC, and TFC were determined for all of the extracts and used to evaluate the effect of time on the polyphenol extractability.
2.7. Sequential Extractions
Three consecutive extractions were performed to evaluate the influence of multiple extractions on the polyphenol extraction yield in OG. The extractions were carried out under the following conditions: LSR of 80 mL/g, MetOH proportion of 65%, temperature of 72 °C, and extraction time of 100 min. The TPC, TAC, and TFC were determined for all of the extracts and were used to evaluate the effect of sequential extractions on the polyphenol extraction yield.
2.8. Application of the Method
The specific and optimized extraction methodology was used to characterize the phenolic profiles of OG, CG, and PG. In brief, the extraction conditions were as follows: LSR of 80 mL/g, MetOH or ethanol (EtOH) proportion of 65% (1% formic acid), temperature of 72 °C, and extraction time of 100 min.
2.9. Analysis of Response Variables
2.9.1. Total Phenolic Content
The TPC of the extracts was determined by the Folin–Ciocalteu method adapted from Nenadis et al. [35
]. Briefly, 10 µL of the extract and 50 µL of the Folin–Ciocalteu reagent were successively added to an Eppendorf tube containing 500 µL of Milli-Q water and mixed. The samples were kept in the dark for three minutes, and 100 µL of Na2
(25%) was added. The samples were brought to a final volume of one mL with Milli-Q water and were maintained in the dark for one h. The absorbance was read at 725 nm using an Eon BioTek spectrophotometer (Izasa, Barcelona, Spain) against a water sample (blank) that underwent identical treatment. GA was used to construct the calibration curve between 40–400 mg/L. The results were expressed as milligrams of gallic acid equivalents per gram of dry weight (mg GAE/g dw).
2.9.2. Total Anthocyanin Content
The TAC of the extracts was analyzed by the pH differential method [11
]. The extracts were diluted with sodium acetate buffer (0.4 M, pH 4.5) and potassium chloride buffer (0.025 M, pH 1.0) to relevant spectrophotometric ranges (0.4–0.6). Next, the absorbance was read at 515 nm and 700 nm using an Eon BioTek spectrophotometer (Izasa, Barcelona, Spain). The TAC was expressed as milligrams of malvidin 3-O
-glucoside equivalents per gram of dry weight (mg Mv3G Eq/g dw). The molar absorbance of Mv3G (493.44 g/mol) used was 28000 L/mol × cm.
2.9.3. Total Flavanol Content
The TFC of extracts was estimated by the DMACA method [38
]. Briefly, the samples (0.1 mL) were mixed with 0.5 mL of DMACA solution (0.1% 1 N HCl in MetOH) and allowed to react at room temperature for 10 min under protection from light exposure. The absorbance was then read at 640 nm using an Eon BioTek spectrophotometer (Izasa, Barcelona, Spain). Different Cat concentrations between 5–100 mg/L were used to construct the calibration curve. TFC was expressed as milligrams of (+)-Cat equivalents per gram of dry weight (mg Cat Eq/g dw).
2.9.4. HPLC-DAD Analysis of Phenolic Compounds
Polyphenol separation was achieved using a ZORBAX Eclipse XDB-C18 (150 mm × 2.1 mm i.d., five-µm particle size) as the chromatographic column (Agilent Technologies, Palo Alto, CA, USA) equipped with a Narrow-Bore guard column (2.1 mm × 12.5 mm, 5 µm particle size). The mobile phase was water:acetic acid (95:5, v:v) (A) and acetonitrile:acetic acid (95:5, v:v) (B) in gradient mode as follows: initial conditions 0% B; 0–30% B, 0–18 min; 30–100% B, 18–19 min; 100% B isocratic, 19–20 min; 100–0% B, 20–21 min. A post-run of six minutes was required for column re-equilibration. The flow rate was set at 0.5 mL/min, and the injection volume was 10 µL for all of the runs. Before the injection, all of the samples were diluted 1:1 in mobile phase A.
Identification and quantification of the phenolic compounds of interest was achieved with a UV/Vis photodiode array detector (1260 Infinity, Agilent Technologies, Palo Alto, CA, USA). Chromatograms were recorded from 200 nm to 600 nm. Flavanols were detected at 280 nm [3
], p-coumaric acid was detected at 290 nm, hydroxycinnamic acids and stilbenes were detected at 320 nm [3
], flavonols were detected at 340 nm, and anthocyanins were detected at 520 nm [3
]. When standard compounds were not available, flavanols were quantified as Cat equivalents, hydroxycinnamic acids were quantified as Chl equivalents, flavonols were quantified as Rut equivalents, and anthocyanins were quantified as Mv3G equivalents. The results were expressed as milligrams of equivalents per kilogram of dry weight (mg Eq/kg dw).
2.9.5. HPLC-DAD Method Validation
Calibration curves, linearity, intraday variability (precision), interday variability (reproducibility), detection limits, and quantification limits were calculated in mobile phase A spiked with polyphenol standards (Table S1
). The peak areas of various concentrations of standards were used to construct the calibration curves. The method’s precision was calculated as the relative standard deviation (% RSD) of the concentration in a triplicate analysis of three different spiked samples (50 µg/mL, 25 µg/mL, and one µg/mL). Method reproducibility was calculated as the relative standard deviation (% RSD) of three different standard compound concentrations (50 µg/mL, 25 µg/mL, and one µg/mL) analyzed in triplicate over three consecutive days. Sensitivity was evaluated by determining the limits of detection (LOD) and quantification (LOQ), respectively, which were defined as the concentrations corresponding to threefold and 10-fold of the signal-to-noise ratio.
The results of the RSM design were analyzed using Design-expert 9.0.6 software (Trial version, Stat-Ease Inc., Minneapolis, MN, USA). SPSS 19 software (SPSS Inc., Chicago, IL, USA) was used for all of the other statistical analysis. All of the experiments were performed in triplicate; the statistical significance was evaluated using one-way ANOVA or Student’s t-test, and p-values less than p < 0.05 were considered to be statistically significant.