Food Control xxx (2016) 1e6
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Comparison of secondary metabolite changes in Camellia sinensis leaves depending on the growth stage Hyung Won Ryu a, 1, Heung Joo Yuk a, 1, Ju Hyeon An a, Doo-Young Kim a, Hyuk-Hwan Song b, **, Sei-Ryang Oh a, * a b
Natural Medicine Research Center, KRIBB, 30-Yeongudanji-ro, Ochang-eup, Cheongwon-gu, Cheongju-si, 363-883, Republic of Korea Agency for Korea National Food Cluster (AnFC), Iksan, 570-749, Republic of Korea
a r t i c l e i n f o
a b s t r a c t
Article history: Received 21 March 2016 Received in revised form 13 September 2016 Accepted 1 October 2016 Available online xxx
To improve tea leaves' utility as a functional crop, it is necessary to understand the distribution and alteration of bioactive secondary metabolites during different growth stages. In the present study, secondary metabolites changes in C. sinensis leaves were investigated according to growth stage using ultraperformance liquid chromatographyequadrupole time-of-flight mass spectrometry (UPLCeQTof MS) with multivariate analysis. Through principal component analysis of the metabolites, patterns could be distinguished from samples harvested between April and August. On the loading plot, significant changes in the contents of metabolites were found during growth, and two gallotannins (4 and 10), three flavan3-ols (6, 7, and 17), two flavonols (14 and 16), and two theaflavins (29 and 31) were evaluated as growth markers among thirty-one isolated metabolites. In particular, the flavonols and theaflavins increased gradually, whereas gallotannins and flavan-3-ols decreased continuously depending on the growth stage. A possible pathway could be deduced using metabolomics analysis, and information regarding physiological characterization and optimal harvesting time was obtained. © 2016 Elsevier Ltd. All rights reserved.
Keywords: Camellia sinensis Secondary metabolite Growth stage UPLCeQTof MS
1. Introduction Camellia sinensis, a commonly known tea plant, belongs to the genus Camellia of flowering plants from the Theaceae family. Many C. sinensis cultivars originate from China, Japan, Korea, and Southeast Asia and are cultivated in various regions worldwide (Mondal, 2011). Tea from C. sinensis leaves has been used in health foods, dietary supplements, and cosmetic products (Su, Shih, & Lin, 2014). Additionally, green tea and its products are commercially used as beverage creams, yogurts, desserts, shampoos, and cosmetics. The health benefits of such products are primarily associated with the secondary metabolites in tea (Hodgson et al., 2014). The metabolites also play an important role in the quality of green tea. By extension, the quality of C. sinensis leaves is attributed to physiological changes in environmental conditions during growth, which depend on cultivar conditions (Chaturvedula & Prakash, 2011).
* Corresponding author. ** Corresponding author. E-mail addresses:
[email protected] (H.-H. Song),
[email protected] (S.-R. Oh). 1 These authors contributed equally to this work.
Korean green tea is harvested between April and July. Specifically, the leaves of C. sinensis and their processed green tea products were categorized according to growth stage (Lee, Kim et al., 2014). Previous reports have revealed the complexity of metabolite content and composition of C. sinensis leaves (Lee et al., 2013; Scoparo, de Souza, Dartora, Sassaki, Gorin, & Iacomini, 2012). However, metabolomic analysis has yet to reveal phytochemical changes during the aforementioned growth stages, despite the importance of the time period during which Korean green tea leaves are harvested. Metabolomics techniques, along with statistical and multivariate data analyses, play an important role in many aspects of natural product and metabolite chemistry, including biomarker screening, biological activity, toxicity, chemotaxonomy, environmental metabolism, and nutritional quality control during processing and cultivation (Rodgers et al., 2012; Wu, Du, Gubbens, Choi, & van Wezel, 2015). UPLCeQTof MS-based metabolomic is useful for the rapid and highly sensitive detection of secondary metabolites from plant biosynthesis pathways (Maruyama et al., 2009). Recently, metabolomic profiles from tomatoes (Moco et al., 2006), blueberries (Lee, Jung et al., 2014; Lee, kim et al.,2014), pitayas (Suh et al., 2014), hop (Farag, Porzel, Schmidt, & Wessjohann, 2012),
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Please cite this article in press as: Ryu, H. W., et al., Comparison of secondary metabolite changes in Camellia sinensis leaves depending on the growth stage, Food Control (2016), http://dx.doi.org/10.1016/j.foodcont.2016.10.017
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soybean leaves (Song, Ryu et al., 2014), Aloe vera (Lee et al., 2012), and C. sinensis (Yang et al., 2012) have been elucidated using UPLCeQTof MS. However, the effects of growth stage on metabolomic profiles have not been evaluated to date. Furthermore, metabolomic studies provide a new approach for monitoring changes in nutraceutical quality during tea plant growth and cultivation. Therefore, in this study, metabolite profiling of C. sinensis leaves cultivated during different growth stages (April, April to early May, mid-May to late May, early June, and after July) was conducted using UPLCQTof MS, and multivariate analysis was used to characterize metabolites to elucidate nutraceutical status and identify major secondary metabolites from C. sinensis leaves. 2. Materials and methods 2.1. Plant materials and sample preparation C. sinensis leaves sample were cultivated at Hwagae in Hadong, South Korea (N 35 130 43.9100, E 127 380 34.2600 ). C. sinensis leaves were handpicked from ten separate plots for 5 months from April 18 to August 25, 2014. The leaf samples were freeze-dried for 7 days and stored at below 20 C before extraction. Secondary metabolites extraction was performed according to a slightly modified version of the procedure described by (Song, Moon et al., 2014; Song, Ryu et al.,2014). Prior to metabolomic analysis, leaves were freeze dried and cut into small pieces with a laboratory blade cutter. The powdered samples (200 mg) were extracted with 4 mL of 80% methanol using an SD 300H sonicator (SD-ultra, Seoul, Korea) at 40 KHz for 15 min and centrifuged at 4 C and 3000 rpm for 10 min (Centrifuge 5430R, Eppendorf, Hamburg, Germany). All the extraction and chromatographic solvents were LC-MS grade for the MS experiments (J. T. Baker, Phillipsburg, NJ, USA). The supernatant was filtered through a 0.2 mm polytetrafluoroethylene (PTFE) filter. The supernatant was subjected to UPLC-QTof MS analysis. 2.2. UPLC-QTof MS analysis A Waters ACQUITY UPLC system (Waters Corporation, Milford, MA, USA) equipped with a binary solvent delivery system, autosampler, and UV detector was combined with a Micromass Q-Tof Premier™ mass spectrometer (Waters Corporation, Milford, MA, USA) system. Aliquots (2 mL) of each sample were then injected into an ACQUITY HSS T3 chromatography column (2.1 100 mm i.d., 1.8 mm particle size) at a flow rate of 0.4 mL/min. Gradient elution was carried out with water/acetonitrile containing 0.1% formic acid. The linear gradient elution program was as follows: 0 min, 5% B; 0e10 min, 5e20% B; 10e18 min, 20e32% B; 18e21 min, 32e99% B; 21e23 min, 99% B, 23.3e25 min, back to 5% B. The total run time, including re-equilibration of the column to the initial conditions, was 25 min. The mass spectrometer was operated in negative ion modes in the following conditions: source temperature 100 C, desolvation temperature 350 C, capillary voltage 2.3 kV, cone voltage 50 V. Leucine-enkephalin (400 pg/mL) was used as the reference lock mass [m/z 554.2615 ()] at a flow rate of 2 mL/min. Accurate mass and elemental composition were calculated using the MassLynx software (Waters Corp.) incorporated in the instrument. MS/MS analysis was also performed under the same conditions used for metabolite scanning. 2.3. Statistical and multivariate data analysis Data preprocessing for the UPLC-QTof MS analysis was performed using MassLynx software (version 4.1, Waters, Manchester, UK). After conversion, the MS data were processed using MarkLynx to obtain a data matrix containing retention times, accurate masses,
and normalized peak intensities. The parameters included a retention time (tR, range of 2.0e17.0 min), mass-to-charge ratio (m/ z, range of 100e1500 Da), and MS tolerance of 0.04 Da. The resulting data were imported into SIMCAePþ software 12.0 (Umetrics, Umeå, Sweden) for multivariate statistical analysis. Principal component analysis (PCA) was used to compare the growth period (April to August) and to identify the major metabolites. The univariate statistics for multiple classes were obtained through the breakdown and one-way ANOVA using STATISTICA (version 8.0, StatSoft Inc., Tulsa, OK). After multivariate statistical analysis, major markers were characterized using mass data and fragmentation patterns (MS and MS/MS) from the QTof MS instrument. Marker compounds were tentatively identified by comparison to be published MS results using SciFinder Scholar and online databases such as PubChem, ChemSpider, MassBank, NIST Chemistry WebBook, METLIN, ReSpect, and an in-house library. 3. Results and discussion 3.1. Multivariate analysis at different growth stages The leaves of C. sinensis during reproductive growth and harvest can be divided into five stages according to the quality of green tea (Supplementary material). It is well known that green tea products differ in terms of metabolite composition, but polyphenolic profiles of tea plants according to growth stage have not been studied extensively. Growth period-specific metabolite profiling of C. sinensis was performed using UPLC-QTof MS combined with multivariate statistical analysis. Differences among the samples were not readily apparent upon visual examination of the chromatograms. However, PCA revealed a clear separation of the clusters upon application of multivariate analysis. The unsupervised PCA base in the negative mode data clearly differentiated 5 to 10 samples depending on growth stage into three clusters, April, MayeJune, and JulyeAugust (Fig. 1A). R2X (cumulative) and Q2 (cumulative) values of the PCA as model validation parameters are 0.897 and 0.784, respectively. As shown Fig. 1A, Principal components 1 (PC 1, 58.2%) and 2 (PC 2, 13.7%) accounted for 71.9% of the variation in the entire dataset. Seasonal samples between April and May to August were clearly separated by PC 1, while MayeJune and JulyeAugust samples were readily discriminated by PC 2. In addition, the corresponding PCA loading plot enabled the detection of several markers responsible for group separation (Fig. 1B). Marker ions at m/z 663.0732 ([MeH], 2.97 min; 4), 289.0714 ([MeH], 4.38 min; 6), 457.0780 ([MeH], 4.58 min; 7), 635.0877 ([MeH], 5.78 min; 10), 771.1992 ([MeH], 6.84 min; 14), 771.1990 ([MeH], 7.29 min; 16), 441.0822 ([MeH], 7.79 min; 17), 715.1284 ([MeH], 15.50 min; 29), and 867.1409 ([MeH], 16.05 min; 31) were far from the loading plot center, suggesting that these compounds might be significant markers for C. sinensis leaf growth. To examine the relationship between nine metabolite markers during various developmental stages of C. sinensis leave growth, a heat map of the accessions was performed (Fig. 2A). The green box indicates that a metabolite was present at greater than the mean level for each marker, and the red box means the metabolite was present at a lower level. Distinct aspects were found in 9 metabolites, which revealed significant correlations during growth (Fig. 2B). A schematic diagram of the biochemical pathway of thirty-one major metabolites in C. sinensis at various developmental stages is presented in Fig. 3. All the disconnected markers are deleted (grey characters) and the metabolites with significant correlations are illustrated together. Colored characters (red: decrease, green: increase, and black: equal or similar) represent the quantitative changes of metabolites during various growth stages. The biosynthesis-related markers were generated using general
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Fig. 1. PCA score plot (A) and loading plot (B) of C. sinensis leaf samples at different growth stages (4, 633.0732 and 2.97 min; 6, 289.0714 and 4.38 min; 7, 457.0780 and 4.58 min; 10, 635.0877 and 5.78 min; 14, 771.1992 and 6.84 min; 16, 771.1990 and 7.29 min; 17, 441.0822 and 7.79 min; 29, 715.1284 and 15.50 min; 31, 867.1409 and 16.05 min).
biochemical pathways presented in the Kyoto Encyclopedia of Genes and Genomes (KEGG) database (http://www.genome.jp/ kegg/pathway.html), by comparison with previously published data (Liu et al., 2015; Punyasiria et al., 2004). Gallotannins, flavan-3ols, flavonols, and theaflavins have a common synthetic pathway from phenylalanine (aromatic amino acid) to coumaroyl-CoA, and these pathways branch after coumaroyl-CoA (Crozier, Jaganath, & Clifford, 2009). Naringenin is converted to chalcone synthase mediates, and this reaction pathway feeds into the flavonol, and theaflavin synthetic pathways (Hanhineva et al., 2009). The relative amount of metabolites in C. sinensis leaves was significantly different as shown Fig. 2B. The flavonols (14 and 16) and theaflavins (29 and 31) content increased when the size and morphology of C. sinensis leaves changed (first buds to old leaves), while the concentration of gallotannins (4, 7, and 10) and flavan-3ols (6, and 17) decreased with leaf growth. Specifically, C. sinensis possesses a biosynthetic function on the 3-position of the flavonoid C-ring, resulting in the production of aglycones. These results correlated with the expression of glycosyltransferases during growth, and the development of plant cell walls (Taylor, Strenge, & Miller, 1998). Also, the most important catechin oxidation products in tea leaf are theaflavin and its mono- and di-gallates. Theaflavins possess a characteristic benzotropolone moiety, which is produced by condensation between the catechol-type Bring of epicatechin and the pyrogallol-type B-ring of epigallocatechin (Haslam, 1998). Although all reactions leading to the biosynthesis pathway of basic theaflavins have not been determined, theaflavin, theaflavin-3-O-gallate, theaflavin-30 -O-gallate, and theaflavin-3,3'-di-O-gallate are derived from epicatechin, epicatechin-3-O-gallate, epigallocatechin, and epigallocatechin-3-Ogallate, respectively (Takino, Imagawa, Horikawa, & Tanaka, 1964). Two markers (4 and 10) within C. sinensis leaves were dramatically reduced during plant development. This result indicates that gallotannins, corresponding to markers (4 and 10), play a significant role in physiological changes in the plant. A typical plant cell does not produce gallotannins simultaneously, and the efficient production of gallotannins has direct negative effects on flavonoid biosynthesis (Ossipov, Salminen, Ossipova, Haukioja, & Pihlaja, 2003; Salminen, Ossipov, Haukioja, & Pihlaja, 2001). For this reason, gallotannins do not reach high concentrations in the same tissue; this can be deduced from the branches shown in the tannin
biosynthetic pathways. It also suggests that further study of C. sinensis leaves according to growth is required to understand the changes in metabolites. 3.2. Tentative identification of metabolites in C. sinensis leaves The retention times, UV/vis spectra, and MS data (accurate mass and fragmentation pattern in negative mode) of the metabolites were compared to metabolite data previously reported for C. sinensis (Lee et al., 2013; Scoparo et al., 2012; Yang et al., 2012). Table 1 provides detailed information on the individual flavonoids and phenolic components of C. sinensis leaves. Accordingly, 31 polyphenolic metabolites could be identified by carefully interpreting the mass spectra including the experimental m/z values, molecular formula, MS/MS (atomic mass unit: amu) analysis, and experimental error (ppm) in Table 1. In this study, we isolated or tentatively identified four gallotannins (peaks 1, 4, 10, and 12), seven proanthocyanidin (peaks 2, 5, 8, 9, 15, 25, and 26), five flavan3-ols (peaks 3, 6, 7, 17, and 24), ten flavonols (peaks 11, 13, 14, 16, 18e23), and five theaflavin derivatives (peaks 27e31) (Supplementary material). Among thirty-one isolated metabolites, details for identification of nine growth markers were as follow. Markers 4 and 10 were determined to be gallotannins. Peak 4 (tR 2.97 min) had a [MeH] at m/z 633 and fragment ions at m/z 463 (due to loss of 170 amu for gallic acid) and 301 (loss of 170 amu for gallic acid þ glucosyl residue of 162 amu). On the basis of these fragments and the difference in experimental error in relation to molecular formula, this compound was tentatively identified as strictinin. Trigalloyl-glucose (peak 10, tR 5.78 min, [MeH] at m/z 635, fragment ions at m/z 169 for gallic acid, 331 for galloyl-glucose, and 483 for digalloyl-glucose) was previously reported in green teas (Scoparo et al., 2012). Markers 6, 7, and 17 were determined to be catechins belonging to the flavan-3-ol class of flavonoids, major components in green tea that are usually detected as gallate ester derivatives (Dou, Lee, Tzen, & Lee, 2007). Peaks 6 (tR 4.38 min) was identified as epicatechin by comparison of MS, UV, and retention time data with authentic compound (diagnostic ESI-MS fragments at m/z 245 and 205) (Lin, Chen, & Harnly, 2008; Scoparo et al., 2012). Peak 7 (tR 4.58 min, [MeH] at m/z 457, fragment ions at m/z 169 for gallic acid, 305 for gallocatechin) and peak 17 (tR 7.79 min, [MeH] at m/z 441, fragment ions at m/z 169 for gallic
Please cite this article in press as: Ryu, H. W., et al., Comparison of secondary metabolite changes in Camellia sinensis leaves depending on the growth stage, Food Control (2016), http://dx.doi.org/10.1016/j.foodcont.2016.10.017
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Fig. 2. Heat map (A) and relative intensity (B) of secondary metabolites that were significantly different during C. sinensis leaf growth (4, strictinin; 6, epicatechin; 7, gallocatechin gallate; 10, trigalloyl-glucose; 14, quercetin-3-O-galactosylrutinoside; 16, quercetin-3-O-glucosylrutinoside; 17, epicatechin gallate; 29, theaflavin-3-gallate; 31, theaflavin-3-3'gallate).
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Fig. 3. Changes of in the marker metabolites during leaf growth stages in a metabolic pathway. The level of significance was set at P < 0.05. The metabolites with grey characters were undetectable. Metabolites in bold (red: decrease, green: increase, and black: equal or similar) were quantified in the present experiment. (For interpretation of the references to colour in this figure legend, the reader is referred to the web version of this article.)
acid, 289 for catechin) were in accordance with previous findings, where they were identified as gallocatechin gallate and catechin gallate, respectively (Dou et al., 2007). Markers 14 and 16 were determined to be flavonols. The parent aglycon of flavonol was quercetin, which was conjugated to a range of sugars, including glucose, galactose, and rutinose (loss of 162 amu for hexose þ 146
amu for rhamnose moieties) as trisaccharides (Dou et al., 2007). Peaks 14 (tR 6.84 min) and 16 (tR 7.29 min) had the same [MeH] at m/z 771 and fragment ions at m/z 609 and 301 corresponding to the loss of a 162 and 470 amu. The loss of 162 and 470 amu was due to the cleavage of a hexosyl and hexosyl-rutinose moiety, respectively. The fragment ion at m/z 301 corresponded to quercetin backbone.
Table 1 Spectral characteristics of the metabolites detected by UPLC-QTof MS in a tea leaf sample. Peak no.
RT (min)
lmax (nm)
[M-H] (m/z)
Fragment ions (m/z)
Molecular formula
Error (ppm)
Identification
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
2.25 2.27 2.88 2.97 3.29 4.38 4.58 4.76 5.70 5.78 6.15 6.25 6.35 6.84 7.09 7.29 7.79 8.02 8.40 8.84 9.46 9.68 10.17 10.28 10.47 10.73 13.45 14.72 15.50 15.99 16.05
272 277 278 272 275 279 275 276 276 277 266, 271 271, 256, 274 266, 277 265, 266, 265, 267, 267, 267, 276 273, 277, 279 268, 271, 274, 275,
483.0763 745.1423 289.0690 633.0732 913.1487 289.0714 457.0780 911.1354 729.1512 635.0877 479.0826 785.0850 479.0826 771.1992 883.1359 771.1990 441.0822 755.2057 463.0881 755.2022 447.0934 593.1503 447.092 425.0896 759.1222 753.1310 551.0825 563.1190 715.1284 715.1311 867.1409
331, 607, 245, 463, 743, 245, 305, 759, 577, 483, 316 633, 316 609, 713, 301 289, 285 301 593, 285 285 285 273, 607, 601, 399, 441 563, 563, 563,
C20H20O14 C37H30O17 C15H14O6 C27H22O18 C44H34O22 C15H14O6 C22H18O11 C44H32O22 C37H30O16 C27H24O18 C21H20O13 C34H26O22 C21H20O13 C33H40O21 C43H32O21 C33H40O21 C22H18O10 C33H40O20 C21H20O12 C33H40O20 C21H20O11 C27H30O15 C21H20O11 C22H18O9 C37H28O18 C35H30O19 C27H20O13 C29H24O12 C36H28O16 C36H28O16 C43H32O20
2.5 2.4 7.6 0.6 2.6 0.7 2.0 5.2 7.7 1.1 0.0 1.7 0.0 1.0 0.1 0.8 0.0 2.9 0.9 1.7 1.6 0.5 1.6 0.9 3.3 0.9 0.2 0.0 2.1 1.7 0.0
digalloyl-glucose gallocatechin catechin gallate catechin strictinin theasinensin A epicatechin gallocatechin gallate theasinensin derivative procyanidin B-2 (or 4) 30 -O-gallate trigalloyl-glucose Myr-3-O-Gal digalloyl-HHDP-glucose Myr-3-O-Glc Quer-3-O-Gal-Rut theasinensin P-2 Quer-3-O-Glc-Rut epicatechin gallate Kaem-3-O-Gal-Rut Quer-3-O-Glc Kaem-3-O-Glc-Rut Kaem-3-O-Gal Kaem-3-O-pCA-Glc Kaem-3-O-Glc epiafzelechin-3-O-gallate theasinensin derivative theasinensin derivative epitheaflagalline-3-gallate theaflavin theaflavin-3-gallate theaflavin-3'-gallate theaflavin-3-3'-gallate
354 354 353 354 347 353 344 341 341 341 390 395 375 375 374 375
169 483, 205, 301 591, 205, 169 591, 559, 331,
305, 169 203, 137 573 203, 137 578, 169 407, 289, 169 169
563, 301 301 543, 405, 301 169
285
169 589, 451, 169 583, 169 295, 261, 169 169 169 169
Myr: myricetin; Quer: quercetin; Kaem: kaempferol; pCA: p-coumaroyl; HHDP: hexahydroxydiphenol; Gal: galactose; Glc: glucose; Rut: rutinoside.
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Quercetin or its glycone were generally detected in tea plants. Hence, peaks 14 and 16 were assigned as quercetin-3-O-galactosylrhamnosyl-glucoside (Quer-3-O-Gal-Rut) and quercetin-3-O-glucosyl-rhamnosyl-glucoside (Quer-3-O-Glu-Rut), because Quer-3-OGal-Rut was eluted earlier than Quer-3-O-Glu-Rut in a reversedphase liquid chromatography system (Scoparo et al., 2012). Markers 29 and 31 were determined to be theaflavin derivatives. Peaks 29 ([MeH] at m/z 715, tR 15.50 min) and 31 ([MeH] at m/z 867, tR 16.05 min) had the same fragment ions [MeH] at m/z 563 and 169 corresponding to the loss of a 152 and 546 amu. The loss of 152 and 546 amu were due to the cleavage of a galloyl and one H2O unit from theaflavin (peak 28, [MeH] at m/z 563, tR 14.72 min) moiety, respectively. On the basis of this information, peaks 29 and 31 were assigned as theaflavin-3-gallate and theflavin-3-3'-gallate, a well-known major component in black tea (Lin et al., 2008). 4. Conclusion In summary, metabolite differences among C. sinensis leaves during various growth stages were examined for the first time using a metabolomics approach. A total of 31 marker metabolites, including 4 gallotannins, 7 proanthocyanidin, 5 flavan-3-ols, 10 flavonols, and 5 theaflavin derivatives, were identified using UPLCQTof MS. In multivariate analysis, the results clearly demonstrate that marker metabolites discriminate between the April, MayeJune, and JulyeAugust samples by PCA. Changes in metabolite levels were revealed, and analysis of putative chemical markers provided insights into the phytochemical metabolism of C. sinensis leaves during germination, development, and maturation. Therefore, this method is convenient and simple for rapid analysis, as well as for evaluation of the overall quality of edible plant, providing thereby high value raw food material for commercial purpose in food industries. Acknowledgement This work was supported by the KRIBB Research Initiative Program (KGM1221622). Appendix A. Supplementary data Supplementary data related to this article can be found at http:// dx.doi.org/10.1016/j.foodcont.2016.10.017. References Chaturvedula, V. S. P., & Prakash, I. (2011). The aroma, taste, color and bioactive constituents of tea. Journal of Medicinal plants Research, 5, 2110e2124. Crozier, A., Jaganath, I. B., & Clifford, M. N. (2009). Dietary phenolics: Chemistry, bioavailability and effects on health. Natural Product Reports, 26, 1001e1043. Dou, J., Lee, V. S. Y., Tzen, J. T., & Lee, M. R. (2007). Identification and comparison of phenolic compounds in the preparation of oolong tea manufactured by semifermentation and drying processes. Journal of Agricultural and Food Chemistry, 55, 7462e7468. Farag, M. A., Porzel, A., Schmidt, J., & Wessjohann, L. A. (2012). Metabolite profiling and fingerprinting of commercial cultivars of Humulus lupulus L. (hop): A comparison of MS and NMR methods in metabolomics. Metabolomics, 8, 492e507. Hanhineva, K., Kokko, H., Siljanen, H., Rogachev, I., Aharoni, A., & K€ arenlampi, S. O. (2009). Stilbene synthase gene transfer caused alterations in the phenylpropanoid metabolism of transgenic strawberry (Fragaria ananassa). Journal of Experimental Botany, 60, 2093e2106. Haslam, E. (1998). Quinone tannin and oxidative polymerization. In Practical polyphenolics. From structure to molecular recognition and physiological action (pp. 335e373). Cambridge, UK: Cambridge University Press. Hodgson, A. B., Randell, R. K., Mahabir-Jagessar-T, K., Lotito, S., Mulder, T., et al.
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Please cite this article in press as: Ryu, H. W., et al., Comparison of secondary metabolite changes in Camellia sinensis leaves depending on the growth stage, Food Control (2016), http://dx.doi.org/10.1016/j.foodcont.2016.10.017
