Comparative study of conventional and pressurized liquid extraction for recovering bioactive compounds from Lippia citriodora leaves

Food Research International 109 (2018) 213–222

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Comparative study of conventional and pressurized liquid extraction for recovering bioactive compounds from Lippia citriodora leaves

T

⁎

Francisco Javier Leyva-Jiméneza, Jesús Lozano-Sáncheza,b, , Isabel Borrás-Linaresa, David Arráez-Romána,b,1, Antonio Segura-Carreteroa,b,1 a b

Functional Food Research and Development Center, Health Science Technological Park, Avenida del Conocimiento s/n, E-18100 Granada, Spain Department of Analytical Chemistry, Faculty of Sciences, University of Granada, Fuentenueva s/n, E-18071 Granada, Spain

A R T I C LE I N FO

A B S T R A C T

Keywords: HPLC-ESI-TOF/MS Pressurized liquid extraction Response surface methodology Phenolic compounds Lippia citriodora

The extraction of bioactive compounds from Lippia citriodora leaves (Lc) has been evaluated by comparison between Pressurized Liquid Extraction (PLE) and conventional extrations combined with HPLC-ESI-TOF-MS in order to maximize recovery of phytochemicals and to know the efficiency of both methods. To achieve these goals, conventional extractions were carried out using different concentrations of ethanol and water. On the other hand, pressurized liquid extractions were performed by a Response Surface Methodology (RSM) based on a Central Composite Design 23 model to address the bioactive compounds extraction. The independent variables selected were temperature, percentage of solvent (ethanol and water) and extraction time. The response variables were extraction yield and recovery of bioactive compounds. Thus, the optimum values to maximize yield was 200 °C, 46% ethanol and 17 min. In addition, the design versatility allowed found the optimal conditions for each chemical group and to validate them. This experimental model followed by HPLC-ESI-TOF/MS analysis offer for the first time an easy, rapid, and objective manner to optimize extraction of bioactive compounds from Lc leaves by PLE, which could be used as methodology for development functional ingredients.

1. Introduction A healthy lifestyle has led the population to a higher consumption for natural food and it has promoted the search for new functional ingredients to be included in food in order to achieve a high life standard. For this reason, current research is focused in the study of natural sources. Botanicals present broad range of phytochemical which have been related to beneficial effects on the treatment of diseases, such as cancer, cardiovascular diseases or osteoporosis, among others (Barreiros, David, & David, 2006; Mokrani & Madani, 2016). Phenolic compounds are one of these phytochemicals able to induce healthy effects. Lippia citriodora is known as lemon verbena or Aloysa citriodora, is a deciduous shrub originated in South America (Hudaib, Tawaha, & Bustanji, 2013). Traditionally, it has been used in infusions to alleviate fever or stomach ache including other beneficial effects, such as, antiinflammatory (Veras et al., 2013) and anti-obesogenic (Herranz-López

et al., 2015). The pharmacological properties of lemon verbena are related to the content of phytochemical in aerial parts of the plant belonging to phenolic compounds, mainly flavonoids and phenylpropanoids. From the chemical composition point of view, phytochemical content of natural extracts depends on the extraction procedure, the solvent used, the origin of the raw material, its storage condition and the pre-treatment applied (Moure et al., 2001). Nowadays, the search of novel techniques which present less extraction times, solvent consumption and more environmental-friendly than conventional ones is being carried out to avoid the degradation of compounds which are thermolabiles (Shuai, Yong, Shouyao, & Zhongyi, 2015). Therefore, alternative methods to extract natural bioactive substances as pressurized liquid extraction (PLE) have been studied. PLE is an innovative technique to get natural extracts from plants with high quality that combines elevated temperature and pressures with solvents to achieve fast and efficient extraction with a wide range of compounds polarities (Howard & Pandjaitan, 2008). PLE has already been used to recover

Abbreviations: ANOVA, analysis of variance; BPC, base peak chromatogram; CCD, central composite design; CV, coefficient of variance; DAD, diode-array detection; GRAS, generally recognized as safe; ESI, electrospray interface; LC, liquid chromatography; LOD, limit of detection; LOQ, limit of quantification; MLS, method of least squares; MS, mass spectrometry; PLE, pressurized liquid extraction; R2, coefficient of regression; RP-HPLC, reversed-phase high-performance liquid chromatography; RSD, relative standard deviation; RSM, response surface methodology; RT, retention time; SLE, solid-liquid extraction; S-N, signal-to-noise ratio; TOF, time of flight ⁎ Corresponding author at: Functional Food Research and Development Center, Health Science Technological Park, Avenida del Conocimiento s/n, E-18100 Granada, Spain. E-mail address: [email protected] (J. Lozano-Sánchez). 1 These authors are joint senior authors on this work. https://doi.org/10.1016/j.foodres.2018.04.035 Received 6 March 2018; Received in revised form 13 April 2018; Accepted 15 April 2018 Available online 17 April 2018 0963-9969/ © 2018 Elsevier Ltd. All rights reserved.

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EXP (Thermo Scientific, Sunnyvale, CA, USA). Each procedure was carried out in triplicate. PLE were performed using a pressurized liquid extractor (ASE™ 350 system, Dionex, Sunnyvale, CA, USA) equipped with a solvent controller. Extractions were done using different combinations among solvent composition (ethanol and water), temperatures and static cycle time. The pre-set default condition was extraction pressure at 11 MPa. The solvents were previously degassed for 15 min to remove the dissolved oxygen in order to avoid any possible oxidation. For each extraction, 5 g of sample were mixed with 10 g of sea sand and loaded onto 33 mL stainless-steel extraction cells. In order to prevent clogging of the metal frits, cellulose filters were placed at each end of the cell and two portions of sand (5 g) were placed between sample and cellulose filters. The obtained extracts were immediately cooled in ice to attain a temperature of 20–25 °C. The extracts were dried under vacuum in a Savant™ SpeedVac Concentrator SC250 EXP (Thermo Scientific, Sunnyvale, CA, USA) and stored at −20 °C until HPLC analysis.

bioactive compounds from different botanicals (Alonso-Salces et al., 2001; Santos, Veggi, & Meireles, 2012). In these researches, this advanced extraction technique was able to extract selectively phenolic compounds using Generally Recognized As Safe (GRAS) solvents, such as water and ethanol. Chemical characterization of phenolic compounds in plant matrix can be a complex task since they have a high variety of structures. The usual technique to analyze polyphenols from botanical matrix is reversed-phase high-performance liquid chromatography (RP-HPLC). Generally, this separative technique has been coupled to different detector for the qualitative and quantitative characterization, such as ultraviolet and diode-array detection (DAD) (Quirantes-Piné, Funes, Micol, Segura-Carretero, & Fernández-Gutiérrez, 2009) and mass spectrometry (MS) (Cádiz-Gurrea et al., 2014). In this context, HPLCMS has proved to be a very useful tool in the characterization of natural products (Quirantes-Piné et al., 2013). The aim of this study was to develop a PLE process followed by the analytical characterization using HPLC-ESI-TOF/MS to maximize the extraction of different phenolic compounds from L. citriodora leaves. The PLE optimization was carried out using a Response Surface Methodology based on Central Composite Design 23 model with 16 experiment including center and star points. The independent variables were temperature, extraction time and percentage of solvent (ethanol and water) and the response variables were yield and chemical composition obtained by the optimized HPLC-ESI-TOF/MS method.

2.4. Experimental desing Response surface methodology (RSM) was applied in order to optimize the recovery of phytochemical compounds. For this purpose, a Central Composite Design 23 (CCD) model with two axial points was used to evaluate the effect of the extraction parameters. Temperature (40, 110, 180 °C), extraction time (5, 12.5, 20 min) and percentage of ethanol (15, 50, 85%) were chosen for independent variables procuring a total of 16 experiments which were conducted in a randomized order (Supporting Information Table 1). The response variables were yield and the chemical composition of the extracts determined by HPLC-ESITOF-MS. Experimental data were then fitted to a quadratic polynomial model which was displayed in the following general equation (Eq. (1)):

2. Materials and methods 2.1. Chemicals All chemicals used in this study were of analytical reagent grade and used as received. For extraction procedure, water was purified by a Milli-Q system from Millipore (Bedford, MA, USA) and ethanol were purchased from VWR chemicals (Radnor, PA, USA) and was analytical grade. For analytical procedure, formic acid was purchased from SigmaAldrich (Steinheim, Germany). Acetonitrile of LC-MS grade was purchased from Fisher chemicals (Waltham, MA, USA). The standards, for the calibration curves, loganic acid, kaempferol-3-glucoside, quercetin, and verbascoside were purchased either from Fluka, Sigma-Aldrich (Steinheim, Germany) or Extrasynthese (Genay Cedex, France). Apigenin were purchased from Sigma-Aldrich (Steinheim, Germany) and used as internal standard.

k

Y = α0 +

k

k

k

∑ αi Xi + ∑ αii Xi2 + ∑ ∑ i=1

i=1

i=1 j=i+1

αij Xi Xj (1)

where Y represent the predicted response; α0 is a constant coefficient that fixed the response at the central point of the experiments, and αi¸ αii and αij are the regression coefficients of the linear, quadratic and interaction terms, respectively; Xi and Xj represent the value of independent variables. The parameters of the models, determination of the optimum conditions and plot of response surface were estimated by using Statgraphics Centurion software XVI provided by Statpoint Technologies (Warrenton, VA, USA). The adequacy of the model obtained for PLE, were checked by evaluating coefficient of determination (R2), coefficient of variation (CV) and the Fisher's test value (F-ratio). Significant values were considered when p < 0.05. The relationship between independent variables and responses were analyzed by 3D response surface plots which represents the dependent variables in function of two most influence independent variables. Optimum conditions were calculated considering the maximization of individual response variables. Therefore, independent variables were kept in ranges while response was optimized.

2.2. Sample preparation L. citriodora leaves were provided by Monteloeder (Alicante, Spain). Lemon verbena leaves were ground using an ultra-centrifugal mill ZM200 (Retsch GmbH, Haan, Germany) at room temperature. The material was storage in darkness and kept at room temperature until extraction. 2.3. Extraction procedures of phenolic and other polar compounds from L. citriodora leaves Isolation of the phytochemical fraction from lemon verbena powder was carried out using conventional solid-liquid extractions (SLE) and pressurized liquid extraction (PLE). With regard to SLE, a comparative study using five different proportions of H2O:EtOH (SLE-A, 100:0; SLEeB, 75:25; SLEeC, 50:50; SLE-D, 25:75; and SLE-E, 0:100, v/v), was performed to determine the best solvent to extract the polar fraction. To achieve this goal, the sample (1.5 g) was shaken for 90 min with 50 mL of the different proportion solvents indicated above. Finally, the samples were centrifuged at 13000 rpm for 10 min in a centrifuge (Sorvall ST 16 R, Thermo Scientific, Leicestershire, UK), and the supernatants were collected and filtered through a 0.45 μm filter. The solvent was evaporated under vacuum in a Savant™ SpeedVac Concentrator SC250

2.5. HPLC-ESI-TOF-MS analysis HPLC analyses were performed with a RRLC 1200 series (Agilent Technologies, Palo Alto, CA, USA), equipped with a vacuum degasser, autosampler, a binary pump, and a DAD detector. The analytical column used was a 150 mm × 4.6 mm id, 1.8 μm Zorbax Eclipse Plus C18 (Agilent Technologies, Palo Alto, CA, USA). The mobile phase used was water: acetonitrile 90:10 (v:v) with 0.1% formic acid as eluent A and acetonitrile as eluent B. The flow rate was 0.5 mL min−1. The total run time was 35 min using the following multistep linear gradient: 0 min, 5% B; 25 min 20% B; 30 min 40% B; 35 min 5% B. The initial condition was hold for 5 min. The injection volume was 10 μL and the 214

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iridoids, flavonoids, phenylpropanoids and other polar compounds. From them, 7 compounds (peaks 2, 7, 8, 11, 12, 15 and 44) could not be characterized.

separation of the compounds was carried out at room temperature. The HPLC system was coupled to a TOF mass spectrometer (Bruker Daltonik, Bremen, Germany) equipped with an orthogonal electrospray (ESI) interface (model G1607 from Agilent Technologies, Palo Alto, CA, USA) operating in negative ionization mode. At this stage, the effluent from the HPLC column was reduced using a “T” type splitter before being introduced into the mass spectrometer (split ratio 1:3). In this sense, the flow which arrived to the ESI-TOF-MS detector was 125 μL min−1. The detection was carried out considering a mass range of 50–1000 m/z. The optimum value of the source parameters were: capillary voltage of +4 kV; drying gas temperature, 210 °C; drying gas flow, 9 L min−1; and nebulizing gas pressure, 2.3 bar. The values of transfer parameters were: capillary exit, −120 V; skimmer 1, −40 V; hexapole 1, −23 V; RF hexapole, 80 Vpp; and skimmer 2, −20 V. The instrument was calibrated externally with a 74,900–00-05 Cole Palmer syringe pump (Vernon Hills, IL, USA) that was directly connected to the interface and contained a 10 mM sodium formate cluster solution. The mixture was injected at the beginning of each run and all the spectra were calibrated prior to compound identification. Because of the compensation for temperature drifts inside the instrument, this external calibration provided accurate mass values that were better than 5 ppm. The accurate mass data of the molecular ions were processed through the software DataAnalysis 4.0 (Bruker Daltonics), which provided a list of possible elemental formulas by using Generate-Molecular Formula Editor. The Generate Molecular Formula Editor uses a CHNO algorithm, which provides standard functionalities such as minimum/ maximum elemental range, electron configuration and ring-plus double bonds equivalents, as well as a sophisticated comparison of the theoretical with the measured isotope pattern (Sigma Value) for increased confidence in the suggested molecular formula. Quantitation of the identified analytes in all extracts was carried by HPLC-ESI-TOF-MS. Four standard calibration graphs of the principal compounds found in the samples were prepared using the four commercial standards. Stock solutions at concentration of 5,000 mg/L for samples were first prepared by dissolving the appropriate amount of the extract in 50:50, H2O:EtOH (v:v) and then diluted to working concentrations (1, 20). Apigenin was added at 25 μg/mL and used as internal standard. The validation of the proposed method was carried out with the linearity, sensitivity, and precision parameters. Limits of detection (LOD) and quantification (LOQ) were, respectively, set at S: N = 3 and S: N = 10, where S: N is the signal-to-noise ratio. Repeatability of the method was measured as relative standard deviation (RSD %) in terms of concentration. A PLE extract, selected randomly, was injected (n = 3) on the same day to determine intraday precision and 3 times on the 3 consecutive days (interday precision, n = 9).

3.1.1. Organic acids According to MS data and the HPLC elution profile, only one compound was characterized as organic acid. Peak 1 with m/z 195.0510 was identified as gluconic acid. It was previously reported in food product obtained from L. citriodora (Quirantes-Piné, Arráez-Román, Segura-Carretero, & Fernández-Gutiérrez, 2010). 3.1.2. Iridoid glycosides The proposed method used in this work enable the characterization of ten iridoid glycosides. Gardoside and theveside (peaks 3 and 9, respectively) were previously identified in genus Lippia (Quirantes-Piné et al., 2010). With regard to the rest of iridoid glycosides, these compounds were tentatively characterized according to their m/z and the data compiled from literature as ixoside (peak 4) (Quirantes-Piné et al., 2010), myxospyroside (peak 10) (Franzyk, Jensen, & Olsen, 2001), teucardoside (peak 13) (Elmasri, Yang, Hegazy, Mechref, & Paré, 2016) and lippisode II (peak 22) (Rastrelli, Caceres, Morales, De Simone, & Aquino, 1998) since they were found in botanical from Lippia genus or lamial order. Peaks 25 and 26 displayed similar m/z (535.1457 and 537.1614) and molecular formula (C25H27O13 and C25H29O13). These compounds were tentatively characterized as lippioside I derivative and lippioside I, respectively (Rastrelli et al., 1998). The origin of the hydrated derivative could be related to the extraction conditions. Peak 34 showed a deprotonated molecula at m/z 521.1698. According to the literature, this compound was assigned to hydroxycampsiside, which was previously identified in lamial order, such as Pondranea ricasoliana and Campsis grandiflora (Guiso, 1982; Han et al., 2012). The MS data allowed to identify compound 35 as a novel iridoid glycoside, lippianoside B. This compound has previously been reported in Aloysa triphylla (Wang et al., 2015). Peaks 37 and 42 gave deprotonated molecules at m/z 551.1770 and 569.2240, respectively, and were proposed as durantoside I and manuleoside H. These chemical compounds were detected in some botanicals from lamial species (Gousiadou, Kokubun, Gotfredsen, & Jensen, 2014; Takeda et al., 1995). 3.1.3. Flavonoids Examination of mass spectra and elution profile of compounds in L. citriodora leaves revealed seven flavonoids including four glucuronic derivatives and three aglycons. In this sense, peak 20 (m/z 637.1140), peak 24 (m/z 621.1097), peak 28 (m/z 651.1355) and peak 39 (m/z 635.1254) were characterized as luteolin-7-diglucuronide, apigenin-7diglucuronide, chrysoeriol-7-diglucuronide and acacetin-7-diglucuronide, respectively, according to literature (Quirantes-Piné et al., 2010). Some of them revealed some anti-inflammatory (El-Hawary, Yousif, Abdel Motaal, & Abd-Hameed, 2012) and anti-obesogenic effects (Herranz-López et al., 2015). Peak 43 (32.0 min), peak 45 (34.6 min) and peak 46 (35.1 min) were identified according to their elution order and mass spectra data as methyl quercetin, dimethyl kaempferol and dimethyl quercetin, respectively.

3. Results and discussion 3.1. Identification of polar compounds in L. citriodora leaves by HPLC-ESITOF-MS The resulting base peak chromatograms (BPC) for L. citriodora leaves extract obtained by the HPLC-ESI-TOF-MS method are shown in Fig. 1A (PLE) and Fig. 1B (SLE). The identification was based upon an interpretation of their MS spectra provided by TOF-MS and the information suitable on the literature. Proposed compounds with their retention times (RT), experimental and calculated m/z, molecular formula, error (ppm) and miliSigma value (mSigma) are compiled in Table 1. These compounds have been numbered according their elution order. Overall, HPLC-ESI-TOF-MS method allowed the detection of 46 compounds. Among these, a total of 39 compounds were classified in five groups corresponding to their chemical structures: organic acids,

3.1.4. Phenylpropanoids/phenylethanoids The proposed method also allowed the characterization of eigthteen phenylpropanoids/phenylethanoids in Lc leaves extracts, being the most abundant chemical group detected in the sample. The most intense peak (29) was associated with verbascoside, which is well-known compound in this plant (Bilia, Giomi, Innocenti, Gallori, & Vincieri, 2008; Carnat, Carnat, Fraisse, & Lamaison, 1999). It showed an experimental m/z at 623.1981 and molecular formula C29H35O15. In addition, two verbascoside isomers were found and associated with peaks 32 (isoverbascoside) and 33 (forsythoside A). These isomeric structures have also been reported in this matrix (Quirantes-Piné et al., 2010). The 215

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Fig. 1. Base Peak Chromatogram of representative PLE (A) and SLE (B) extracts obtained by HPLC-ESI-TOF-MS.

3.1.5. Other polar compounds The elution time and m/z value at 387.1661 allowed to characterize peak 14 as tuberonic acid glucoside. This compound is a plant hormone related with abiotic and biotic stress (Seto et al., 2011) found in Lc studies (Quirantes-Piné et al., 2010).

bioactivity of these compounds have been related to the activation of the energy sensor AMPK or to ameliorates joint diseases (Cheng, Murugaiyah, & Chan, 2015; Herranz-López et al., 2015). Regarding verbascoside derivatives, peaks 17 and 18 with experimental m/z 641.2087 and molecular formula (C29H37O16) generated by TOF analyzer, were tentatively proposed as the hydration products of βhydroxyverbascoside (peak 19) and β-hydroxyisoverbascoside (peak 21) (Quirantes-Piné et al., 2013). Peak 23 yielded a deprotonated molecule at m/z 637.1765 and molecular formula (C29H33O16). This compound was tentatively identified as the oxidation product of verbascoside (oxoverbascoside). This oxidized form, being more polar than its nonoxidized derivative, elutes earlier (13.5 and 16.4 min, respectively). Verbascoside A (peak 31) was also found at 18.3 min and it presented a m/z at 667.2244. Other phenylpropanoids/phenylethanoids were also detected. Peak 5 displayed a deprotonated molecule at m/z 461.1664. According to the elution order and molecular formula, this compound was identified as verbasoside (Quirantes-Piné et al., 2009). Peak 6 was characterized as cistanoside F according to previous reports in bibliography (QuirantesPiné et al., 2013). Peak 16 was found to be descaffeoylcrenatoside, which was previously reported in botanical sources from the same order of Lc (Qu et al., 2016). Peak 27 was characterized as campneoside I. Lariciresinol glucopyranoside was tentatively proposed as new compound to peak 30 since it was detected in other botanical related to Lc (Karioti, Protopappa, Megoulas, & Skaltsa, 2007). Peak 36 with a molecular formula C29H35O14 was characterized as lipedoside A I. This phenylethanoid was previously reported in plant of order lamial, specifically Lamiophlomis rotate. Compounds 38 and 40 were also characterized as eukovoside and martynoside since they were well described in genus Lippia in many reports (Bilia et al., 2008; QuirantesPiné et al., 2013; Timóteo, Karioti, Leitão, Vincieri, & Bilia, 2015). Regarding the molecular formula (C29H36O13) of peak 41, this compound was tentatively identified as osmanthisude B, which was previously reported in Cistanche tubulosa (Morikawa et al., 2014).

3.2. Quantification of polar compounds in L. citriodora leaves by HPLCESI-TOF-MS In order to quantify the amount of polar compounds present in Lc leaves, four calibration curves were prepared using loganic acid, kaempferol-3-glucoside, quercetin, and verbascoside. Calibration curves were calculated by using ten points at different concentrations. All of them were obtained by plotting the standard concentration as a function of the peak area (area standard/area internal standard) obtained from HPLC-ESI-TOF analyses. The validation of the proposed method was carried out with the linearity, sensitivity and precision parameters. Supporting information Table 2 shows the following analytical parameters: limits of detection (LOD) and quantification (LOQ), calibration range, calibration curve equations and regression coefficient (R2). The linearity range of the analytical method was established with standard solutions. In all cases, the linearity of calibration curves was better than 0.99. Repeatability of the method described was measured as relative standard deviation (RSD %) in terms of concentration. A Lc leaves extract was injected (n = 3) on the same day (intraday precision) and 3 times on the 3 consecutive days (interday precision, n = 12). Intraday repeatability of the developed method (for all the analytes) was from 1.18 to 8.47%, whereas the interday repeatability was from 4.92 to 9.70%, for MS detector. The compound concentrations were determined using the ratio area standard/area internal standard and by interpolation in the corresponding calibration curve. Verbascoside was the only compound quantified with its respective commercial standard. The rest of polar 216

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Table 1 Proposed Compounds tentatively identified in Lippia citriodora leaves by HPLC-ESI-TOF-MS. Numbers designing compounds correspond to peaks as depicted in Fig. 1A. Pico

RT (min)

m/z experimental

Molecular formula

m/z calculated

Error (ppm)

mSigma

Proposed compound

1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30 31 32 33 34 35 36 37 38 39 40 41 42 43 44 45 46

2.8 3.0 3.8 4 4.1 4.7 5.2 5.7 5.9 7.2 7.5 8.2 8.2 8.5 8.8 9.0 9.7 10.1 10.3 10.5 10.6 11.3 13.5 14 14.3 14.7 15.0 15.7 16.4 17.5 18.3 18.6 19.1 19.4 19.4 19.8 20.3 20.9 23.9 26.9 30.7 31.2 32 33.7 34.6 35.1

195.0524 191.0201 373.1170 387.0966 461.1707 487.1492 475.1422 355.0653 389.1078 449.1277 355.0655 593.1512 489.1600 387.1653 433.2074 459.1485 641.2061 641.2081 639.1946 637.1085 639.1946 553.1544 637.1765 621.1097 535.1437 537.1613 653.2125 651.1304 623.2029 521.2038 667.2289 623.2042 623.2019 521.1698 549.1640 607.2062 551.1770 637.2208 635.1310 651.2356 591.2136 569.2278 315.0515 327.2159 299.0589 329.0690

C6H11O7 C6H7O7 C16H21O10 C16H19O11 C20H29O12 C21H27O13 C27H23O8 C12H13O8 C16H21O11 C18H25O13 C15H15O10 C27H29O15 C21H29O13 C18H27O9 C20H33O10 C20H27O12 C29H37O16 C29H37O16 C29H35O16 C27H25O18 C29H35O16 C25H29O14 C29H33O16 C27H25O17 C25H27O13 C25H29O13 C30H37O16 C28H27O18 C29H35O15 C26H33O11 C31H39O16 C29H35O15 C29H35O15 C25H29O12 C26H29O13 C29H35O14 C26H31O13 C30H37O15 C28H27O17 C31H39O15 C29H35O13 C27H37O13 C16H11O7 C18H31O5 C16H11O6 C17H13O7

195.0510 191.0197 373.1140 387.0933 461.1664 487.1457 475.1398 355.0671 389.1089 449.1301 355.0671 593.1496 489.1614 387.1661 433.2079 459.1508 641.2087 641.2087 639.1931 637.1140 639.1931 553.1563 637.1774 621.1097 535.1457 537.1614 653.2087 651.1355 623.1981 521.2028 667.2244 623.1981 623.1981 521.1664 549.1614 607.2032 551.1770 637.2138 635.1254 651.2294 591.2083 569.2240 315.0510 327.2177 299.0561 329.0667

−7.0 −2.0 −7.9 −8.4 −9.2 −7.2 −4.9 4.9 2.9 5.4 4.3 2.7 9.3 1.8 1.1 5.1 4.1 −1.1 −2.3 −6.0 −2.3 3.3 1.5 0 3.8 0.2 −5.8 8.0 −7.6 −1.9 −6.8 −9.8 −6.1 −6.4 −4.8 −4.9 0 −6.6 −8.9 −9.4 −8.9 −6.7 −1.7 5.1 −9.5 −7.1

9.7 3.2 5.1 4.3 21.8 3.4 7.3 2.7 12.1 13.4 24.3 16.3 7.2 8.4 15.3 14.6 18.3 13.5 44.3 57.5 44.3 10.5 6.2 3.8 14.7 15.4 20.7 19.1 27.0 32.5 13.3 25.1 18.1 2.2 13 5.1 19.7 23.4 12.2 30.6 29.5 10.6 9.2 23.0 5.3 11.8

Gluconic acid UK1 Gardoside Ixoside Verbasoside Cistanoside F UK2 UK3 Theveside Myxopyroside UK4 UK5 Teucardoside Tuberonic acid glucoside UK6 Descaffeoylcrenatoside β Hydroxyverbascoside derivative β Hydroxyisoverbascoside derivative β Hydroxyverbascoside Luteolin-7-diglucoronide β Hydroxyisoverbascoside Lippioside II Oxoverbascoside Apigenin-7-diglucoronide Lippioside I Derivative Lippioside I Campneoside I Chrysoeriol-7-diglucuronide Verbascoside Lariciresinol glucopyranoside Verbascoside A Isoverbascoside Forsythoside A Hydroxycampsiside Lippianoside B Lipedoside A I Durantoside I Eukovoside Acacetin-7-diglucoronide Martynoside Osmanthuside B Manuleoside H Methyl quercetin UK7 Dimethyl kaempferol Dimethyl quercetin

compounds were resulted to apply PLE condition 7, 8, 10, 12 and 15 (94,386, 96,481, 96,555 and 94,816 μg analyte/g of Lc leaves, respectively). Regardless of the time of the static cycle, all of these conditions were performed at temperatures above 110 °C and ethanol concentrations higher than 50%. As expected, the most abundant group in PLE extracts was phenylethanoids/phenylpropanoids, ranging from 34,126 to 83,506. Among these, verbascoside was the most representative compound. In case of flavonoids, chrysoeriol-7-diglucuronide was the most recovered analyte meanwhile theveside was the most abundant iridoid glycoside. Concerning SLE, the quantification data are shown in Supporting Information Table 2. It can be seen that SLE-B was the condition where most polar compounds were recovered (76,367 μg / g of Lc leaves) meanwhile SLE-E was the condition where lower amount of polar compounds retrieved (9648 μg / g of Lc leaves). Broadly, phenylethanoids and phenylpropanoids chemical group were present at highest concentrations, being verbascoside the major compound in all SLE conditions. It is necessary to remark that higher concentrations of eukovoside and martynoside were found in SLE-B and SLE-C conditions. As expected, chrysoeriol-7-diglucuronide was the most representative flavonoid in Lc leaves in all SLE conditions, excluding SLE-E (100%

compounds were tentatively quantified on the basis of other compounds with similar structures. Thus, loganic acid calibration curve was used to quantify iridoid glycosides. According to their structures, luteolin-7-diglucuronide, chrysoeriol-7-diglucuronide, apigenin-7-diglucuronide and acacetin-7-diglucuronide were quantified using kaempferol-3-glucoside meanwhile methyl quercetin, dimethyl kaempferol and dimethyl quercetin were quantified by using of quercetin calibration curve. Regarding phenypropanoids and phenylethanoids group, all these compounds were quantified with verbacoside calibration curve. It should be taken into account that the response of the standards could differ from that of the analytes in the Lc extracts and consequently the quantitation of these compounds is only an estimation of their real concentration. Total content for all phenolic and other polar compounds in Lc leaves extracts was tentatively calculated as the sum of the individual compound concentrations. Table 2 shows the total polar compound for all PLE and SLE extracts. The individual compound concentrations to each experimental conditions are given in Supporting Information Table 3 (PLE) and Table 4 (SLE). In all experimental PLE conditions, the range of concentrations was from 45,791 to 96,555 μg analyte/g of Lc leaves. Results of quantification revealed the highest concentrations of 217

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between the CCD and response variables. Since the applied model could be satisfactorily used to describe experimental data, MLS was followed to generation of regression equations and creation of response surface graphs, shown in Fig. 2(A to E).

Table 2 Extraction yield and quantitative results of Central Composite 23 experimental design and SLE extractions expressed in μg Analyte/g of Lc leaves; Value = X ± SD. Yield (%)

Total polar compound (μg / g of Lc leaves)

Experimental design condition PLE1 25.82 PLE2 12.64 PLE3 33.26 PLE4 32.22 PLE5 20.85 PLE6 18.17 PLE7 32.63 PLE8 35.60 PLE9 33.38 PLE10 33.81 PLE11 12.12 PLE12 34.49 PLE13 39.67 PLE14 18.25 PLE15 62.11 PLE16 43.40

90,048 50,702 86,704 88,030 47,779 59,384 94,386 96,481 52,621 95,991 45,791 96,555 66,572 65,680 94,816 47,994

SLE SLE SLE SLE SLE SLE

26,000 ± 1475 76,367 ± 5743 57,567 ± 4517 38,289 ± 3122 9648 ± 1048

condition A B C D E

20.06 25.28 18.50 18.4 4.03

± ± ± ± ± ± ± ± ± ± ± ± ± ± ± ±

3.3.2. Extraction yield Extraction yields for each experimental condition are summarized in Table 2 which includes the ANOVA test of the regression models for the response variables extraction yield (Y1) and total polar compounds (Y2). With regard to extraction yield, the results revealed that the regression coefficient (R2) reached a high value (93%), allowing an explanation of considerable part of the variance within data. In addition to this parameter, the lack of fit was not significant. The obtained parameters of ANOVA enabled to confirm that this model provided a good approximation to the experimental conditions. Concerning the effect of independent variables, temperature exerted significant effects on extraction yield (p < 0.05). Furthermore, first and second-order terms of solvent composition were also significant (p < 0.10). However, the main effect of time, quadratic effects of time and temperature and the interaction between variables were not significant. In this scenario, temperature was the most influential variable when extraction yield was maximized. Fitting experimental data to a reduced model and keeping only the significant parameters in the quadratic model, provided the model equation (Eq. (2)):

5556 2831 3571 3375 1715 2599 4601 3928 3138 3438 2445 2453 1082 3360 2692 1492

Y1 = 8.018 + 0.172X1 + 0.449X2 − 0.006X22 ethanol). For this solid-liquid extraction conditions, it was detected but the concentration was between LOD and LOQ. The amount of total flavonoid evaluated in SLE conditions was much lower than quantified in PLE extracts. Similarly to PLE extraction, theveside in SLE extracts was the iridoid glycoside predominant but the quantity of this compound was lower than PLE extracts. Overall, pressurized liquid extractions revealed to have more yield and they allowed a higher recoveries of all chemical groups than SLE determining that temperature and pressure could exert an enhance about recovery of polar compounds and obtaining higher amount of dry extract from Lc leaves.

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Regarding the suggested model of extraction yield (Eq. (2)), a great value on this response variable could be obtained when high temperature values are applied to Lc leaves. As far as the negative coefficient of ethanol concentration second-order term is concerned, intermediate ethanol concentration could be applied to maximize this variable. Indeed, the analysis of yield RSM graph (Fig. 2A) showed how an increase on the temperature and the use of intermediates concentrations of ethanol were required to achieve higher yield values. It could be explained considering that the application of high temperatures may cause a cell break of raw material releasing polar compounds. Furthermore, the low dielectric constant value for these experimental conditions enables the transference of a large number of compounds towards the solvent increasing extraction yield. Regarding the good fitting results of the model and understanding the influence of independent variables, an optimization of conditions to maximize extraction yield was evaluated. Optimum conditions of PLE extraction yield from Lc leaves were found at 200 °C, 17 min and 46% ethanol. On account of good fitting of the model on extraction yield, optimal conditions were reproduced to confirm the theoretical result. The predictable value to yield was calculated considering the obtained equation (Eq. (2)) after analysis of the model and compared with experimental value when optimal conditions were applied. The theoretical and experimental value were 55.69 and 54.85%, respectively. Results showed a very slight variance (CV = 1%) verifying the results statistical data acquired after CCD analysis. Supporting information Table 5 shows a comparison between experimental results and predicted values for PLE conditions.

3.3. RSM analysis of PLE extraction condition 3.3.1. Model fitting As mentioned above, pressurized extractions revealed better results about extraction of polar compounds and yield. Thus, a response surface methodology based on a CCD 23 was performed in order to maximize the response variables. An analysis of variance (ANOVA) for each response was performed in order to fit and optimize the statistical model. Desirability function did not use because concentration of polar compounds data were calculated using gram of dry extract obtained in each PLE extraction. The response variables results are represented in Table 2. Results were fitted to a quadratic polynomial model (Eq. (1)) and regression coefficients were generated for all responses using the method of least squares (MLS). Regression coefficients, p values and ANOVA results are displayed in Table 3. To evaluate the adequacy of the model, the first indicator used was coefficient of regression (R2). This value evaluates the ability of the model to predict response variable behavior. Regarding response variables of the experimental model applied, this parameter presented high correlation. In addition, a coefficient of variance (CV) was also used, a lower value (< 10%) indicates a good reproducibility of the investigated systems and a value between 11 and 20%, indicates an acceptable variation, since CV describes dispersion of data and a small value indicated low variation in the main value (Liyana-Pathirana & Shahidi, 2005). In this model, CV was not > 7.5%, and consequently the results pointed out a good reproducibility. Other interesting value used to evaluate the adequacy of the model could be provided by lack-of-fit, indicating a good fitting

3.3.3. Total polar compounds According to statistical parameters showed in Table 3 for total polar compounds (Y2), the model presented a great correlation coefficient (R2 = 0.901) and good coefficient of variation (CV < 1%) indicating a slight variance of data and a good prediction of the model to extract these compounds. The significant variables which exert effect on the total polar compounds extraction were temperature, ethanol and time. The quadratic effects of all variables and interaction between them were also significant. Following these results, the model was reduced in the Eq. (3) and used to predict this response variable from Lc leaves: 218

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Table 3 Analysis of variance (ANOVA) of the regression models.a Response variables: extraction yield and total polar compounds. Source

Y1 SS

×1:Temperature ×2:Ethanol ×3:Time ×1 ×2 ×1 ×3 ×2 ×3 X12 X22 X32 Lack-of-fit Pure error Total (corr.) R2 CV

1636.36 170.00 32.32 46.51 14.43 13.32 18.30 258.23 44.03 32.94 1.60922 2399.83 0.931 2.04

Y2 Df 1 1 1 1 1 1 1 1 1 5 1 15

MS 1636.36 170.00 32.32 46.51 14.43 13.32 18.30 258.23 44.03 32.94 1.60922

F-Ratio

P value b

1016.87 105.64 20.08 28.90 8.97 8.28 11.37 160.47 27.36 20.47

0.020 0.062c 0.140 0.117 0.205 0.213 0.183 0.050c 0.120 0.164

SS

Df

MS

F-Ratio

P value

4.81E + 08 3.17E + 06 2.53E + 06 1.32E + 09 6.44E + 08 6.46E + 08 1.16E + 09 1.25E + 09 2.05E + 08 6.20E + 08 20,930.2 6.32E + 09 0.902 0.08

1 1 1 1 1 1 1 1 1 5 1 15

4.81E + 08 3.17E + 06 2.53E + 06 1.32E + 09 6.44E + 08 6.46E + 08 1.16E + 09 1.25E + 09 2.05E + 08 1.24E + 08 20,930.2

23,004.39 151.52 120.81 62,925.51 30,748.30 30,875.63 55,240.30 59,620.14 9787.08 5925.43

0.004b 0.052c 0.059c 0.002b 0.004b 0.004b 0.003b 0.003b 0.006b 0.010

a Y1 = Yield; Y2 = Total polar compounds; ×1 = Temperature; ×2 = Ethanol; ×3 = Time; SS = sum of squares; Df = degrees of freedom; MS = mean square; R2 = Quadratic correlation coefficient; CV = coefficient of variation (%). b Significant (p < .050). c Marginally significant (p < .100).

Fig. 2. Response surface plots. Effect of PLE factor on yield (A) and total polar content (B) iridoid glycosides (C), flavonoids (D) and phenylpropanoid / phenylethanoids (E).

the second-order term and interactions showed a negative sign while its first-order term presented a positive sign. The opening of equation parabola was downward from the beginning to the ending, having better extraction conditions when short times were applied. Therefore, better extractions were reached when time applied was the lowest of

Y2 = −10246.500 + 696.435X1 + 1067.470X2 + 6242.930X3 + 5.237 X1 X2 − 17.084X1 X3 − 34.239X2 X3 − 2.961X12 − 12.306 X22 − 108.591X32 (3) According to the coefficient obtained for independent variable time, 219

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Taking into account that the optimal condition proposed by the experimental model was the axial points of the CCD (39 °C, 5% ethanol and 22 min) and the proposed regression, the effect of time could be the most important factor to maximize extraction of iridoid glycosides. Hence longer extraction times impacted positively on iridoids concentration in the Lc extracts as shown in Fig. 2C. Optimal conditions were done and experimental data were compared with theoretical values. The theoretical and experimental value were 9755 and 4742 μg analyte/g of Lc leaves, respectively. As expected, a huge variation between them were obtained (CV = 49%). These results could be related to a difference behaviour of individual compound belonging to this chemical group. Concerning the analysis of the effect of independent variables on flavonoid group (Y4), the results revealed good fitting parameters. Considering the first parameter to evaluate the fitting, R2 was 0.834 and the lack of fit was not significant. In addition, the main interaction of ethanol concentration, its interaction with temperature and the quadratic effect of them were significant. Besides interaction between ethanol and time was marginally significant. In this sense, Eq. 5 displays a reduced model to explain the flavonoid behavior:

CCD range. These results could be explained because of high temperatures and long extraction times enable degradations of compounds. On the other hand, considering values of temperatures in Eq. (3), higher temperatures could be required to increase the degree of breakage of cell membrane in leaves and to allow the release of compound from the matrix. To better understanding this fitting, Fig. 2B shows the effects of time and temperature towards the total polar compounds. Considering the equation to explain the model for polar compounds behavior and understanding the influence of each independent variables, an optimization of conditions to maximize extraction of the total polar compounds were proposed. Optimum conditions were found at 177 °C, 77% ethanol and 3 min. To check the fitting of the model on polar compounds extraction, the proposed optimal conditions were carried out to confirm the theoretical results. The predictable value to all polar compounds was calculated applying the obtained equation (Eq. (3)) after analysis of the model and compared with experimental value when optimal conditions were applied. The theoretical and experimental value were 101,623 and 83,166 μg analyte/g of Lc leaves, respectively. Analysis of results revealed an acceptable variance (CV = 14%) between theoretical and experimental data obtained from optimum conditions (Supporting Information Table 5). Nevertheless, the lack of fit was significant when total polar compounds were used as response variable. For this reason, the analysis of each chemical group was evaluated in order to establish the fitting of the model for individual families.

Y4 = 4645.860 + 78.506X1 + 91.062X2 + 170.739X3 + 0.605X1 X2 − 3.145X2 X3 − 0.499X12 − 2.012X22

This reduced model explains how time exerted a slight effect on flavonoid extraction. The analysis of the time linear term, exhibited a positive effect on flavonoids recovery, which would mean that prolonged extraction times would affect positively to flavonoid extraction. Nevertheless, the interaction of this factor with ethanol concentration was negative and consequently to maximize flavonoid concentration on Lc extract, time could not be much higher. As shown in Fig. 2D, the negative quadratic term of ethanol concentration revealed that flavonoid content reached the maximum at lower level with the saddle point near to 50% of ethanol. This flavonoid behavior was similar to previously reports in which other green technologies were applied (Zeković, Vladić, Vidović, Adamović, & Pavlić, 2016). This result may be related with the most abundant flavonoids present in Lc leaves which were identified as glucuronide derivatives. These derivatives were recovered with higher effectiveness at high water content in the solvent composition (PLE 1, 3, 5, 9, and 16). After evaluating the model fitting, optimal condition for this chemical group was proposed: 90 °C, 23% ethanol and 16 min. The

3.3.4. Analysis of the model fitting for each chemical group: Iridoid glycosides, flavonoids and phenylpropanoids/phenylethanoids Table 4 shows the ANOVA results for each chemical group: iridoid glycoside (Y3) content, flavonoid content (Y4) and phenylpropanoid/ phenylethanoid content (Y5). As far as iridoid glycosides are concerned, the statistical treatment revealed that the CCD was not fitted for this response variable. Despite that the dispersion of the data was not above 10%, indicating the reproducibility of PLE systems and the lack of fit was not significant, value of R2 indicated that the model adequacy was not acceptable (76%) and the p values of all independent variables showed to be non-significant effects. The corresponding regression model, in spite of not to have a good adequacy is shown in Eq. 4:

Y3 = 2978.200 + 19.464X1 + 36.562X2 + 503.316X3 + 0.666X1 X2 − 0.951X1 X3 − 3.412 X2 X3 − 0.149X12 − 0.842X22 − 8.28 X32

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Table 4 Analysis of variance (ANOVA) of the regression models.a Response variables: Iridoid glycoside content, Flavonoid content and Phenylpropanoid/Phenylethanoid content. Source

×1 ×2 ×3 ×1 ×2 ×1 ×3 ×2 ×3 X12 X22 X32 Lack-of-fit Pure error Total (corr.) R2 CV

Y3

Y4

Y5

SS

Df

MS

F-Ratio

P value

SS

Df

MS

F-Ratio

P value

SS

Df

MS

F-Ratio

P value

3.50E + 06 4.05E + 06 2.80E + 05 2.13E + 07 2.00E + 06 6.42E + 06 2.95E + 06 5.85E + 06 1.19E + 06 1.44E + 07 5.54E + 05 6.26E + 07 0.760 5.22

1 1 1 1 1 1 1 1 1 5 1 15

3.50E + 06 4.05E + 06 2.80E + 05 2.13E + 07 2.00E + 06 6.42E + 06 2.95E + 06 5.85E + 06 1.19E + 06 2.89E + 06 5.54E + 05 6.26E + 07

6.32 7.31 0.51 38.46 3.60 11.58 5.33 10.56 2.15 5.21

0.241 0.226 0.607 0.102 0.309 0.182 0.260 0.190 0.381 0.316

6.25E + 04 9.53E + 07 1.15E + 05 1.76E + 07 1.05E + 06 5.46E + 06 3.29E + 07 3.34E + 07 3.55E + 06 3.77E + 07 1.01E + 05 2.27E + 08 0.834 7.50

1 1 1 1 1 1 1 1 1 5 1 15

62,533 9.53E + 07 1.15E + 05 1.76E + 07 1.05E + 06 5.46E + 06 3.29E + 07 3.34E + 07 3.55E + 06 7.53E + 06 1.01E + 05

0.62 943.60 1.14 174.25 10.44 54.03 325.41 330.44 35.21 74.63

0.576 0.021b 0.479 0.048b 0.191 0.086c 0.035b 0.035b 0.106 0.086

4.13E + 08 9.98E + 07 6.05E + 06 7.55E + 08 5.26E + 08 4.22E + 08 7.05E + 08 7.36E + 08 1.28E + 08 2.47E + 08 7.96E + 04 4.04E + 09 0.939 0.21

1 1 1 1 1 1 1 1 1 5 1 15

4.13E + 08 9.98E + 07 6.05E + 06 7.55E + 08 5.26E + 08 4.22E + 08 7.05E + 08 7.36E + 08 1.28E + 08 4.93E + 07 7.96E + 04

5189.24 1254.68 75.97 9489.02 6606.44 5307.79 8859.54 9248.93 1614.74 619.53

0.009b 0.018b 0.073c 0.006b 0.008b 0.009b 0.007b 0.007b 0.016b 0.030

a

Y3 = Iridoid glycoside content; Y4 = Flavonoid content; Y5 = Phenylpropanoid/Phenylethanoid content; ×1 = Temperature; ×2 = Ethanol; ×3 = Time; SS = sum of squares; Df = degrees of freedom; MS = mean square; R2 = Quadratic correlation coefficient; CV = coefficient of variation (%). b Significant (p < .050). c Marginally significant (p < .100). 220

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theoretical and experimental value were 11,185 and 9207 μg analyte/g of Lc leaves, respectively. The obtained variance between theoretical values and experimental quantification of total flavonoids in Lc leaves was acceptable, pointing out that the model presented a very good fitting on extraction of flavonoids and revealed that developed CCD could be used to maximize flavonoid concentration of extract to be added, as ingredient, in functional foods. With regard to phenylpropanoids / phenylethanoid, the R2 coefficient for this response was 0.93 indicating a good correlation between the experimental results and predicted values by the model. The variability of the results was small due to CV was lower than 1%. However the lack of fit was significant showing a slight lack of fit of the model on this polar group but it showed an adequate precision (< 10%) which indicated that the model can be used to navigate the design space. All effects and their quadratic interactions were significant, except for the main effect of time, which was marginally significant. Therefore, the regression model was the same as follow:

displayed to be influenced by time and ethanol concentration. As expected PLE showed to be more efficient than SLE and it can be applied to processes for obtaining enriched extracts on phenolic compounds from Lc leaves. Supplementary data to this article can be found online at https:// doi.org/10.1016/j.foodres.2018.04.035. Acknowledgments This work was funded by projects AGL2015-67995-C3-2-R (Spanish Ministry of Science and Innovation) and P11-CTS-7625 (Andalusian Regional Government Council of Innovation and Science). The author Leyva-Jimenez gratefully acknowledges the Spanish Ministry of Economy and Competitiveness (MINECO) for the FPI grant BES-2016076618 given to develop this work. The author Lozano-Sánchez also thanks the MINECO for the grant IJCI-2015-26789. References

Y5 = −15253.800 + 589.824X1 + 939.845X2 + 5135.21X3 + 3.965X1 X2 − 15.441X1 X3 − 27.680X2 X3 − 2.312X12 − 9.451X22 − 86.005X32

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Alonso-Salces, R. M., Korta, E., Barranco, A., Berrueta, L. A., Gallo, B., & Vicente, F. (2001). Pressurized liquid extraction for the determination of polyphenols in apple. Journal of Chromatography. A, 933(1–2), 37–43. Retrieved from http://www.ncbi. nlm.nih.gov/pubmed/11758745. Barreiros, A. L. B. S., David, J. M., & David, J. P. (2006). Estresse oxidativo: Relação entre geração de espécies reativas e defesa do organismo. Química Nova, 29(1), 113–123. http://dx.doi.org/10.1590/S0100-40422006000100021. Bilia, A. R., Giomi, M., Innocenti, M., Gallori, S., & Vincieri, F. F. (2008). HPLC–DAD–ESI–MS analysis of the constituents of aqueous preparations of verbena and lemon verbena and evaluation of the antioxidant activity. Journal of Pharmaceutical and Biomedical Analysis, 46(3), 463–470. http://dx.doi.org/10.1016/j. jpba.2007.11.007. Carnat, A., Carnat, A., Fraisse, D., & Lamaison, J. (1999). The aromatic and polyphenolic composition of lemon verbena tea. Fitoterapia, 70(1), 44–49. http://dx.doi.org/10. 1016/S0367-326X(98)00016-1. Cádiz-Gurrea, M. L., Lozano-Sanchez, J., Contreras-Gámez, M., Legeai-Mallet, L., Fernández-Arroyo, S., & Segura-Carretero, A. (2014). Isolation, comprehensive characterization and antioxidant activities of Theobroma cacao extract. Journal of Functional Foods, 10, 485–498. http://dx.doi.org/10.1016/j.jff.2014.07.016. Cheng, L.-C., Murugaiyah, V., & Chan, K.-L. (2015). Flavonoids and phenylethanoid glycosides from Lippia nodiflora as promising antihyperuricemic agents and elucidation of their mechanism of action. Journal of Ethnopharmacology, 176, 485–493. http://dx.doi.org/10.1016/j.jep.2015.11.025. El-Hawary, S. S., Yousif, M. F., Abdel Motaal, A. A., & Abd-Hameed, L. M. (2012). Bioactivities, phenolic compounds and in-vitro propagation of Lippia citriodora Kunth cultivated in Egypt. Bulletin of Faculty of Pharmacy, Cairo University, 50(1), 1–6. http://dx.doi.org/10.1016/J.BFOPCU.2011.12.001. Elmasri, W. A., Yang, T., Hegazy, M.-E. F., Mechref, Y., & Paré, P. W. (2016). Iridoid glycoside permethylation enhances chromatographic separation and chemical ionization. Rapid Communications in Mass Spectrometry : RCM, 30(18), 2033–2042. http:// dx.doi.org/10.1002/rcm.7681. Franzyk, H., Jensen, S. R., & Olsen, C. E. (2001). Iridoid glucosides from Myxopyrum smilacifolium. Journal of Natural Products, 64(5), 632–633. Retrieved from http:// www.ncbi.nlm.nih.gov/pubmed/11374960. Gousiadou, C., Kokubun, T., Gotfredsen, C. H., & Jensen, S. R. (2014). Unexpected secoiridoid glucosides from Manulea corymbosa. Journal of Natural Products, 77(3), 589–595. http://dx.doi.org/10.1021/np400853f. Guiso, M. (1982). Pondraneoside, A New Iridoid Glucoside From Pondranea ricasoliana. Journal of Natural Products, 45(4), 462–465. http://dx.doi.org/10.1021/ np50022a018. Han, X. H., Oh, J.-H., Hong, S. S., Lee, C., Park, J. I., Lee, M.-S. M. K., ... Lee, M.-S. M. K. (2012). Novel iridoids from the flowers of Campsis grandiflora. Archives of Pharmacal Research, 35(2), 327–332. http://dx.doi.org/10.1007/s12272-012-0213-9. Herranz-López, M., Barrajón-Catalán, E., Segura-Carretero, A., Menéndez, J. A., Joven, J., & Micol, V. (2015). Lemon verbena (Lippia citriodora) polyphenols alleviate obesityrelated disturbances in hypertrophic adipocytes through AMPK-dependent mechanisms. Phytomedicine : International Journal of Phytotherapy and Phytopharmacology, 22(6), 605–614. http://dx.doi.org/10.1016/j.phymed.2015.03.015. Howard, L., & Pandjaitan, N. (2008). Pressurized liquid extraction of flavonoids from spinach. Journal of Food Science, 73(3), http://dx.doi.org/10.1111/j.1750-3841. 2007.00658.x. Hudaib, M., Tawaha, K., & Bustanji, Y. (2013). Chemical profile of the volatile oil of lemon verbena (aloysia citriodora paláu) growing in Jordan. Journal of Essential OilBearing Plants, 16(5), 568–574. http://dx.doi.org/10.1080/0972060X.2013.854494. Karioti, A., Protopappa, A., Megoulas, N., & Skaltsa, H. (2007). Identification of tyrosinase inhibitors from Marrubium velutinum and Marrubium cylleneum. Bioorganic & Medicinal Chemistry, 15(7), 2708–2714. http://dx.doi.org/10.1016/j.bmc.2007.01. 035. Liyana-Pathirana, C., & Shahidi, F. (2005). Optimization of extraction of phenolic compounds from wheat using response surface methodology. Food Chemistry, 93(1), 47–56. http://dx.doi.org/10.1016/J.FOODCHEM.2004.08.050.

The first-order terms of the equation pointed out a positive influence of the independent variables on phenylpropanoid / phenylethanoid recovery. By contrast, negative action of quadratic time term suggested that longer extraction times decreased the content of this polar group exhibiting the same performance of flavonoids. On the other side, the second factor which had more effect to this response was ethanol concentration. After analysis of RSM plot (Fig. 2E) to this variable, when a high temperature was applied, greater ethanol concentrations were required to achieve better phenylpropanoids / phenylethanoids recoveries. This behavior could be explained because high temperatures were needed to enable the liberation of compound from the matrix. Furthermore, the combination of high temperatures and ethanol concentration allowed a low dielectric constant values with greater recoveries of these compounds applying lower times. Similar result was reported from other phenolic compound extractions (Mendes et al., 2016). Therefore, the optimum conditions were 191 °C, 85% ethanol and 3 min. Theoretical and experimental value were 87,850 and 73,726 μg analyte/g of Lc leaves, respectively. A great approach between experimental content of this chemical group and theoretical values was obtained. These results indicated that the CCD may be used to maximize the extraction of bioactive compounds, such as verbascoside, contented on Lc leaves. 4. Conclusions The pressurized liquid extraction of Lippia citriodora leaves was implemented to optimizing the extraction yield, total polar compounds and individual extraction of three different chemical groups found in their leaves. Furthermore, the quadratic model applied in this design represented a very good approximation of extraction yield and extraction of flavonoids. By contrast, the model was not fitted for total polar content due to iridoid glycosides were the group with the worst fit towards the experimental model proposed. Phenylpropanoids/ phenylethanoids group shown a good approach of theoretical values. However, the statistical analysis allowed to know the influence of temperature, percentage of ethanol and time to enable the optimum conditions maximizing the response of dependent variables studied. The optimum values of each response variable were proposed and reproduced to verify the predictable data from individual models. According to these results, for extraction yield, high temperature was determinant to increase this response. As far as flavonoids extraction is concerned, high temperature procured a degradation decreasing their response, being percentage of ethanol determinant to achieved greater recoveries. The last response which showed a relative good fitting was phenylpropanoids / phenylethanoids group. This response variable 221

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