Multiclass determination of phytochemicals in vegetables and fruits by ultra high performance liquid chromatography coupled to tandem mass spectrometry

Food Chemistry 141 (2013) 1120–1129

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Analytical Methods

Multiclass determination of phytochemicals in vegetables and fruits by ultra high performance liquid chromatography coupled to tandem mass spectrometry María Isabel Alarcón-Flores, Roberto Romero-González, José Luis Martínez Vidal, Antonia Garrido Frenich ⇑ Group ‘‘Analytical Chemistry of Contaminants’’, Department of Chemistry and Physics, Research Centre for Agricultural and Food Biotechnology (BITAL), University of Almería, Agrifood Campus of International Excellence, ceiA3, E-04120 Almería, Spain

a r t i c l e

i n f o

Article history: Received 10 April 2012 Received in revised form 18 March 2013 Accepted 19 March 2013 Available online 18 April 2013 Keywords: Phytochemicals Vegetables Fruits UHPLC–MS/MS Fast analysis Simultaneous determination

a b s t r a c t In this study a simultaneous determination of several classes of phytochemicals (isoflavones, glucosinolates, flavones, flavonols and phenolic acids) in tomato, broccoli, carrot, eggplant and grape has been carried out by ultra high performance liquid chromatography coupled to tandem mass spectrometry (UHPLC–MS/MS). Solid–liquid extraction assisted by rotary agitator was utilised, using a mixture of methanol:water (80:20, v/v) as solvent. The analytical procedure was validated in all the matrices, obtaining recoveries ranging from 60% to 120% with repeatability values (expressed as relative standard deviations, RSDs) lower than 25%. Limits of quantification (LOQs) were always equal or lower than 50 lg/ kg, except for some glucosinolates (125 lg/kg). Finally the method was applied to different matrices such as tomato, broccoli, carrot, grape and eggplant, observing that chlorogenic acid was detected in most of the samples at higher concentrations in relation to the other compounds. Ó 2013 Elsevier Ltd. All rights reserved.

1. Introduction Phytochemicals, also known as bioactive compounds, are secondary metabolites synthesized by plants (Yao et al., 2004) and they can be described as chemicals from plants that may affect health, but they are not essential nutrients (Temple, 2000). There are several families of phytochemicals, such as glucosinolates, flavonols, isoflavones, phenolic acids and flavones. Moreover, different types and numbers of sugars can be conjugated to aglycones, forming numerous structures of phytochemicals (Luthria & Natarajan, 2009). The most prevalent glycosylation are glucose, although rhamnose, galactose, xylose and arabinose are also present in phytochemicals (Sakakibara, Honda, Nakagawa, Ashida, & Kanazawa, 2003). In this sense, it has been demonstrated that the bioavailability of phytochemicals depends on the sugar moiety attached to the phenolic structure (Hollmanm & Katan, 1999). Fruits and vegetables are considered particularly protective for human health due to their content of phytochemicals (Pennington & Fisher, 2010). In general, these compounds present several characteristics such as antioxidant capacity (Kim, Padilla-Zakour, & Griffiths, 2004), antiinflammation (Vincent, Bourguignon, & Taylor, 2010), lipid profile modification (Wang, Melnyk, Tsao, & Marcone,

⇑ Corresponding author. Tel.: +34 950015985; fax: +34 950015483. E-mail address: [email protected] (A.G. Frenich). 0308-8146/$ - see front matter Ó 2013 Elsevier Ltd. All rights reserved. http://dx.doi.org/10.1016/j.foodchem.2013.03.100

2011) and antitumor effects (Stan, Kar, Stoner, & Singh, 2008). Besides these beneficial properties of phytochemicals in human health, these compounds are responsible of colour, flavour and smell in fruit and vegetables (Miglio, Chiavaro, Visconti, Flogliano, & Pellegrini, 2008), and their contents are influenced by variety, crop type, environmental conditions, location, germination, maturity, processing and storage (Björkman et al., 2011; Carbone, Giannini, Picchi, Lo Scalzo, & Cecchini, 2011). There is no specific legislation related to the presence of this type of compounds in food, but European labelling regulation (European Commission, 2006) requires that nutrition and health claims, which are made on the labels of the products or any form of consumer advertising, are based on scientific studies, where the composition of phytochemicals, including qualitative and quantitative characteristics, must be clearly specified. Common extraction procedures, such as solid–liquid extraction (SLE) assisted with sonication (Gómez-Romero, Segura-Carretero, & Fernández-Gutiérrez, 2010; Helmja, Vaher, Püssa, Raudsepp, & Kaljurand, 2008) or agitation (Velasco et al., 2011), have been employed for the extraction of phytochemical from vegetables or fruits, applying as a solvent methanol (Gómez-Romero et al., 2010) or mixtures of methanol and water at different proportions (Helmja et al., 2008; Velasco et al., 2011). Pressurized liquid extraction (PLE) has been also used (Mustafa & Turner, 2011), but the extraction time for a large number of samples would increase considerably.

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A wide range of analytical methods has been reported for profiling the content of one or more families of phytochemicals in food. The most commonly used technique is high performance liquid chromatography (HPLC) coupled to ultraviolet (UV) detection (Hubert, Berger, & Daydeä, 2005), photodiode-array detection (DAD) (Chen, Li, Chen, Guo, & Cai, 2010), evaporative light scattering detection (ELSD) (Yan, Xin, Luo, Wang, & Cheng, 2005), mass spectrometry (MS) (Volpi & Bergonzini, 2006), DAD–MS (He et al., 2011), UV–MS (El-Hela, Al-Amier, & Ibrahim, 2010) or tandem MS (MS/MS) (Kuhnle et al., 2008). Among all of them, MS/ MS has emerged as one of the preferred analytical techniques for quantification purposes offering sufficient sensitivity, as well as unambiguous identification and quantification of phytochemicals in trace amounts from a single injection. However, HPLC analysis of phytochemical requires 20 min or longer. To reduce this, ultra high performance liquid chromatography (UHPLC) can be used instead. The advantages of this technique are better resolution, shorter running time and higher sensitivity. Furthermore, UHPLC can be coupled to MS/MS for routine analysis, because it allows a rapid detection of more compounds in shorter running times. UHPLC–MS/MS has been used for the detection of several types of phytochemicals such as glucosinolates (Gratacós-Cubarsí, Ribas-Agustí, García-Regueiro, & Castellari, 2010) or other phenolic compounds (Suárez, Macià, Romero, & Motilva, 2008), in different matrices. Moreover, there are no published papers describing simultaneously analysis of glucosinolates, phenolic acids, flavones, isoflavones and flavonols in different vegetables or fruits (Velasco et al., 2011). In general, published papers are mainly focused on the determination of one or a few families of phytochemicals and there are few data related to validation of phytochemicals in different matrices. Therefore, the aim of the present work has been the development of a sensitive and fast method for the simultaneous determination of more than 30 phytochemicals belonging to several classes of phytochemicals such as isoflavones, flavonols, glucosinolates, phenolic acids, flavones and their derivates in fruit and vegetables applying SLE and UHPLC–MS/MS determination. The proposed method can be used for quantification and confirmation of phytochemicals in different vegetables or fruit indicating that it can be applied in routine analysis.

2. Material and methods 2.1. Chemicals and reagents Commercial phenolic compound standards such as genistein, apigenin, quercetin, quercetin-3-O-glucoside, sulforaphane, baicalein, gallic acid, ferulic acid and caffeic acid were purchased from Sigma–Aldrich (Steinheim, Germany). Other standards as isorhamnetin, isorhamnetin-3-O-rutinoside, isorhamnetin-3-O-glucoside, apigenin-7-O-rutinoside, apigenin-7-O-neohesperoside, daidzein, kaempferol, kaempferol-3-O-glucoside, kaempferol-3-O-rutinoside, luteolin, luteolin-4-O-glucoside, luteolin-7-O-glucoside, glycitein, luteolin-6-C-glucoside, luteolin-8-C-glucoside, apigenin-7-Oglucoside, apigenin-6-C-glucoside, apigenin-8-C-glucoside, quercetin-3-O-rutinoside, quercetin-3-O-ramnoside, quercetin-3-Ogalactoside and tamarixetin were purchased from Extrasynthese (Genay, France). Progoitrin, gluconasturtin and glucoraphanin were supplied by PhytoLab GmbH & Co. (Vestenbergsgreuth, Germany). Glucotropaeolin, glucoerucin and glucoiberin were purchased from Scharlab (Barcelona, Spain). Stock standard solutions of individual compounds (with concentrations between 200 and 300 mg/L) were prepared by precise weighing of the powdered compounds, which were dissolved in 10 mL of HPLC grade methanol or in a mixture of methanol:water (50:50, v/v), and stored at

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20 °C in dark bottles. A multicompound working standard solution at a concentration of 5 mg/L of each compound was prepared by appropriate dilution of the stock solutions with methanol and stored in screw-capped glass tubes at 20 °C. The solutions were prepared every 6 months. Formic acid (purity > 98%), HPLC-grade methanol and dimethylsulfoxide were provided by Sigma (Madrid, Spain). Ultrapure water was obtained from a Milli-Q Gradient water system (Millipore, Bedford, MA, USA). Ammonium acetate was purchased from Panreac (Barcelona, Spain). Millex-GN nylon filters of 0.20-lm were provided by Millipore (Millipore, Carrightwohill, Ireland). Cellulose filters of 11 mm from Whatman (Maidstone, England) and Hydromatrix (Varian, Harbour City, CA, USA) were used for PLE.

2.2. Apparatus and software Chromatographic analysis was carried out using an Agilent series 1290 RRLC instrument (Agilent, Santa Clara, CA, USA) equipped with a binary pump (G4220A), a high-performance autosampler (G4226A), an autosampler thermostat (G1330B) and a column compartment thermostat (G1316C). The system was coupled to an Agilent triple quadrupole mass spectrometer (6460A) with a Jet Stream ESI ion source (G1958-65138). For the chromatographic separation of the extracts, a Zorbax Eclipse Plus C18 column (100 mm  2.1 mm, 1.8 lm particle size) from Agilent was used. Column temperature was set at 30 °C and the injection volume was 5 lL. Chromatographic separation was carried out using a gradient elution with methanol as eluent A, and an aqueous solution of ammonium acetate (0.03 mol/L), adjusted to pH 5 with formic acid, was used as eluent B. The elution started at 5% of eluent A for 1.5 min, and then it was increased to 30% of eluent A in 2.5 min. Subsequently, it was increased to 100% of eluent A over 4 min. These conditions were maintained for 2 min, before being returned to the initial conditions in 30 s. This was maintained for a further 30 s prior to the next analysis, obtaining a total run time of 11 min. The flow rate was set at 0.2 mL/min. Jet Stream Ion Source parameters: drying gas temperature and sheath gas temperature were 325 and 400 °C respectively; drying gas flow and sheath gas flow were 7 and 12 mL/min respectively; nebulizer pressure was 275865 Pa; capillary voltage was set at 4000 and 3500 V in positive and negative acquisitions, respectively. For the optimization of the MS/MS conditions, fragmentor voltage settings, collision energies and the most abundant MS/MS product ions per analyte were determined automatically using Agilent MassHunter Optimizer software, by direct injection of a standard solution of 5 mg/L of each phytochemical at a flow rate of 0.1 mL/min. Solutions were prepared in 1 mL of a mixture of an aqueous solution of ammonium acetate 0.03 mol/L:methanol (50:50, v/v) and injected into the electrospray (ESI) source. ESI in positive and negative ion mode was evaluated. Multiple reaction monitoring (MRM) mode was used. Table 1 shows the transitions selected for each compound. Positive and negative transitions were measured in the same run. During the analysis, a total of 65 transitions (two per compound, except gallic acid) were monitored. The dynamic MRM function allows the transition list to be built based on a retention time window specified for each analyte. Consequently, the phytochemicals are only monitored during the elution window in the analytical run. In addition, the switching time and the dwell time of 7.7 ms for each transition led to an overall cycle time of 500 ms, sufficient to obtain enough data points per peak for the compounds of interest. An Agilent Mass Hunter Quantitative analysis (Agilent Technologies, Inc.) was used for data acquisition and quantification of samples.

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Table 1 MS/MS parameters.

a

Phytochemicals

Family

Code

Ionisation mode

Retention time window (min)

Fragmentor (V)

Quantification transitiona

Confirmation transitiona

Ion ratio (%)

Glucoiberin Glucoraphanin Progoitrin Gallic acid Chlorogenic acid Glucotropaeolin Glucoerucin Caffeic acid Gluconasturtin Ferulic acid Isorhamnetin-3-Orutinoside Luteolin-C-glucoside Sulforaphane Apigenine-C-glucoside Luteolin-O-glucoside Quercetin-3-Orutinoside Quercetin-O-derivate Apigenin-O-derivate Apigenine-7-Oglucoside Kaempferol-3-Oglucoside Quercetin-3-Orhamnoside Kaempferol-3-Orutinoside Tamarixetin Isorhamnetin-3-Oglucoside Daidzein Glycitein Quercetin Kaempferol Luteolin Isorhamnetin Apigenin Genistein Baicalein

Glucosinolates Glucosinolates Glucosinolates Phenolic acids Phenolic acids Glucosinolates Glucosinolates Phenolic acids Glucosinolates Phenolic acids Flavonols

1 2 3 4 5 6 7 8 9 10 11

ESI ESI ESI ESI ESI + ESI ESI ESI ESI ESI ESI

1.30–1.40 1.36–1.45 1.36–1.42 1.54–1.76 4.68–4.90 4.86–5.01 4.98–5.13 5.01–5.38 6.01–6.10 6.23–6.71 7.00–7.30

135 135 115 80 80 140 140 80 170 80 140

422.0 > 96.8(20) 436.1 > 371.9(10) 388.1 > 97.0(15) 169.0 > 125.0(64) 355.1 > 163.0(4) 408.0 > 96.9(15) 420.0 > 96.8(25) 179.0 > 135.1(12) 422.0 > 97.0(20) 193.1 > 134.1(12) 623.2 > 271.0(68)

422 > 357.6(15) 436.1 > 97.0(10) 388.1 > 74.9 (30) – 355.1 > 89.0(64) 408.0 > 74.8 (30) 420.0 > 74.9 (25) 179.0 > 89.0(30) 422.0 > 75.0(25) 193.1 > 178.0(8) 623.2 > 315.0(28)

49 53 61 – 19 44 57 3 41 42 51

Flavones Glucosinolates Flavones Flavones Flavonols

12 13 14 15 16

ESI ESI + ESI ESI ESI

7.14–7.21 7.23–7.31 7.33–7.76 7.49–7.56 7.53–7.90

200 80 200 200 200

447.1 > 327.0(20) 178.0 > 114.0(4) 431.1 > 311.0(16) 447.1 > 285.0(20) 609.1 > 300.0(36)

447.1 > 357.0(20) 178.0 > 72.0(28) 431.1 > 283.0(36) 447.1 > 133.0(20) 609.1 > 271.0(64)

58 61 47 13 45

Flavonols Flavones Flavones

17 18 19

ESI ESI + ESI +

7.59–7.65 7.68–7.76 7.73–7.83

170 140 110

463.1 > 300.0(24) 579.2 > 271.1(20) 433.1 > 271.0.1(12)

463.1 > 271.0(44) 579.2 > 153.1(76) 433 > 153.0(60)

47 11 9

Flavonols

20

ESI

7.75–7.83

170

447.1 > 284.0(24)

447.1 > 255.0(40)

87

Flavonols

21

ESI

7.82–7.88

170

447.1 > 300.4(20)

447.1 > 271.0(44)

60

Flavonols

22

ESI

7.82–7.90

140

593.1 > 255.0(64)

593.1 > 227.1(72)

52

Flavonols Flavonols

23 24

ESI + ESI

7.83–7.97 7.84–7.96

140 140

317.1 > 302.0(20) 477.1 > 314.0(24)

317.1 > 153.0(36) 477.1 > 243.0(48)

75 87

Isoflavones Isoflavones Flavonols Flavonols Flavones Flavonols Flavones Isoflavones Flavones

25 26 27 28 29 30 31 32 33

ESI ESI ESI ESI ESI ESI ESI ESI ESI

8.13–8.21 8.18–8.28 8.23–8.35 8.27–8.38 8.29–8.45 8.59–8.72 8.62–8.75 8.63–8.77 8.72–8.83

170 140 140 170 80 140 140 135 200

255.1 > 137.0(24) 285.0 > 270.1(24) 301.0 > 151.0(12) 287.1 > 153.0(32) 285.0 > 151.1(20) 315.0 > 300.0(12) 269.0 > 117.1(32) 271.1 > 153.0(28) 271.1 > 123.0(32)

255.1 > 152.1(44) 285.0 > 118.1(44) 301.0 > 121.1(24) 287.1 > 121.0(32) 285.0 > 133.1(32) 315.0 > 151.1(24) 269.0 > 149.0(20) 271.1 > 91.0(44) 271.1 > 69.0(72)

78 40 28 57 20 12 21 54 41

+ + +

+ +

Collision energy (eV) is given in brackets.

PLE was performed using an ASE 100 Accelerated Solvent Extraction system (Dionex, Sunnyvale, CA, USA) equipped with 11 mL stainless steel extraction cells. An analytical balance AB204-S from Mettler Toledo (Greifensee, Switzerland) was also used. A Reax-2 rotary agitator from Heidolph (Schwabach, Germany), an ultrasonic from Elma (Singen, Germany), lyophilizer Alpha from Martin Christ (Osterode, Germany) and vacuum pump from Vacuubrand (Wertheim, Germany) were also utilised.

placed in a 10 mL stainless steel extraction cell, and the remaining with Hydromatrix. Extraction was performed using the following experimental conditions: temperature, 100 °C; equilibration time, 10 min; static extraction time, 10 min; pressure, 10,342 MPa; flush volume, 75%; purge time, 60 s and one cycle (Luthria & Natarajan, 2009).

2.3. Extraction procedure

Six samples of tomato, broccoli, carrot, grape and eggplant from the same variety were randomly collected from different supermarkets located in the province of Almeria (southeast of Spain). Samples were chopped and homogenised at room temperature. Samples were kept frozen at 18 °C until lyophilization. Internal quality control was applied in every batch of samples to check the system. This quality control was based on the evaluation of the recovery in one spiked sample with 2.5 mg/kg of target compounds. Moreover, linearity was also checked in the working concentration range (see below).

Samples were homogenised, transferred to a Petri dish, weighed and cooled to 18 °C. Then, all samples were processed according to the following procedure: 150 mg of lyophilized sample were weighed in a 15 mL polypropylene centrifuge tube and 3 mL of methanol:water (80:20, v/v) were added. The mixture was agitated for 30 min with a rotary shaker. After that, the extract was filtered and 100 lL were transferred into a vial containing 900 lL of mobile phase (50:50 v/v of eluent A and B), and 5 lL were injected into the UHPLC system. For the optimization process, tomato was selected as representative matrix, and it was fortified with 5 mg/kg of phytochemicals, using 150 mg of lyophilized sample. Experiments were carried out in triplicate. PLE was also evaluated during the optimization of the extraction procedure. Aliquots (500 mg) of lyophilized sample were

2.4. Samples

2.5. Method validation Several parameters such as linearity, trueness (expressed as recovery), intra-day precision, inter-day precision, limits of detection (LODs) and quantification (LOQs) were studied during the validation of the method.

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Fig. 1. UHPLC–MS/MS chromatograms obtained from an extracted tomato spiked with the target compounds at 0.5 mg/kg.

The identification of the phytochemicals was carried out by searching the appropriate retention time windows (RTWs) defined as the retention time ± three times the standard deviation of the retention time of ten standards at 0.5 mg/L, using the conditions indicated in Table 1 for identification purposes. The confirmation

was performed by the acquisition of two MS/MS transitions and comparing the intensity ratio of both (quantification and confirmation). Matrix effect was studied to ensure bias-free analytical results. Because the samples were not standard reference materials and no

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tomato sample (n = 5) were extracted without spiking (S0) and recovery was calculated as follows: R = 100  (S1 S0)/Cspiked. Precision of the overall method was estimated by performing both repeatability and reproducibility (inter-day precision) studies. Repeatability was evaluated at 1, 2.5 and 5.0 mg/kg from the recovery studies by performing five replicates at each concentration. 3. Results and discussion 3.1. Optimisation of the UHPLC–MS/MS determination

Fig. 2. Effect of (a) type of solvent; (b) solvent volume and (c) extraction time on the recoveries of the selected phytochemicals when a tomato was spiked at 5 mg/ kg. Error bars indicated the standard deviation (n = 3).

blank samples were available, several vegetables and fruit samples (eggplant, broccoli, carrot, grape and tomato), were spiked after extraction with all target compounds at different concentrations (0.05–2 mg/L) and the slopes of the calibration plots were compared with results obtained when standard solutions of the phytochemicals were analysed. Linearity within the working concentration range was also evaluated by spiking an extracted sample with all target compounds at different concentrations (from 0.05 to 2 mg/L). LODs and LOQs were determined as the lowest concentration level that yielded a signal-to-noise (S/N) ratio of 3 and 10 (when the quantification ion was monitored), respectively. Bearing in mind the presence of matrix effect and no ‘‘blank’’ matrices were available, LODs and LOQs were estimated by extrapolation of the S/N of the extract with known amount of analytes and they were expressed as lg/kg in the matrix. Trueness was estimated through recovery studies. Before extraction, different aliquots of tomato (n = 5) were spiked at three levels, 1, 2.5 and 5 mg/kg with the target compounds and extracted using the developed method (S1). Additional aliquots of the same

Chromatographic and MS/MS conditions were optimised to obtain sufficient sensitivity and short analysis time. Protonated molecules, [M+H]+, were observed for all the compounds ionised in positive mode, whereas for the compounds detected in negative mode, deprotonated molecule [M H] , were monitored in the first quadrupole. Two transitions were monitored for all the target compounds, except for gallic acid. For this compound, only one sensitive transition was obtained due to the lack of sensitivity of the observed product ions. Table 1 shows the MS/MS transitions, as well as the fragmentor and collision energies optimised for each compound. Other parameters such as drying gas temperature, sheath gas temperature, drying gas flow, sheath gas flow, nebulizer pressure, capillary voltage and nozzle voltage were studied, selecting the optimum conditions indicated in Section 2.2. The chromatographic conditions were studied to obtain the best peak shape and reduce the analysis time. If acetonitrile was used as organic solvent in the mobile phase, retention time decreased, but the peak shape of the compounds that elute at the beginning of the chromatogram was not adequate. Furthermore, sensitivity was better when methanol was used, and it was selected as organic phase. On the other hand, better peak shapes were obtained when an aqueous solution of ammonium acetate (0.03 mol/L), pH = 5 adjusted with formic acid, was used. Furthermore, better sensitivity was also obtained considering that the selected aqueous phase allows ionisation in positive and negative mode. Other parameters such as flow rate, injection volume and column temperature were optimised in order to get a fast and reliable separation: 0.2 mL/min as optimum flow rate, 30 °C as column temperature and 5 lL as injection volume. All the selected compounds were eluted with high sensitivity and selectivity in less than 11 min, including cleaning and re-equilibration steps. Dynamic MRM acquisition was used, selecting as default variables a peak width of 0.07 min and a delta retention time of 30 s. The resulting number of data points across the peaks was higher than 10 for all compounds, which is enough for quantitation purposes. Retention time ranged from 1.3 min (glucoiberin) to 8.7 min (baicalein) using the chromatographic conditions optimised previously. Finally, it must be emphasised that five pairs of phytochemicals: luteolin-4-O-glucoside + luteolin-7-O-glucoside (luteolin-Oglucoside); luteolin-6-C-glucoside + luteolin-8-C-glucoside (luteolin-C-glucoside); apigenin-6-C-glucoside + apigenin-8-C-glucoside (apigenin-O-derivate); apigenin-7-O-rutinoside + apigenin-7-Oneohesperoside; and quercetin-3-O-glucoside + quercetin-3-Ogalactoside (quercetin-O-derivate) must be determined jointly because they were not chromatographically resolved and they presented the same precursor and product ions. The specific MS/MS parameters for each phytochemical are shown in Table 1. Fig. 1 shows a representative chromatogram obtained from a standard mixture at 0.5 mg/kg of the selected compounds spiked in an extracted tomato. 3.2. Optimization of the extraction procedure The results obtained during the optimization of the extraction procedure are shown in Fig. 2. In Fig. 2a it can be observed that bet-

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Fig. 3. Slope ratios between matrix-matched and solvent calibration. Compliance interval covering the range between 0.8 and 1.2 for tolerable matrix effect has been plotted. Phytochemical codes are indicated in Table 1. (a) Eggplant; (b) broccoli; (c) carrot; (d) grape and (e) tomato.

ter results (higher number of extracted compounds) were obtained when higher percentages of methanol were used in the mixture, allowing the extraction of compounds with different properties. On the other hand, least compounds were recovered when percentages of methanol and water were similar, and intermediate situation was found at higher percentages of water. According to the results, a mixture of methanol:water (80:20 v/v) was used for further experiments. Then, the solvent volume was evaluated, and 2, 3, 4 and 5 mL were studied. It can be observed that most compounds (Fig. 2b) were recovered when 2 or 3 mL were used. However, in order to fully re-constitute lyophilized sample, 3 mL were used for subsequent experiments. The extraction time was also evaluated, studying 10, 20, 30, 40 and 50 min. It can be observed (Fig. 2c) that the recovery of the compounds increased from 10 to 30 min and then decreased or remained constant. Therefore, 30 min was selected as extraction time for further experiments. Whilst the extraction time might be considered prolonged, it must be noted that this approach and the subsequent analysis quantifies a significant number of compounds simultaneously compared with alternatives. The PLE procedure was checked using two different solvent mixtures: methanol:water (80:20, v/v), which had previously been

optimised, and ethanol:dymethylsolfoxide:water (70:5:20, v/v/v) (Luthria & Natarajan, 2009). In this case, better results were obtained when a mixture of methanol:water (80:20) were used. Although PLE and SLE (data not shown) produced similar yields, SLE was selected because it allowed simultaneous extraction of a large number of samples, increasing sample throughput. 3.3. Validation of the proposed method A validation protocol of the optimised procedure was carried out in order to establish the performance characteristics of the method, ensuring the adequate identification, confirmation and quantification of the target compounds. First matrix effect was studied and Fig. 3 shows slope ratios matrix/solvent for each compound in the matrices evaluated. A tolerable signal suppression or enhancement effect was considered if the slope ratio ranged between 0.8 and 1.2. Values lower than 0.8 or higher than 1.2 imply a signal suppression or enhancement, respectively. It can be observed that matrix significantly suppresses or enhances the response for all phytochemicals depending on the type of matrix, except for isorhamnetin and apigenin. For these compounds, no matrix effect was observed in any of the matrices tested. Therefore, in order to compensate this effect, stan-

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Table 2 Validation parameters of the optimised UHPLC–MS/MS method in tomato. Phytochemicals

LOD (lg/kg)

LOQ (lg/kg)

Interday-precisionb

Recovery a

Gallic acid Chlorogenic acid Glucotropaeolin Glucoerucin Caffeic acid Gluconasturtin Ferulic acid Isorhamnetin-3-O-rutinoside Luteolin-C-glucoside Sulforaphane Apigenine-C-glucoside Luteolin-O-glucoside Quercetin-3-O-rutinoside Quercetin-O-derivate Apigenin-O-derivate Apigenine-7-O-glucoside Kaempferol-3-O-glucoside Quercetin-3-O-rhamnoside Kaempferol-3-O-rutinoside Tamarixetin Isorhamnetin-3-O-glucoside Daidzein Glycitein Quercetin Kaempferol Luteolin Isorhamnetin Apigenin Genistein Baicalein a b

25 25 100 100 25 25 25 25 25 5 25 5 25 5 5 5 5 5 25 25 25 5 5 25 25 25 25 5 25 25

50 50 125 125 50 50 50 50 50 25 50 25 50 25 25 25 25 25 50 50 50 25 25 50 50 50 50 25 50 50

a

1.0 mg/kg

2.5 mg/kg

109.1(19) 119.3 (14) 70.2 (23) 71.4 (24) 104.9 (8) 105.3 (10) 94.8 (13) 60.0 (19) 99.6 (12) 101.6 (9) 101.1 (4) 90.3 (12) 113.4 (14) 89.8 (6) 85.9 (10) 90.4 (19) 96.9 (8) 110.7 (12) 99.6 (3) 84.5 (6) 91.2 (21) 97.3 (7) 101.3 (4) 71.0 (13) 74.0 (18) 84.5 (13) 73.9 (16) 77.3 (18) 98.8 (8) 92.5 (17)

68.4 (16) 89.5 (7) 76.5 (19) 80.8 (13) 69.8 (12) 86.7 (2) 82.8 (2) 79.0 (4) 69.0 (8) 68.8 (3) 71.9 (6) 69.7 (10) 78.1 (15) 75.0 (3) 85.0 (7) 77.1 (15) 85.0 (1) 61.4 (10) 88.2 (2) 74.3 (10) 70.9 (8) 92.8 (7) 79.0 (4) 99.2 (6) 80.4 (12) 76.9 (1) 77.9 (5) 76.4 (3) 87.8 (3) 119.5 (14)

5.0 mg/kg

a

96.6 (8) 99.5 (5) 96.1 (13) 89.5 (5) 77.4 (3) 80.8 (1) 72.4 (1) 98.8 (6) 81.5 (3) 77.8 (5) 83.6 (6) 83.7 (12) 89.6 (10) 83.9 (7) 95.2 (11) 93.8 (11) 102.9 (12) 83.5 (13) 104.6 (2) 104.0 (6) 87.8 (8) 96.5 (5) 92.1 (7) 91.2 (5) 87.9 (7) 90.5 (6) 86.3 (4) 88.4 (2) 90.9 (1) 98.5 (8)

13 10 11 9 1 8 9 8 6 4 9 5 6 10 5 14 18 21 1 11 7 11 19 21 22 19 23 22 16 17

Intraday precision (expressed as RSD values) is given in brackets (n = 5). Estimated at 5 mg/kg (n = 5).

Table 3 Validation parameters of the optimised method in different matrices. Phytochemicals

Recovery (%)a Eggplant

Glucoiberin Glucoraphanin Progoitrin Gallic acid Chlorogenic acid Glucotropaeolin Glucoerucin Caffeic acid Gluconasturtin Ferulic acid Isorhamnetin-3-O-rutinoside Luteolin-C-glucoside Sulforaphane Apigenine-C-glucoside Luteolin-O-glucoside Quercetin-3-O-rutinoside Quercetin-O-derivate Apigenin-O-derivate Apigenine-7-O-glucoside Kaempferol-3-O-glucoside Quercetin-3-O-rhamnoside Kaempferol-3-O-rutinoside Tamarixetin Isorhamnetin-3-O-glucoside Daidzein Glycitein Quercetin Kaempferol Luteolin Isorhamnetin Apigenin Genistein Baicalein a

Broccoli

Grape

Carrot

2.5 mg/kg

5.0 mg/kg

2.5 mg/kg

5.0 mg/kg

2.5 mg/kg

5.0 mg/kg

2.5 mg/kg

5.0 mg/kg

115.3 (15)a 70.3 (20) 63 .1 (21) 77.9 (10) 91.5 (17) 60.5 (15) 112.4 (16) 69.9 (6) 70.7 (21) 77.2 (9) 69.1 (11) 77.2 (4) 82.7 (4) 80.5 (11) 82.2 (13) 71.7 (17) 70.9 (15) 78.6 (2) 97.2 (11) 71.5 (22) 72.6 (13) 93.8 (20) 88.4 (24) 75.4 (3) 81.7 (12) 85.3 (6) 73.1 (7) 70.9 (24) 80.0 (16) 78.9 (2) 79.1 (18) 79.1 (12) 96.1 (14)

65.7 (6) 93.9 (11) 70.2 (17) 62.7 (5) 78.0 (3) 71.4(16) 119.6 (7) 97.8 (1) 93.5 (22) 111.2 (8) 110.0 (4) 90.7 (4) 96.7 (1) 79.5 (4) 118.0 (9) 84.6 (10) 118.0 (9) 100.7 (4) 120.8 (2) 78.1 (3) 75.9 (9) 105.1 (10) 115.8 (12) 61.0 (3) 99.8 (7) 100.4 (6) 77.2 (6) 119.2 (12) 74.7 (10) 84.8 (2) 83.8 (10) 102.6 (6) 81.2 (7)

66.0 (14) 62.6 (22) 75.7 (18) 76.8 (24) 70.5 (12) 64.7 (15) 90.2 (2) 71.0 (24) 94.0 (4) 110.0 (17) 60.0 (11) 63.3 (12) 83.1 (4) 63.4 (9) 68.7 (17) 60.3 (5) 85.1 (21) 63.7 (12) 68.3 (20) 80.4 (25) 91.0 (19) 62.2 (25) 70.6 (10) 68.8 (24) 63.4 (13) 69.6 (14) 111.9 (12) 105.0 (14) 60.1(19) 80.2 (5) 64.2 (3) 65.2 (10) 86.6 (8)

78 (10) 81.2 (11) 82.9 (13) 115 (15) 120.6 (4) 94.3 (11) 81.9 (1) 98.2 (12) 83.1 (5) 60 (7) 91.0(8) 62.2 (8) 68.4 (4) 64.3 (1) 62.6 (7) 100.4 (3) 84.8 (10) 77.5 (11) 77.8 (10) 71.5 (12) 70.6 (14) 63.2 (6) 104.9 (4) 67.9 (15) 69.5 (12) 78.2 (8) 76.1 (9) 73.8 (4) 64.8 (12) 73.1 (1) 75.0 (4) 67.5 (8) 80.7 (11)

72.8(5) 96.7 (20) 110.0 (24) 97.0 (25) 105.6 (4) 82.8 (7) 112.9 (6) 109.3 (3) 104.3 (7) 100.0 (11) 78.0 (15) 103.0 (4) 94.6 (3) 99.6 (4) 102.4 (6) 104.5 (24) 93.3 (6) 91.7 (5) 90.1 (10) 117.0 (9) 100.7 (7) 132.0 (7) 79.8 (19) 91.8 (19) 91.8 (7) 93.7 (6) 86.8 (14) 74.7 (12) 88.7 (6) 95.3 (2) 98.6 (8) 86.4 (18) 62.2 (12)

100.3 (6) 79.1 (15) 91.8 (4) 80.9 (8) 77.8 (1) 86.1 (2) 103.7 (2) 79.8 (1) 69.7 (1) 73.9 (4) 76.1 (10) 79.4 (2) 78.9 (3) 79.6 (1) 79.2 (6) 67.0 (15) 90.5 (5) 79.1 (2) 72.5 (7) 80.6 (7) 95.1 (5) 82.5 (4) 71.9(11) 85 (16) 73.3 (2) 82.1 (1) 64.9 (10) 76.9 (3) 73.3 (1) 76.9 (1) 73.8 (3) 83.9 (10) 87.8 (6)

75.3 (13) 112.8 (17) 77.3 (10) 88.7 (10) 68.1 (22) 93.1 (5) 105.7 (9) 90.1 (6) 98.4 (11) 80.7 (4) 75.9 (5) 90.8 (2) 86.9 (2) 89.3 (3) 93.1 (8) 79.4 (12) 71.8 (25) 85.0 (5) 97.3 (16) 71.8 (29) 119.2 (16) 97.9 (8) 117.0 (25) 94.2 (7) 87.5 (6) 88.4 (4) 118.2 (3) 73.1 (20) 104.9 (1) 98.7 (2) 97.9 (9) 97.7 (8) 99.6 (16)

111.3 (7) 86.6 (9) 84.3 (11) 76.0 (3) 91.0 (16) 105.2 (7) 107.8 (5) 77.9 (9) 82.1 (7) 100.5 (4) 85.7 (3) 88.2 (1) 81.8 (2) 86.7 (1) 83.6 (4) 120.3 (14) 83.5 (23) 79.8 (7) 81.0 (10) 81.4 (25) 99.1 (4) 107.4 (2) 61.4 (5) 82.8 (6) 81.9 (5) 87.3 (1) 101.6 (2) 82.9 (4) 93.4 (1) 91.3 (1) 91.3 (5) 89.5 (4) 94.1 (8)

Intraday precision (expressed as RSD values) is given in brackets (n = 3).

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dard addition calibration was used for quantification purposes. Although this could increase the overall analysis time, considering the short running time, the method is still suitable to routine analysis. Linearity was also evaluated and in all the cases, determination coefficient was higher than 0.98, and deviation of the individual points from the calibration curve was always lower than 20%. LODs were ranged from 5 to 100 lg/kg whereas LOQs ranged from 25 to 125 lg/kg (Table 2). These were low enough for quantification of the compounds in samples. Trueness results are shown in Table 2 and it can be observed that recoveries ranged from 60 (isorhamnetin-3-O-rutinoside) to 119% (chlorogenic acid) for the selected compounds at 1 mg/kg, from 61 (quercetin-3-O-rhamnoside) to 119% (baicalein) at 2.5 mg/kg, and from 72 (ferulic acid) to 105% (kaempferol-3-Orutinoside) at 5 mg/kg. Moreover, it can be noted that repeatability values (expressed as relative standard deviation, RSD) were always lower than 19%. Inter-day precision was evaluated at 5 mg/kg in five different days (see Table 2), obtaining values lower than 23%. In order to check the suitability of the proposed method in other matrices (broccoli, carrot, eggplant and grape) the recovery studies were carried out at two concentration levels (2.5 and 5.0 mg/kg). Table 3 shows the results obtained, and it can be observed that recoveries ranged from 60% to 120% for both concentration levels, which were similar to those obtained in tomato. These results indicate that this method can be applied to different vegetable matrices. Finally, although glucoiberin, glucoraphanin and progoitrin recovery in spiked tomato samples was outside the range (60–

120%), the method is nevertheless suitable for the extraction of phytochemicals in several matrices. These compounds do not occur naturally in tomato, and recoveries in vegetables where they are usually present (e.g., broccoli) were within acceptable limits.

3.4. Application to real samples Once the analytical method was validated, it was applied to evaluate the concentration of phytochemicals in tomato, broccoli, carrot, grape and eggplant. The results are shown in Table 4 and they were expressed as mg/kg dry weight (DW), because the samples were lyophilized. Furthermore, Fig. 4 shows the chromatograms of different matrices, containing chlorogenic acid at 648 mg/kg in eggplant samples, glucoraphanin at 3059 mg/kg in broccoli samples, luteolin at 5 mg/ kg in carrot samples, quercetin-O-derivate at 143 mg/kg in grape samples and quercetin-3-O-rutinoside at 100 mg/kg in tomato samples. The predominant phytochemicals of tomato were quercetin-3O-rutinoside and chlorogenic acid. Other phytochemicals found in cherry tomato were baicalein, apigenin, ferulic acid, kaempferol-7-O-rutinoside, caffeic acid, kaempferol, luteolin, quercetin, isorhmanetin, luteolin-O-glucoside and quercetin-O-derivate. The total amount of phytochemicals determined in the analysed tomato samples was 376 mg/kg DW. Glucosinolates, isoflavones, apigenin-C-glucoside, apigenin-7O-glucoside, tamarixetin, apigenin-O-derivate, isorhamnetin-3-Oglucoside, isorhamnetin-3-O-rutinoside, quercetin-3-O-ramnoside,

Table 4 Average value of phytochemicals detected (mg/kg dry weight) in different fruit or vegetables of the same variety.

Glucoiberin Glucoraphanin Progoitrin Gallic acid Chlorogenic acid Glucotropaeolin Glucoerucin Caffeic acid Gluconasturtin Ferulic acid Isorhamnetin-3-O-rutinoside Luteolin-C-glucoside Sulforaphane Apigenine-C-glucoside Luteolin-O-glucoside Quercetin-3-O-rutinoside Quercetin-O-derivate Apigenin-O-derivate Apigenine-7-O-glucoside Kaempferol-3-O-glucoside Quercetin-3-O-rhamnoside Kaempferol-3-O-rutinoside Tamarixetin Isorhamnetin-3-O-glucoside Daidzein Glycitein Quercetin Kaempferol Luteolin Isorhamnetin Apigenin Genistein Baicalein Total concentration (mg/kg)c a b c

Tomato

Broccoli

Carrot

Grape

Eggplant

nda nd nd nd 105 (17) nd nd 11 (8) nd 24 (2) nd 7 (22) nd nd 10 (17) 106 (5) 0.1 (2) nd nd nd nd 13 (10) nd nd nd nd 10 (3) 11 (15) 11 (4) 10 (20) 28 (16) nd 29 (16) 376

630 (36)b 3738 (27) ndb nd 36 (10) nd 33 (16) nd 33 (5) 2 (1) nd 2 (1) 13 (3) nd 1 (1) nd nd nd nd nd nd nd nd nd nd nd 2 (1) 4 (1) 6 (2) 6 (1) 3 (1) nd 4(3) 4513

nd nd nd nd 338 (156) nd nd 1 (1) nd 1 (1) nd 2 (1) nd nd 3 (2) nd nd nd nd nd nd nd nd nd nd nd 4 (1) nd 6 (2) 5 (1) 2 (1) nd 3 (3) 415

nd nd nd 1 (1) nd nd nd nd nd nd nd 4 (4) nd nd 2 (1) 10 (4) 165 (128) nd nd 15 (14) 3 (1) 4 (3) 16 (7) 8 (5) nd 3 (2) 6 (3) nd 9 (2) 9 (2) 3 (1) nd 4 (4) 263

nd nd nd nd 730 (848) nd nd 3 (2) nd 1 (1) nd 3 (1) nd nd 3 (1) nd nd nd nd 6 (1) nd 13 (10) 6 (2) nd nd nd 5 (3) 7 (2) 6 (1) 5 (1) 3 (1) nd 5 (5) 797

nd: Not detected. Intra-variety precision (expressed as SD) values is given in brackets (n = 6). Obtained from the average values.

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Fig. 4. UHPLC–MS/MS MRM chromatogram for different matrices containing: (a) Chlorogenic acid at 648 mg/kg in eggplant samples; (b) Glucoraphanin at 3059 mg/kg in broccoli samples; (c) Luteolin at 5 mg/kg in carrot samples; (d) Quercetin-O-derivate at 143 mg/kg in grape samples and (e) Quercetin-3-O-rutinoside at 100 mg/kg in tomato samples.

gallic acid and kaempferol-3-O-glucoside were not detected in any tomato at concentrations higher than the established LOQ. Similar results were obtained in previous studies (Slimestad & Verheul, 2005; Stewart et al., 2000), where it was observed that the content of chlorogenic acid ranged from 102 to 780 mg/kg DW, ferulic acid ranged from 18 to 58 mg/kg DW, quercetin-3-Orutinoside ranged from 72 to 184 mg/kg DW, kaempferol ranged from 4 to 11 mg/kg DW, quercetin ranged from not detected to 14 mg/kg DW, which are similar to the values obtained in this work. In general, it can be observed that the concentration range of these compounds are wide because the content in matrices depends on several factors such as variety, crop type, environmental conditions, location, germination, maturity, processing and storage. The predominant glucosinolates of broccoli were glucoraphanin and glucoiberin followed by glucoerucin, as indicated by Tian, Rosselot, and Schwartz (2005). The major phenolic acids determined in our broccoli samples was chlorogenic acid, but there are no papers describing chlorogenic acid in broccoli. Phytochemicals detected at lower concentrations were quercetin, ferulic acid, luteolin-C-glucoside and luteolin-O-glucoside (see Table 4). Chlorogenic acid was found to be the predominant phenolic acid in carrot, and luteolin and isorhmanetin, followed by quercetin (see Table 4) the predominant flavonoids, as shown by previous studies (Mazzeo et al., 2011). In the case of chlorogenic acid, carrots showed the highest variability, as observed previously (Mattila

& Kumpulainen, 2002; Mazzeo et al., 2011; Sun, Simon, & Tanumihardjo, 2009). In relation to grape, it was observed that some authors such as Anastasiadi, Pratsinis, Kletsas, Skaltsounis, and Haroutounian (2010) found that the major flavonoids were quercetin-3-O-glucoside and quercetin-3-O-galactoside, which is in agreement with our data (see Table 4). Furthermore, Ferrandino and Guidoni (2010) indicated that anthocyanins and phenolic acids are also important compounds in grapes. Finally, the predominant phenolic acid of eggplant was chlorogenic acid, representing from 70% to 95% of total phytochemicals in fresh eggplant (see Table 4) as showed by Concellón, Zaro, Chaves, and Vicente (2012), who found that chlorogenic acid was the major phenolic acid in different eggplant tissues. Furthermore, a high variability of chlorogenic acid content was also observed, although higher than in other works (Mishra, Gautam, & Sharma, 2012; Singh et al., 2009), where the variability is nearly 25%. Finally it is important to highlight that the highest concentration of phytochemical in fruit and vegetables studied was found in broccoli, corresponding to 4513 mg/kg DW. 4. Conclusion This work presents a suitable method for the extraction, detection and quantification of phytochemicals by UHPLC–MS/MS. Extraction method and UHPLC–MS/MS determination for multi-

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class determination was validated in different matrices obtaining good results. Furthermore the chromatographic method was fast (running time 11 min). The reliability of the method was performed by checking its suitability in different matrices such as tomato, carrot, eggplant, broccoli and grape. Adequate performance characteristics were obtained in all the matrices evaluated. Moreover, the content of phytochemicals in different matrices was compared; glucosinolates were detected at higher concentrations in broccoli and chlorogenic acid in eggplant, tomato and carrot. Considering running time, and the applicability of the method (extraction of a wide range of phytochemicals from several matrices), the proposed method could be applied in routine analysis. Acknowledgements The authors are grateful to Andalusian Regional Government (Regional Ministry of Innovation, Science and Enterprise) and FEDER (Project Ref. P11-AGR-7034), as well as the Centre for Industrial Technological Development (CDTI) and FEDER for financial support (Project Ref. IDI-20110017). M.I.A.F. acknowledges her Grant (FPU, Ref. AP 2009-2074) from the Spanish Ministry of Education. R.R.G. is also grateful for personal funding through Ramon y Cajal Program (Spanish Ministry of Economy and Competitiveness-European Social Fund). References Anastasiadi, M., Pratsinis, H., Kletsas, D., Skaltsounis, A. L., & Haroutounian, S. A. (2010). Bioactive non-coloured polyphenols content of grapes, wines and vinification by-products: Evaluation of the antioxidant activities of their extracts. Food Research International, 43, 805–813. Björkman, M., Klingen, I., Birch, A. N. E., Bones, A. M., Bruce, T. J. A., Johansen, T. J., et al. (2011). Phytochemicals of Brassicaceae in plant protection and human health – Influences of climate, environment and agronomic practice. Phytochemistry, 72, 538–556. Carbone, K., Giannini, B., Picchi, V., Lo Scalzo, R., & Cecchini, F. (2011). Phenolic composition and free radical scavenging activity of different apple varieties in relation to the cultivar, tissue type and storage. Food Chemistry, 127, 493–500. Chen, H., Li, X., Chen, J., Guo, S., & Cai, B. (2010). Simultaneous determination of eleven bioactive compounds in Saururus chinensis from different harvesting seasons by HPLC-DAD. Journal of Pharmaceutical and Biomedical Analysis, 51, 1142–1146. Concellón, A., Zaro, M. J., Chaves, A. R., & Vicente, A. R. (2012). Changes in quality and phenolic antioxidants in dark purple American eggplant (Solanum melongena L. cv. Lucía) as affected by storage at 0 and 10 °C. Postharvest Biology and Technology, 66, 35–41. El-Hela, A. A., Al-Amier, H. A., & Ibrahim, T. A. (2010). Comparative study of the flavonoids of some Verbena species cultivated in Egypt by using highperformance liquid chromatography coupled with ultraviolet spectroscopy and atmospheric pressure chemical ionization mass spectrometry. Journal of Chromatography A, 1217, 6388–6393. Ferrandino, A., & Guidoni, S. (2010). Anthocyanins, flavonols and hydroxycinnamates: An attempt to use them to discriminate Vitis vinifera L. cv ‘‘Barbera’’ clones. European Food Research Technology, 230, 417–427. Gómez-Romero, M., Segura-Carretero, A., & Fernández-Gutiérrez, A. (2010). Metabolite profiling and quantification of phenolic compounds in methanol extracts of tomato fruit. Phytochemistry, 71, 1848–1864. Gratacós-Cubarsí, M., Ribas-Agustí, A., García-Regueiro, J. A., & Castellari, M. (2010). Simultaneous evaluation of intact glucosinolates and phenolic compounds by UPLC–DAD–MS/MS in Brassica oleracea L. var. botrytis. Food Chemistry, 121, 257–263. He, D., Shan, Y., Wu, Y., Liu, G., Chen, B., & Yao, S. (2011). Simultaneous determination of flavanones, hydroxycinnamic acids and alkaloids in citrus fruits by HPLC–DAD–ESI/MS. Food Chemistry, 127, 880–885. Helmja, K., Vaher, M., Püssa, T., Raudsepp, P., & Kaljurand, M. (2008). Evaluation of antioxidative capability of the tomato (Solanum lycopersicum) skin constituents by capillary electrophoresis and high-performance liquid chromatography. Electrophoresis, 29, 3980–3988. Hollmanm, P. C. H., & Katan, M. B. (1999). Dietary flavonoids: Intake, health effects and bioavailability. Food and Chemical Toxicology, 37, 937–942.

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