Accepted Manuscript Effect of cultivar and variety on phenolic compounds and antioxidant activity of cherry wine Zuobing Xiao, Lingling Fang, Yunwei Niu, Haiyan Yu PII: DOI: Reference:
S0308-8146(15)00052-7 http://dx.doi.org/10.1016/j.foodchem.2015.01.050 FOCH 17001
To appear in:
Food Chemistry
Received Date: Revised Date: Accepted Date:
29 October 2014 2 January 2015 7 January 2015
Please cite this article as: Xiao, Z., Fang, L., Niu, Y., Yu, H., Effect of cultivar and variety on phenolic compounds and antioxidant activity of cherry wine, Food Chemistry (2015), doi: http://dx.doi.org/10.1016/j.foodchem. 2015.01.050
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Effect of cultivar and variety on phenolic compounds and antioxidant activity of cherry wine Zuobing Xiao, Lingling Fang, Yunwei Niu, Haiyan Yu* Department of Food Science and Technology, Shanghai Institute of Technology, Shanghai, 201418, China Running title: Phenolic compounds of different cherry wines
*
Corresponding author:
Tel.: +86-21-60873150.
[email protected] (H. Yu)
1
fax:
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ABSTRACT To compare the influence of cultivar and variety on the phenolic compounds and antioxidant activity (AA) of cherry wines, total phenolic (TP), total flavonoid (TF), total anthocyanin (TA), total tannin (TT), five individual phenolic acids, and AA were determined. An ultra-performance liquid chromatography tandem mass spectrometry (HPLC-DAD/ESI-MS) method was developed for the determination of gallic acid (GAE), p-hydroxybenzoic acid (PHB), chlorogenic acid (CHL), vanillic acid (VAN), and caffeic acid (CAF). A principal component analysis (PCA) and a cluster analysis (CA) were used to analyze differences related to cultivar and variety. The TP, TF, TA, TT, and AA of samples sourced from the Shandong province of China were higher than those from the Jiangsu province. The PCA and CA results showed that phenolic compounds in cherry wines were closely related to cultivar and variety and that cultivar had more influence on the phenolic compounds of cherry wines than variety. Keywords: Cherry wine; phenolic compounds; antioxidant activity; cluster analysis
2
1.
Introduction Cherry is one of the most popular fruits because it has an exotic flavor and is a good source of
bioactive phenolic compounds that are widely considered to be healthy for reasons such as strong antioxidant activity (Faniadis, Drogoudi, & Vasilakakis, 2010; Kirakosyan et al., 2010). Cherries are rich in anthocyanins (cyanidin, cyanidin 3-glucoside, cyanidin 3-rutinoside, peonidin, peonidin 3-glucoside, etc.), flavonols (isorhamnetin, isorhamnetin 3-glucoside, quercetin, kaempferol, etc.) (Kirakosyan, Seymour, Urcuyo, Kaufman, & Bolling, 2009), and phenolic acids (ferulic acid, chlorogenic acid, p-coumaric acid, etc.) (Jakobek, Šeruga, Šeruga, Novak, & Medvidovic-Kosanovic, 2009). Because cherries are a relatively perishable fruit and often not available to the consumer in an optimal condition after transportation (Yoo, Al-Farsi, Lee, Yoon, & Lee, 2010), they are often processed into juices and wines. Cherry wine is becoming popular in China. Phenolic compounds are responsible for the antioxidant activity and important sensory properties of wines, including color, astringency, and bitterness (Cadot, Caille, Samson, Barbeau, & Cheynier, 2012). An important group of phenolic compounds found in foods is the phenylpropanoid derivatives. These polyphenol derivatives form a diverse range of compounds and can be classified into many groups (anthocyanin pigments, flavonoids, flavanols, and flavonols, etc.). Phenolic acids have antioxidant activity, which may influence the quality and stability of the wine (Rodtjer, Skibsted, & Andersen, 2006). Flavonoids have been demonstrated to have antiviral, antiallergenic, and anticarcinogenic activities (Havsteen, 1983). Anthocyanins are natural pigments and are responsible for a wide range of colors in wines (Tagliazucchi, Verzelloni, Bertolini, & Conte, 2010). Tannins and anthocyanins influence the color of plant organs (Beninger & Hosfield, 1998). It is generally accepted that the final quality of fruit wine is significantly influenced by the raw materials (Gonzalez-Mas, Garcia-Riano, Alfaro, Rambla, Padilla, & Gutierrez, 2009). Therefore, the variety and cultivar of raw materials may have important effects on wines, including with regard to 3
sensory properties and antioxidant activity (Mikami-Konishide et al., 2013). Due to their high sugar levels and edibility, sweet cherry (Prunus avium L.) and Chinese cherry (Prunus pseudocerasus L.) are the two varieties that are extensively used in cherry wines. The main locations of cherry production in China are in the Shandong, Jiangsu, and Sichuan provinces. In this study, the effects of cultivar and variety on the phenolic compounds (total phenolic, total flavonoid, total anthocyanin, total tannin, and five individual phenolic acids) and antioxidant activity of cherry wine were investigated. Analysis of differences in wine samples with respect to cultivar and variety was performed using a principal component analysis (PCA) and a cluster analysis (CA). 2.
Materials and methods
2.1. Materials and reagents HPLC-grade methanol was purchased from Merck (Darmstadt, Germany). Gallic acid (GAE), p-hydroxybenzoic acid (PHB), chlorogenic acid (CHL), vanillic acid (VAN), caffeic acid (CAF), catechin, Cy-3-glc, Folin-Ciocalteu reagent, and 2,2-diphenyl-1-picrylhy-drazyl (DPPH) stable free-radical were purchased from Sigma-Aldrich (St. Louis, MO, USA). 1,10-Phenanthroline was purchased from J & K chemical (Shanghai, China). Ethylene diamine tetra-acetic acid (EDTA), sodium carbonate, sodium dihydrogen phosphate, disodium hydrogen phosphate, and other reagents were purchased from Shanghai Chemical Plant (Shanghai, China). Nine cherry wine samples were made from two different cherry varieties (Chinese cherry and sweet cherry) and two different cultivars from China (Shandong and Jiangsu provinces). Table 1 shows the varieties, cultivars, total acid, and pH of the cherry wine samples. The samples were stored at room temperature until analyzed. Prior to chromatographic analysis, the samples were filtered through 0.22 µm polytetrafluoroethylene (PTFE) filters. 2.2. Determination of total phenolic (TP) contents The amount of TP in cherry wines was determined according to the Folin-Ciocalteu procedure
4
(Čanadanovic-Brunet, Djilas, Ćetkovic, Tumbas, Mandic, & Čanadanovic, 2006). Briefly, 0.5 mL from each sample was mixed with 0.5 mL of the Folin-Ciocalteu reagent, 1.5 mL of 20% Na2CO 3 solution, and 7.5 mL deionized water. The reaction mixture was held at 75 °C for 60 min, and then, the absorbance at 760 nm was measured. The amount of TP in cherry wine was expressed as gallic acid equivalents (GAE) in mg/L. 2.3. Total flavonoid (TF) contents The total flavonoid (TF) content was determined by using a colorimetric assay (Verzelloni, Tagliazucchi, & Conte, 2007). Briefly, a 5 mL aliquot of an appropriately diluted sample was added to a 15 mL tube with 0.3 mL of 5% NaNO2, and the mixture was held at 25 °C for 10 min. Then, 0.3 mL of 5% AlCl3 was added to the mixture, and after 10 min, 4 mL of 1 mol/L NaOH was added. The total solution was adjusted to 10 mL with distilled water. The absorbance of the mixture was determined at 510 nm against an appropriate blank. The TF content was expressed as catechin equivalents (CE) in mg/L. 2.4. Total anthocyanin (TA) contents TA contents were determined using the pH differential method (Hosseinian, & Beta, 2008). The pH values of the diluted samples were 1.0 (0.03 M, 0.95 g KCl in 490 mL distilled water) and 4.5 (0.4 M, 27.2 g CH3CO2Na•3H2O in 480 mL distilled water). The absorbance was measured at both 520 and 700 nm against a distilled water blank. The TA was expressed as Cy-3-glc equivalents in mg/L. 2.5. Total tannin (TT) determination TT determination was performed using the procedure proposed by Cliff, King, & Schlosser, (2007). Each sample was placed in a tube, and the background absorbance was measured at 520 nm using a UV spectrophotometer. Twenty-five microliters of ferric chloride reagent (0.3% FeCl3) was added to each tube. The reaction mixture was held for 30 min, and the absorbance of each sample was measured at 520 nm. The TT content in each sample was calculated by subtracting the background 5
and final absorbances and comparing the value obtained to a standard curve. The TT content was reported in terms of mg/L. 2.6. Phenolic acids analysis Chromatographic analysis for phenolic acids was performed using an ultra-performance liquid chromatography-mass spectrometry (UPLC-MS) system (Waters, Milford, MA, USA). A bridged ethylene hybrid (BEH) C18 analytical column (100 mm×2.1 mm, 1.7 µm, Waters, Massachusetts, USA) was used at 25 °C. The mobile phase consisted of solvent A (water containing 5% formic acid v/v) and solvent B (methanol containing 5% formic acid v/v). The flow rate was 0.25 mL/min. The gradient program was as follows: 0 to 20 min, 5% to 15% B; 20 to 25 min, 15% to 10% B; and 25 to 30 min, 10% to 5% B. At the end of this sequence, the column was equilibrated under the initial conditions for 5 min. The mass spectrum analysis was carried out with an ESI Trap MS in negative mode. The effluent was introduced into an electrospray source (source block temperature 100 °C, desolvation temperature 350 °C, capillary voltage 3.2 kV, cone voltage 40 V). Argon was used as the collision gas (collision energy 2 eV), and nitrogen was used as the desolvation gas (540 L/h). 2.7. Antioxidant activity (AA) determination The AA of the cherry wines was investigated using the DPPH method. The DPPH method is based on measurements of the scavenging ability of antioxidants towards the stable free radical DPPH• (Socha, Juszczak, Pietrzyk, Galkowska, Fortuna, & Witczak, 2011). The wine was analyzed using the DPPH method after a ten-fold dilution. One microliter of each diluted wine was mixed with 2 mL of DPPH• solution (0.2 mmol/L) and 2 mL of methanol. The reaction mixture was held in the dark at room temperature for 30 min, and then, the mixture’s (Asample) absorbance at 517 nm was measured against a blank sample (1 mL of diluted wine in 4 mL of methanol). The DPPH• blank solution was prepared fresh each day, and its absorbance at 517 nm (ADPPH) was also measured daily. The relative antioxidant activity was calculated using the following equation:
6
%Inhibition =[(ADPPH − Asample)/ ADPPH] × 100
(1).
The percentage inhibition of the DPPH• radical from each diluted wine sample was plotted against the volume of undiluted wine in the reaction mixture. Using the curve obtained, antioxidant activity was expressed as the volume (mL) of wine needed to reduce the initial concentration of the DPPH• radical by 50% (i.e., efficient concentration, EC50). 2.8. Multivariate statistical analysis Principal component analysis (PCA) is one of the most popular multivariate statistical methods because it reduces the dimensionality, compresses noise and correlates measurements in a simple informational sub-space of the data set (Saavedra, Cordova, Galvez, Quezada, & Navarro, 2013). The principal components (PCs) or eigenvectors are orthogonal and are a linear combination of the original variables. PCA analysis was performed using XLSTAT Ver. 7.5 (Addinsoft, New York, NY, USA). Cluster analysis (CA) was applied to establish the relationships between the samples in the data set and produce a tree diagram (Cliff et al., 2007). The samples were clustered stepwise according to the similarity of their characteristics. The similarities between samples were calculated based on the average distance. In this study, a CA was used to establish the relationships between the cherry wine samples from different varieties and cultivars and was performed using SPSS 17.0 software. 3.
Results and Discussion The TP, TF, TA, and TT contents of nine cherry wine samples are shown in Table 2 (mean value
± SD). Among the four parameters, TP content was the highest, followed by TT. This trend was in accordance with that obtained by Toydemir et al. (2013). The TP contents were in the range of 235.53 to 736.54 mg GAE/L. W1 from the Shandong province had the highest TP content (736.54 mg GAE/L), and W7 from the Jiangsu province had the lowest TP content (235.53 mg GAE/L). The TP contents determined in this study revealed significant differences between samples sourced from the 7
Shandong province and those from the Jiangsu province. The TP contents of W1, W2, and W9 made with Chinese cherry were significantly different from those of W3, W4, W5, W6, W7, and W8, which were made with sweet cherry. The TF contents of samples from the Shandong province were higher than those from the Jiangsu province. All nine cherry wines contained anthocyanins, which are the major phenolics in cherries (Chaovanalikit & Wrolstad, 2004, Toydemir et al., 2013). The TA contents varied from 0.18 to 1.67 mg catechin/L. There were significant differences between samples sourced from the Shandong province, but no significant differences between samples sourced from the Jiangsu province were found. The TA contents of the Shandong province wines were higher than those from the Jiangsu province. In the research of Toydemir et al. (2013), TA content varied as the source region of sour cherry was different. The TT contents of wines from the Shandong province were comparatively higher than those from the Jiangsu province. During winemaking, the phenolic compounds pass from the fruits to the wines and remain active, but their profiles change and partially degrade during the disintegration of raw materials, fermentation and ageing (Chaovanalikit et al., 2004). Thus, cherry wines made from different raw materials had different contents of phenolic compounds. DPPH method was used to assess AA of the cherry wines. The results were normalized by calculating the antioxidant concentration and summarized in Table 1 as mean ± SD of triplicate measurements. The scavenging rate was 50% and expressed as EC50. The antioxidant properties are inversely correlated with the EC50 values. The AA of wines showed a similar trend with TP, TF, TA and TT. All wines in this study exhibited potent radical scavenging activity. AA expressed as EC50 ranged from 0.20 to 0.42 mL of wines as measured by the DPPH method. There were significant differences between samples sourced from Shangdong province, but there were no significant differences between samples sourced from Jiangsu province. A total of five different phenolic acids were identified and quantified. The chromatogram of the phenolic acid standard solutions is shown in Fig. 1. Peaks 1, 2, 3, 4, and 5 were identified as GAE,
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PHB, CHL, VAN, and CAF, respectively, by comparing the retention time and MS/MS information with standards. In a study by González-Gómez et al., five different phenolic acids in cherry wine samples were identified and determined by HPLC-DAD/ESI-MS (Gonzalez-Gomez, Lozano, Fernandez-leon, Bernalte, Ayuso, & Rodriguez, 2010). CAF was the most important phenolic acid analyzed in the samples from the Shandong province, while GAE was the most important in the samples from the Jiangsu province (Table 3). To obtain an overall picture of the distribution of the compounds according to the sample classification, a PCA was performed (Fig. 2). The first principal component (PC1) explained 46.79% of the variation across the samples, while the second PC (PC2) explained 31.18% of the variance. Although PCA itself cannot be used as a classification tool, it may indicate a trend that is relevant for visualizing the dimension space. The initial two factors, which were related to chemical quality and are indicated as positive or negative, are used to make the differentiation clearer. The samples sourced from the Shandong province grouped together, and the samples sourced from the Jiangsu province grouped together. A CA was applied to determine whether the data set could be divided into groups and explain the scale and relationship of the varieties and cultivars for the analyzed cherry wines. The similarities between the samples were determined based on the Euclidean distance, and the objects were clustered using the minimum linkage method. The CA dendrogram is shown in Fig. 3, in which two clusters were suggested. The first group consisted of W1, W2, and W3, which were all sourced from the Shandong province, and the second group consisted of W4, W5, W6, W7, W8, and W9, which were all sourced from the Jiangsu province. This result suggests that cultivar had more influence on the all the phenolic compounds of cherry wines than variety. Kimura et al. (2014) characterized onions produced from different varieties and cultivars. Their results suggested that the optimal usage of onions as processing ingredients varied with varieties and cultivars. Kwee and Niemeyer (2011) 9
reported that cultivar had a significant effect on TP of basil. García-Salas et al. (2014) reported that phenolic compounds concentration of eggplant depended on cultivar. 4.
Conclusions This study found that cherry wine had high TP and TT. The TP, TF, TA, TT contents and that
the AA of samples sourced from the Shandong province were higher than those from the Jiangsu province. The highest concentration of CAF was found in the samples from the Shandong province, while the highest concentration of GAE was found in the samples from the Jiangsu province. Statistical correlations were observed between phenolic compounds and cultivar and variety. Since the phenolic compounds might change and partially degrade during the fermentation and ageing process, the effect of fermentation and ageing on phenolic compounds and antioxidant activity of cherry wine will be investigated. Acknowledgements The authors gratefully acknowledge the financial support provided by the National Natural Science Foundation of China (No. 21105065) and the Special Funds for National Excellent Doctoral Dissertation of China (No. 201059). References Beninger, C. W., & Hosfleld, G. L. (1998). Flavonol glycosides from the seed coat of a new manteca type dry bean (Phaseolus vulgaris L.). Journal of Agricultural and Food Chemistry, 46, 2906–2910. Cadot, Y., Caille, S., Samson, A., Barbeau, G., & Cheynier, V. (2012). Sensory representation of typicality of Cabernet Franc wines related to phenolic composition: impact of ripening stage and maceration time. Analytica Chimica Acta, 732, 91–99. Čanadanovic-Brunet, J. M., Djilas, S. M., Ćetkovic, G. S., Tumbas, V. T., Mandic, A. I., & Čanadanovic, V. M. (2006). Antioxidant activities of different Teucrium Montanum L. extracts.
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International Journal of Food Science and Technology, 41, 667–673. Chaovanalikit, A., & Wrolstad, R. E. (2004). Anthocyanin and polyphenolic composition of fresh and processed cherries. Journal of Food Science, 69, FCT73–FCT83. Cliff, M. A., King, M. C., & Schlosser, J. (2007). Anthocyanin, phenolic composition, colour measurement and sensory analysis of BC commercial red wines. Food Research International, 40, 92–100. Faniadis, D., Drogoudi, P. D., & Vasilakakis, M. (2010). Effects of cultivar, orchard elevation, and storage on fruit quality characters of sweet cherry (Prunus avium L.). Scientia Horticulturae, 125, 301–304. García-Salas,
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in purple wheat. Food Chemistry, 109, 916–924. Jakobek, L., Šeruga, M., Šeruga, B., Novak, I., & Medvidovic-Kosanovic, M. (2009). Phenolic compound composition and antioxidant activity of fruits of Rubus and Prunus species from Croatia. International Journal of Food Science and Technology, 44, 860–868. Kimura, Y., Okazaki, K., Yanagida, D., Muro, T. (2014). Cultivar and regional differences in the metabolite compositionof onion (Allium cepa). Scientia Horticulturae, 168, 1–8. Kirakosyan, A., Seymour, E. M., Noon, K. R., Urcuyo, L. D. E., Kaufman, P. B., & Warber, S. L., et al. (2010). Interactions of antioxidants isolated from tart cherry (Prunus cerasus) fruits. Food Chemistry, 122, 78–83. Kirakosyan, A., Seymour, E. M., Urcuyo , L. D. E., Kaufman , P. B., & Bolling, S. F. (2009). Chemical profile and antioxidant capacities of tart cherry products. Food Chemistry, 115, 20–25. Kwee, E. M., & Niemeyer E. D. (2011). Variations in phenolic composition and antioxidant properties among 15 basil (Ocimum basilicum L.) cultivars. Food Chemistry, 128, 1044–1050. Mikami-Konishide, I., Murakami, S., Nakanishi, K., Takahashi, Y., Yamaguchi, M., & Shioya, T., et al. (2013). Antioxidant capacity and polyphenol content of extracts from crops cultivated in Japan, and the effect of cultivation environment. Food Science and Technology Research, 19, 69–79. Rodtjer, A., Skibsted, L. H., & Andersen, M. L. (2006). Antioxidative and prooxidative effects of extracts made from cherry liqueur pomace. Food Chemistry, 99, 6–14. Saavedra, J., Cordova, A., Galvez, L., Quezada, C., & Navarro, R. (2013). Principal component analysis as an exploration tool for kinetic modeling of food quality: A case study of a dried apple cluster snack. Journal of Food Engineering, 119, 229–235. Socha, R., Juszczak, L., Pietrzyk, S., Galkowska, D., Fortuna, T., & Witczak, T. (2011). Phenolic profile and antioxidant properties of polish honeys. International Journal of Food Science and
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Technology, 46, 528–534. Toydemir, G., Capanoglu, E., Kamiloglu, S., Boyacioglu, D., de Vos, R. C. H., Hall, R. D., & Beekwild, J. Changes in sour cherry (Prunus cerasus L.) antioxidants during nectar processing and in vitro gastrointestinal digestion. Journal of Functional Foods, 5, 1402–1413. Tagliazucchi, D., Verzelloni, E., Bertolini, D., & Conte, A. (2010). In vitro bio-accessibility and antioxidant activity of grape polyphenols. Food Chemistry, 120, 599–606. Verzelloni, E., Tagliazucchi, D., & Conte, A. (2007). Relationship between the antioxidant properties and the phenolic and flavonoid content in traditional balsamic vinegar. Food Chemistry, 105, 564–571. Yoo, K. M., Al-Farsi, M., Lee, H., Yoon, H., & Lee, C. Y. (2010). Antiproliferative effects of cherry juice and wine in Chinese hamster lung fibroblast cells and their phenolic constituents and antioxidant activities. Food Chemistry, 123, 734–74.
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Figure Captions Fig. 1. Ultra-performance liquid chromatography-mass spectrometry separation of phenolic acid standards. Fig. 2. Principal component analysis scores for the nine cherry wine samples. Fig. 3. Dendrogram constructed with minimum linkage method for the nine cherry wine samples.
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Fig. 1. Ultra-performance liquid chromatography-mass spectrometry separation of phenolic acid standards. Peaks 1, 2, 3, 4, and 5 were identified as gallic acid (GAE), p-hydroxybenzoic acid (PHB), chlorogenic acid (CHL), vanillic acid (VAN), and caffeic acid (CAF), respectively
15
Fig. 2. Principal component analysis scores for the nine cherry wine samples.
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Fig. 3. Dendrogram constructed with minimum linkage method for the nine cherry wine samples.
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Table 1 Variety, cultivar, total acid, and pH information of nine cherry wine samples. Sample
Variety
Cultivar
code
Total acid
pH
(g/L)
W1
Chinese cherry
Shandong
5.52
3.37
W2
Chinese cherry
Shandong
5.67
3.49
W3
Sweet cherry
Shandong
7.59
3.73
W4
Sweet cherry
Jiangsu
7.97
3.70
W5
Sweet cherry
Jiangsu
7.74
3.68
W6
Sweet cherry
Jiangsu
7.97
3.69
W7
Sweet cherry
Jiangsu
7.82
3.79
W8
Sweet cherry
Jiangsu
7.90
3.68
W9
Chinese cherry
Jiangsu
9.99
3.37
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Table 2 Total phenolic (TP), total flavonoid (TF), total anthocyanin (TA), total tannin (TT) contents and antioxidant activity (AA) of nine cherry wine samples*. Sample
TP
TF
TA
TT
AA-EC50
(mg GAE /L)
(mg/L)
(mg catechin/L)
(mg/L)
(mL of wine)
W1
736.54 ±15.81a
2.15 ± 0.18a
1.67 ± 0.12a
183.65 ± 14.58a
0.17 ± 0.01c
W2
619.26 ± 13.20a
0.16 ± 0.01b
0.18 ± 0.01d
122.66 ± 10.32ab
0.42 ± 0.03a
W3
687.83 ± 12.32a
0.86 ± 0.08b
0.78 ± 0.05b
149.84 ± 12.48ab
0.28 ± 0.03b
W4
261.45 ± 8.45b
0.69 ± 0.06b
0.48 ± 0.02c
90.18 ± 8.59b
0.19 ± 0.01bc
W5
268.17 ± 9.69b
0.55 ± 0.03b
0.54 ± 0.04cb
85.21 ± 7.43b
0.28 ± 0.02d
W6
262.29 ± 10.27 b
0.53 ± 0.05 b
0.55 ± 0.05 cb
92.83 ± 9.04 b
0.24 ± 0.03 d
W7
235.53± 11.05 b
0.44 ± 0.02 b
0.44 ± 0.02 cd
97.81 ± 9.37 b
0.21 ± 0.01 cd
W8
296.57 ± 10.16 b
0.50 ± 0.03 b
0.50 ± 0.03c
120.01 ± 11.52 ab
0.35 ± 0.03 c
W9
616.08 ± 16.25 a
0.12±0.01 b
0.21 ± 0.01 d
6.66 ± 0.39 c
0.20 ± 0.01 d
code
* Mean scores for each attribute within a column with different letters are significantly different (p < 0.05) using Duncan’s multiple comparison tests.
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Table 3 Five individual phenolic acids of nine cherry wine samples* (mg/L). Sample
GAE
PHB
CHL
VAN
CAF
W1
0f
0d
0c
40.34 ± 3.47 a
51.80 ± 4.83 c
W2
35.02 ± 2.85 b
12.81 ± 1.09 b
13.22 ± 1.14 b
5.07 ± 0.38 d
101.79 ± 10.04 b
W3
83.55 ± 7.38 a
50.88 ± 4.62 a
21.31 ± 1.92 a
40.76 ± 3.68 a
135.93 ± 12.58 a
W4
9.86 ± 0.85 e
0.26 ± 0.02 d
1.30 ± 0.11 c
8.87 ± 0.62 c
4.35 ± 0.32 e
W5
7.26 ± 0.67 e
0.25 ± 0.02 d
0.76 ± 0.05 c
3.01 ± 0.26 d
2.55 ± 0.14 e
W6
19.24 ± 1.65 d
4.24 ± 0.38 c
22.21 ± 2.07 a
11.85 ± 1.09 b
0.70 ± 0.06 e
W7
30.02 ± 2.89 c
0.28 ± 0.02 d
12.41 ± 1.07 b
3.75 ± 0.31 d
1.32 ± 0.11 e
W8
6.57 ± 0.53 e
0.31 ± 0.02 d
1.09 ± 0.10 c
4.38 ± 0.37 d
0.30 ± 0.02 e
W9
7.84 ± 0.69 e
1.05± 0.01 d
0c
0e
13.27± 1.28 d
code
* Mean scores for each attribute within a column with different letters are significantly different (p < 0.05) using Duncan’s multiple comparison tests.
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Highlights
A HPLC-DAD/ESI-MS method was developed for analysis of phenolic acids in cherry wine.
The phenolic compounds in cherry wines were closely related to cultivar and variety.
Cultivar had more influence on the phenolic compounds of cherry wines than variety.
21
S0308-8146(15)00052-7 http://dx.doi.org/10.1016/j.foodchem.2015.01.050 FOCH 17001
To appear in:
Food Chemistry
Received Date: Revised Date: Accepted Date:
29 October 2014 2 January 2015 7 January 2015
Please cite this article as: Xiao, Z., Fang, L., Niu, Y., Yu, H., Effect of cultivar and variety on phenolic compounds and antioxidant activity of cherry wine, Food Chemistry (2015), doi: http://dx.doi.org/10.1016/j.foodchem. 2015.01.050
This is a PDF file of an unedited manuscript that has been accepted for publication. As a service to our customers we are providing this early version of the manuscript. The manuscript will undergo copyediting, typesetting, and review of the resulting proof before it is published in its final form. Please note that during the production process errors may be discovered which could affect the content, and all legal disclaimers that apply to the journal pertain.
Effect of cultivar and variety on phenolic compounds and antioxidant activity of cherry wine Zuobing Xiao, Lingling Fang, Yunwei Niu, Haiyan Yu* Department of Food Science and Technology, Shanghai Institute of Technology, Shanghai, 201418, China Running title: Phenolic compounds of different cherry wines
*
Corresponding author:
Tel.: +86-21-60873150.
[email protected] (H. Yu)
1
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ABSTRACT To compare the influence of cultivar and variety on the phenolic compounds and antioxidant activity (AA) of cherry wines, total phenolic (TP), total flavonoid (TF), total anthocyanin (TA), total tannin (TT), five individual phenolic acids, and AA were determined. An ultra-performance liquid chromatography tandem mass spectrometry (HPLC-DAD/ESI-MS) method was developed for the determination of gallic acid (GAE), p-hydroxybenzoic acid (PHB), chlorogenic acid (CHL), vanillic acid (VAN), and caffeic acid (CAF). A principal component analysis (PCA) and a cluster analysis (CA) were used to analyze differences related to cultivar and variety. The TP, TF, TA, TT, and AA of samples sourced from the Shandong province of China were higher than those from the Jiangsu province. The PCA and CA results showed that phenolic compounds in cherry wines were closely related to cultivar and variety and that cultivar had more influence on the phenolic compounds of cherry wines than variety. Keywords: Cherry wine; phenolic compounds; antioxidant activity; cluster analysis
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1.
Introduction Cherry is one of the most popular fruits because it has an exotic flavor and is a good source of
bioactive phenolic compounds that are widely considered to be healthy for reasons such as strong antioxidant activity (Faniadis, Drogoudi, & Vasilakakis, 2010; Kirakosyan et al., 2010). Cherries are rich in anthocyanins (cyanidin, cyanidin 3-glucoside, cyanidin 3-rutinoside, peonidin, peonidin 3-glucoside, etc.), flavonols (isorhamnetin, isorhamnetin 3-glucoside, quercetin, kaempferol, etc.) (Kirakosyan, Seymour, Urcuyo, Kaufman, & Bolling, 2009), and phenolic acids (ferulic acid, chlorogenic acid, p-coumaric acid, etc.) (Jakobek, Šeruga, Šeruga, Novak, & Medvidovic-Kosanovic, 2009). Because cherries are a relatively perishable fruit and often not available to the consumer in an optimal condition after transportation (Yoo, Al-Farsi, Lee, Yoon, & Lee, 2010), they are often processed into juices and wines. Cherry wine is becoming popular in China. Phenolic compounds are responsible for the antioxidant activity and important sensory properties of wines, including color, astringency, and bitterness (Cadot, Caille, Samson, Barbeau, & Cheynier, 2012). An important group of phenolic compounds found in foods is the phenylpropanoid derivatives. These polyphenol derivatives form a diverse range of compounds and can be classified into many groups (anthocyanin pigments, flavonoids, flavanols, and flavonols, etc.). Phenolic acids have antioxidant activity, which may influence the quality and stability of the wine (Rodtjer, Skibsted, & Andersen, 2006). Flavonoids have been demonstrated to have antiviral, antiallergenic, and anticarcinogenic activities (Havsteen, 1983). Anthocyanins are natural pigments and are responsible for a wide range of colors in wines (Tagliazucchi, Verzelloni, Bertolini, & Conte, 2010). Tannins and anthocyanins influence the color of plant organs (Beninger & Hosfield, 1998). It is generally accepted that the final quality of fruit wine is significantly influenced by the raw materials (Gonzalez-Mas, Garcia-Riano, Alfaro, Rambla, Padilla, & Gutierrez, 2009). Therefore, the variety and cultivar of raw materials may have important effects on wines, including with regard to 3
sensory properties and antioxidant activity (Mikami-Konishide et al., 2013). Due to their high sugar levels and edibility, sweet cherry (Prunus avium L.) and Chinese cherry (Prunus pseudocerasus L.) are the two varieties that are extensively used in cherry wines. The main locations of cherry production in China are in the Shandong, Jiangsu, and Sichuan provinces. In this study, the effects of cultivar and variety on the phenolic compounds (total phenolic, total flavonoid, total anthocyanin, total tannin, and five individual phenolic acids) and antioxidant activity of cherry wine were investigated. Analysis of differences in wine samples with respect to cultivar and variety was performed using a principal component analysis (PCA) and a cluster analysis (CA). 2.
Materials and methods
2.1. Materials and reagents HPLC-grade methanol was purchased from Merck (Darmstadt, Germany). Gallic acid (GAE), p-hydroxybenzoic acid (PHB), chlorogenic acid (CHL), vanillic acid (VAN), caffeic acid (CAF), catechin, Cy-3-glc, Folin-Ciocalteu reagent, and 2,2-diphenyl-1-picrylhy-drazyl (DPPH) stable free-radical were purchased from Sigma-Aldrich (St. Louis, MO, USA). 1,10-Phenanthroline was purchased from J & K chemical (Shanghai, China). Ethylene diamine tetra-acetic acid (EDTA), sodium carbonate, sodium dihydrogen phosphate, disodium hydrogen phosphate, and other reagents were purchased from Shanghai Chemical Plant (Shanghai, China). Nine cherry wine samples were made from two different cherry varieties (Chinese cherry and sweet cherry) and two different cultivars from China (Shandong and Jiangsu provinces). Table 1 shows the varieties, cultivars, total acid, and pH of the cherry wine samples. The samples were stored at room temperature until analyzed. Prior to chromatographic analysis, the samples were filtered through 0.22 µm polytetrafluoroethylene (PTFE) filters. 2.2. Determination of total phenolic (TP) contents The amount of TP in cherry wines was determined according to the Folin-Ciocalteu procedure
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(Čanadanovic-Brunet, Djilas, Ćetkovic, Tumbas, Mandic, & Čanadanovic, 2006). Briefly, 0.5 mL from each sample was mixed with 0.5 mL of the Folin-Ciocalteu reagent, 1.5 mL of 20% Na2CO 3 solution, and 7.5 mL deionized water. The reaction mixture was held at 75 °C for 60 min, and then, the absorbance at 760 nm was measured. The amount of TP in cherry wine was expressed as gallic acid equivalents (GAE) in mg/L. 2.3. Total flavonoid (TF) contents The total flavonoid (TF) content was determined by using a colorimetric assay (Verzelloni, Tagliazucchi, & Conte, 2007). Briefly, a 5 mL aliquot of an appropriately diluted sample was added to a 15 mL tube with 0.3 mL of 5% NaNO2, and the mixture was held at 25 °C for 10 min. Then, 0.3 mL of 5% AlCl3 was added to the mixture, and after 10 min, 4 mL of 1 mol/L NaOH was added. The total solution was adjusted to 10 mL with distilled water. The absorbance of the mixture was determined at 510 nm against an appropriate blank. The TF content was expressed as catechin equivalents (CE) in mg/L. 2.4. Total anthocyanin (TA) contents TA contents were determined using the pH differential method (Hosseinian, & Beta, 2008). The pH values of the diluted samples were 1.0 (0.03 M, 0.95 g KCl in 490 mL distilled water) and 4.5 (0.4 M, 27.2 g CH3CO2Na•3H2O in 480 mL distilled water). The absorbance was measured at both 520 and 700 nm against a distilled water blank. The TA was expressed as Cy-3-glc equivalents in mg/L. 2.5. Total tannin (TT) determination TT determination was performed using the procedure proposed by Cliff, King, & Schlosser, (2007). Each sample was placed in a tube, and the background absorbance was measured at 520 nm using a UV spectrophotometer. Twenty-five microliters of ferric chloride reagent (0.3% FeCl3) was added to each tube. The reaction mixture was held for 30 min, and the absorbance of each sample was measured at 520 nm. The TT content in each sample was calculated by subtracting the background 5
and final absorbances and comparing the value obtained to a standard curve. The TT content was reported in terms of mg/L. 2.6. Phenolic acids analysis Chromatographic analysis for phenolic acids was performed using an ultra-performance liquid chromatography-mass spectrometry (UPLC-MS) system (Waters, Milford, MA, USA). A bridged ethylene hybrid (BEH) C18 analytical column (100 mm×2.1 mm, 1.7 µm, Waters, Massachusetts, USA) was used at 25 °C. The mobile phase consisted of solvent A (water containing 5% formic acid v/v) and solvent B (methanol containing 5% formic acid v/v). The flow rate was 0.25 mL/min. The gradient program was as follows: 0 to 20 min, 5% to 15% B; 20 to 25 min, 15% to 10% B; and 25 to 30 min, 10% to 5% B. At the end of this sequence, the column was equilibrated under the initial conditions for 5 min. The mass spectrum analysis was carried out with an ESI Trap MS in negative mode. The effluent was introduced into an electrospray source (source block temperature 100 °C, desolvation temperature 350 °C, capillary voltage 3.2 kV, cone voltage 40 V). Argon was used as the collision gas (collision energy 2 eV), and nitrogen was used as the desolvation gas (540 L/h). 2.7. Antioxidant activity (AA) determination The AA of the cherry wines was investigated using the DPPH method. The DPPH method is based on measurements of the scavenging ability of antioxidants towards the stable free radical DPPH• (Socha, Juszczak, Pietrzyk, Galkowska, Fortuna, & Witczak, 2011). The wine was analyzed using the DPPH method after a ten-fold dilution. One microliter of each diluted wine was mixed with 2 mL of DPPH• solution (0.2 mmol/L) and 2 mL of methanol. The reaction mixture was held in the dark at room temperature for 30 min, and then, the mixture’s (Asample) absorbance at 517 nm was measured against a blank sample (1 mL of diluted wine in 4 mL of methanol). The DPPH• blank solution was prepared fresh each day, and its absorbance at 517 nm (ADPPH) was also measured daily. The relative antioxidant activity was calculated using the following equation:
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%Inhibition =[(ADPPH − Asample)/ ADPPH] × 100
(1).
The percentage inhibition of the DPPH• radical from each diluted wine sample was plotted against the volume of undiluted wine in the reaction mixture. Using the curve obtained, antioxidant activity was expressed as the volume (mL) of wine needed to reduce the initial concentration of the DPPH• radical by 50% (i.e., efficient concentration, EC50). 2.8. Multivariate statistical analysis Principal component analysis (PCA) is one of the most popular multivariate statistical methods because it reduces the dimensionality, compresses noise and correlates measurements in a simple informational sub-space of the data set (Saavedra, Cordova, Galvez, Quezada, & Navarro, 2013). The principal components (PCs) or eigenvectors are orthogonal and are a linear combination of the original variables. PCA analysis was performed using XLSTAT Ver. 7.5 (Addinsoft, New York, NY, USA). Cluster analysis (CA) was applied to establish the relationships between the samples in the data set and produce a tree diagram (Cliff et al., 2007). The samples were clustered stepwise according to the similarity of their characteristics. The similarities between samples were calculated based on the average distance. In this study, a CA was used to establish the relationships between the cherry wine samples from different varieties and cultivars and was performed using SPSS 17.0 software. 3.
Results and Discussion The TP, TF, TA, and TT contents of nine cherry wine samples are shown in Table 2 (mean value
± SD). Among the four parameters, TP content was the highest, followed by TT. This trend was in accordance with that obtained by Toydemir et al. (2013). The TP contents were in the range of 235.53 to 736.54 mg GAE/L. W1 from the Shandong province had the highest TP content (736.54 mg GAE/L), and W7 from the Jiangsu province had the lowest TP content (235.53 mg GAE/L). The TP contents determined in this study revealed significant differences between samples sourced from the 7
Shandong province and those from the Jiangsu province. The TP contents of W1, W2, and W9 made with Chinese cherry were significantly different from those of W3, W4, W5, W6, W7, and W8, which were made with sweet cherry. The TF contents of samples from the Shandong province were higher than those from the Jiangsu province. All nine cherry wines contained anthocyanins, which are the major phenolics in cherries (Chaovanalikit & Wrolstad, 2004, Toydemir et al., 2013). The TA contents varied from 0.18 to 1.67 mg catechin/L. There were significant differences between samples sourced from the Shandong province, but no significant differences between samples sourced from the Jiangsu province were found. The TA contents of the Shandong province wines were higher than those from the Jiangsu province. In the research of Toydemir et al. (2013), TA content varied as the source region of sour cherry was different. The TT contents of wines from the Shandong province were comparatively higher than those from the Jiangsu province. During winemaking, the phenolic compounds pass from the fruits to the wines and remain active, but their profiles change and partially degrade during the disintegration of raw materials, fermentation and ageing (Chaovanalikit et al., 2004). Thus, cherry wines made from different raw materials had different contents of phenolic compounds. DPPH method was used to assess AA of the cherry wines. The results were normalized by calculating the antioxidant concentration and summarized in Table 1 as mean ± SD of triplicate measurements. The scavenging rate was 50% and expressed as EC50. The antioxidant properties are inversely correlated with the EC50 values. The AA of wines showed a similar trend with TP, TF, TA and TT. All wines in this study exhibited potent radical scavenging activity. AA expressed as EC50 ranged from 0.20 to 0.42 mL of wines as measured by the DPPH method. There were significant differences between samples sourced from Shangdong province, but there were no significant differences between samples sourced from Jiangsu province. A total of five different phenolic acids were identified and quantified. The chromatogram of the phenolic acid standard solutions is shown in Fig. 1. Peaks 1, 2, 3, 4, and 5 were identified as GAE,
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PHB, CHL, VAN, and CAF, respectively, by comparing the retention time and MS/MS information with standards. In a study by González-Gómez et al., five different phenolic acids in cherry wine samples were identified and determined by HPLC-DAD/ESI-MS (Gonzalez-Gomez, Lozano, Fernandez-leon, Bernalte, Ayuso, & Rodriguez, 2010). CAF was the most important phenolic acid analyzed in the samples from the Shandong province, while GAE was the most important in the samples from the Jiangsu province (Table 3). To obtain an overall picture of the distribution of the compounds according to the sample classification, a PCA was performed (Fig. 2). The first principal component (PC1) explained 46.79% of the variation across the samples, while the second PC (PC2) explained 31.18% of the variance. Although PCA itself cannot be used as a classification tool, it may indicate a trend that is relevant for visualizing the dimension space. The initial two factors, which were related to chemical quality and are indicated as positive or negative, are used to make the differentiation clearer. The samples sourced from the Shandong province grouped together, and the samples sourced from the Jiangsu province grouped together. A CA was applied to determine whether the data set could be divided into groups and explain the scale and relationship of the varieties and cultivars for the analyzed cherry wines. The similarities between the samples were determined based on the Euclidean distance, and the objects were clustered using the minimum linkage method. The CA dendrogram is shown in Fig. 3, in which two clusters were suggested. The first group consisted of W1, W2, and W3, which were all sourced from the Shandong province, and the second group consisted of W4, W5, W6, W7, W8, and W9, which were all sourced from the Jiangsu province. This result suggests that cultivar had more influence on the all the phenolic compounds of cherry wines than variety. Kimura et al. (2014) characterized onions produced from different varieties and cultivars. Their results suggested that the optimal usage of onions as processing ingredients varied with varieties and cultivars. Kwee and Niemeyer (2011) 9
reported that cultivar had a significant effect on TP of basil. García-Salas et al. (2014) reported that phenolic compounds concentration of eggplant depended on cultivar. 4.
Conclusions This study found that cherry wine had high TP and TT. The TP, TF, TA, TT contents and that
the AA of samples sourced from the Shandong province were higher than those from the Jiangsu province. The highest concentration of CAF was found in the samples from the Shandong province, while the highest concentration of GAE was found in the samples from the Jiangsu province. Statistical correlations were observed between phenolic compounds and cultivar and variety. Since the phenolic compounds might change and partially degrade during the fermentation and ageing process, the effect of fermentation and ageing on phenolic compounds and antioxidant activity of cherry wine will be investigated. Acknowledgements The authors gratefully acknowledge the financial support provided by the National Natural Science Foundation of China (No. 21105065) and the Special Funds for National Excellent Doctoral Dissertation of China (No. 201059). References Beninger, C. W., & Hosfleld, G. L. (1998). Flavonol glycosides from the seed coat of a new manteca type dry bean (Phaseolus vulgaris L.). Journal of Agricultural and Food Chemistry, 46, 2906–2910. Cadot, Y., Caille, S., Samson, A., Barbeau, G., & Cheynier, V. (2012). Sensory representation of typicality of Cabernet Franc wines related to phenolic composition: impact of ripening stage and maceration time. Analytica Chimica Acta, 732, 91–99. Čanadanovic-Brunet, J. M., Djilas, S. M., Ćetkovic, G. S., Tumbas, V. T., Mandic, A. I., & Čanadanovic, V. M. (2006). Antioxidant activities of different Teucrium Montanum L. extracts.
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International Journal of Food Science and Technology, 41, 667–673. Chaovanalikit, A., & Wrolstad, R. E. (2004). Anthocyanin and polyphenolic composition of fresh and processed cherries. Journal of Food Science, 69, FCT73–FCT83. Cliff, M. A., King, M. C., & Schlosser, J. (2007). Anthocyanin, phenolic composition, colour measurement and sensory analysis of BC commercial red wines. Food Research International, 40, 92–100. Faniadis, D., Drogoudi, P. D., & Vasilakakis, M. (2010). Effects of cultivar, orchard elevation, and storage on fruit quality characters of sweet cherry (Prunus avium L.). Scientia Horticulturae, 125, 301–304. García-Salas,
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in purple wheat. Food Chemistry, 109, 916–924. Jakobek, L., Šeruga, M., Šeruga, B., Novak, I., & Medvidovic-Kosanovic, M. (2009). Phenolic compound composition and antioxidant activity of fruits of Rubus and Prunus species from Croatia. International Journal of Food Science and Technology, 44, 860–868. Kimura, Y., Okazaki, K., Yanagida, D., Muro, T. (2014). Cultivar and regional differences in the metabolite compositionof onion (Allium cepa). Scientia Horticulturae, 168, 1–8. Kirakosyan, A., Seymour, E. M., Noon, K. R., Urcuyo, L. D. E., Kaufman, P. B., & Warber, S. L., et al. (2010). Interactions of antioxidants isolated from tart cherry (Prunus cerasus) fruits. Food Chemistry, 122, 78–83. Kirakosyan, A., Seymour, E. M., Urcuyo , L. D. E., Kaufman , P. B., & Bolling, S. F. (2009). Chemical profile and antioxidant capacities of tart cherry products. Food Chemistry, 115, 20–25. Kwee, E. M., & Niemeyer E. D. (2011). Variations in phenolic composition and antioxidant properties among 15 basil (Ocimum basilicum L.) cultivars. Food Chemistry, 128, 1044–1050. Mikami-Konishide, I., Murakami, S., Nakanishi, K., Takahashi, Y., Yamaguchi, M., & Shioya, T., et al. (2013). Antioxidant capacity and polyphenol content of extracts from crops cultivated in Japan, and the effect of cultivation environment. Food Science and Technology Research, 19, 69–79. Rodtjer, A., Skibsted, L. H., & Andersen, M. L. (2006). Antioxidative and prooxidative effects of extracts made from cherry liqueur pomace. Food Chemistry, 99, 6–14. Saavedra, J., Cordova, A., Galvez, L., Quezada, C., & Navarro, R. (2013). Principal component analysis as an exploration tool for kinetic modeling of food quality: A case study of a dried apple cluster snack. Journal of Food Engineering, 119, 229–235. Socha, R., Juszczak, L., Pietrzyk, S., Galkowska, D., Fortuna, T., & Witczak, T. (2011). Phenolic profile and antioxidant properties of polish honeys. International Journal of Food Science and
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Technology, 46, 528–534. Toydemir, G., Capanoglu, E., Kamiloglu, S., Boyacioglu, D., de Vos, R. C. H., Hall, R. D., & Beekwild, J. Changes in sour cherry (Prunus cerasus L.) antioxidants during nectar processing and in vitro gastrointestinal digestion. Journal of Functional Foods, 5, 1402–1413. Tagliazucchi, D., Verzelloni, E., Bertolini, D., & Conte, A. (2010). In vitro bio-accessibility and antioxidant activity of grape polyphenols. Food Chemistry, 120, 599–606. Verzelloni, E., Tagliazucchi, D., & Conte, A. (2007). Relationship between the antioxidant properties and the phenolic and flavonoid content in traditional balsamic vinegar. Food Chemistry, 105, 564–571. Yoo, K. M., Al-Farsi, M., Lee, H., Yoon, H., & Lee, C. Y. (2010). Antiproliferative effects of cherry juice and wine in Chinese hamster lung fibroblast cells and their phenolic constituents and antioxidant activities. Food Chemistry, 123, 734–74.
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Figure Captions Fig. 1. Ultra-performance liquid chromatography-mass spectrometry separation of phenolic acid standards. Fig. 2. Principal component analysis scores for the nine cherry wine samples. Fig. 3. Dendrogram constructed with minimum linkage method for the nine cherry wine samples.
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Fig. 1. Ultra-performance liquid chromatography-mass spectrometry separation of phenolic acid standards. Peaks 1, 2, 3, 4, and 5 were identified as gallic acid (GAE), p-hydroxybenzoic acid (PHB), chlorogenic acid (CHL), vanillic acid (VAN), and caffeic acid (CAF), respectively
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Fig. 2. Principal component analysis scores for the nine cherry wine samples.
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Fig. 3. Dendrogram constructed with minimum linkage method for the nine cherry wine samples.
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Table 1 Variety, cultivar, total acid, and pH information of nine cherry wine samples. Sample
Variety
Cultivar
code
Total acid
pH
(g/L)
W1
Chinese cherry
Shandong
5.52
3.37
W2
Chinese cherry
Shandong
5.67
3.49
W3
Sweet cherry
Shandong
7.59
3.73
W4
Sweet cherry
Jiangsu
7.97
3.70
W5
Sweet cherry
Jiangsu
7.74
3.68
W6
Sweet cherry
Jiangsu
7.97
3.69
W7
Sweet cherry
Jiangsu
7.82
3.79
W8
Sweet cherry
Jiangsu
7.90
3.68
W9
Chinese cherry
Jiangsu
9.99
3.37
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Table 2 Total phenolic (TP), total flavonoid (TF), total anthocyanin (TA), total tannin (TT) contents and antioxidant activity (AA) of nine cherry wine samples*. Sample
TP
TF
TA
TT
AA-EC50
(mg GAE /L)
(mg/L)
(mg catechin/L)
(mg/L)
(mL of wine)
W1
736.54 ±15.81a
2.15 ± 0.18a
1.67 ± 0.12a
183.65 ± 14.58a
0.17 ± 0.01c
W2
619.26 ± 13.20a
0.16 ± 0.01b
0.18 ± 0.01d
122.66 ± 10.32ab
0.42 ± 0.03a
W3
687.83 ± 12.32a
0.86 ± 0.08b
0.78 ± 0.05b
149.84 ± 12.48ab
0.28 ± 0.03b
W4
261.45 ± 8.45b
0.69 ± 0.06b
0.48 ± 0.02c
90.18 ± 8.59b
0.19 ± 0.01bc
W5
268.17 ± 9.69b
0.55 ± 0.03b
0.54 ± 0.04cb
85.21 ± 7.43b
0.28 ± 0.02d
W6
262.29 ± 10.27 b
0.53 ± 0.05 b
0.55 ± 0.05 cb
92.83 ± 9.04 b
0.24 ± 0.03 d
W7
235.53± 11.05 b
0.44 ± 0.02 b
0.44 ± 0.02 cd
97.81 ± 9.37 b
0.21 ± 0.01 cd
W8
296.57 ± 10.16 b
0.50 ± 0.03 b
0.50 ± 0.03c
120.01 ± 11.52 ab
0.35 ± 0.03 c
W9
616.08 ± 16.25 a
0.12±0.01 b
0.21 ± 0.01 d
6.66 ± 0.39 c
0.20 ± 0.01 d
code
* Mean scores for each attribute within a column with different letters are significantly different (p < 0.05) using Duncan’s multiple comparison tests.
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Table 3 Five individual phenolic acids of nine cherry wine samples* (mg/L). Sample
GAE
PHB
CHL
VAN
CAF
W1
0f
0d
0c
40.34 ± 3.47 a
51.80 ± 4.83 c
W2
35.02 ± 2.85 b
12.81 ± 1.09 b
13.22 ± 1.14 b
5.07 ± 0.38 d
101.79 ± 10.04 b
W3
83.55 ± 7.38 a
50.88 ± 4.62 a
21.31 ± 1.92 a
40.76 ± 3.68 a
135.93 ± 12.58 a
W4
9.86 ± 0.85 e
0.26 ± 0.02 d
1.30 ± 0.11 c
8.87 ± 0.62 c
4.35 ± 0.32 e
W5
7.26 ± 0.67 e
0.25 ± 0.02 d
0.76 ± 0.05 c
3.01 ± 0.26 d
2.55 ± 0.14 e
W6
19.24 ± 1.65 d
4.24 ± 0.38 c
22.21 ± 2.07 a
11.85 ± 1.09 b
0.70 ± 0.06 e
W7
30.02 ± 2.89 c
0.28 ± 0.02 d
12.41 ± 1.07 b
3.75 ± 0.31 d
1.32 ± 0.11 e
W8
6.57 ± 0.53 e
0.31 ± 0.02 d
1.09 ± 0.10 c
4.38 ± 0.37 d
0.30 ± 0.02 e
W9
7.84 ± 0.69 e
1.05± 0.01 d
0c
0e
13.27± 1.28 d
code
* Mean scores for each attribute within a column with different letters are significantly different (p < 0.05) using Duncan’s multiple comparison tests.
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Highlights
A HPLC-DAD/ESI-MS method was developed for analysis of phenolic acids in cherry wine.
The phenolic compounds in cherry wines were closely related to cultivar and variety.
Cultivar had more influence on the phenolic compounds of cherry wines than variety.
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