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Scientia Horticulturae 165 (2014) 156–162

Contents lists available at ScienceDirect

Scientia Horticulturae

journa l h om epa ge: www.elsev ier .com/ locate /sc ihor t i

omological and biochemical characterization of European curranterry (Ribes sp.) cultivars

oban Djordjevic a,∗, Vera Rakonjaca, Milica Fotiric Aksic a, Katarina Savikinb, Todor Vulic a

University of Belgrade, Faculty of Agriculture, Belgrade, SerbiaInstitute for Medicinal Plants Research “Dr Josif Pancic”, Belgrade, Serbia

r t i c l e i n f o

rticle history:eceived 25 June 2013eceived in revised form 29 October 2013ccepted 13 November 2013

eywords:enetic resources

a b s t r a c t

Descriptive and multivariate statistic analysis was used to determine phenotypical diversity among 29European currant cultivars. Variation in 39 traits related to phenology, vegetative and reproductive poten-tial, fruit quality, content of antioxidants in berries, and oils in seeds indicated a high diversity of currantcultivars. PCA analysis showed high discrimination capabilities of variables measured. The most impor-tant traits for cultivars grouping were related to yield efficiency, fruit quality, and content of bioactivecompounds. These traits should be taken into account in the further currant cultivars characterization

ieldruit qualityCAluster analysis

and analysis of breeding material. Both, cluster (CA) and principal component analysis (PCA), showed anadequate grouping of cultivars in two groups of cultivars, one consisting of black and the second groupcomprising the red and white currant cultivars. By PCA, within each of these two major groups dispersedlayout of cultivars was obtained, while using CA a larger number of sub-clusters was separated indi-cating phenotypic and thus genotypic heterogeneity of studied cultivars. Great diversity in this currant

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collection showed its goo

. Introduction

Currants are botanically classified in the genus Ribes L. belongingo Grossulariaceae family. The Ribes genus includes more than 150pecies, where commercially the most important are Ribes nigrum., ancestor of black currants and Ribes rubrum L., ancestor of rednd white currant cultivars. In global production of berry fruits,urrants are ranked immediately after strawberries (Ratundo et al.,998). Currants are an important fruit crop produced commercially

n moderate temperature regions encompassing many countriesBrennan, 2008). Compared with other fruit types, currant has mod-st requirements in terms of growing, and allows for a rapid returnn investment and profitability.

The currants fruit has been used for centuries both as a food ands medication. The fruits have beneficial effects to human healthecause of their high contents of bioactive compound and antiox-

dants (Go –devac et al., 2011). Contents of vitamin C in currantserries far outweigh the contents of this antioxidant compared

ith other berry fruits (Benvenuti et al., 2004; Ciornea et al.,

009). Also, its fruits are important source of flavonoids, espe-ially anthocyanins, flavonols, and other polyphenolic compounds

∗ Corresponding author at: University of Belgrade, Faculty of Agriculture, Neman-ina 6, Belgrade, Serbia. Tel.: +381 112199805; fax: +381 112199805.

E-mail address: b.djordjevic@agrif.bg.ac.rs (B. Djordjevic).

304-4238/$ – see front matter © 2013 Elsevier B.V. All rights reserved.ttp://dx.doi.org/10.1016/j.scienta.2013.11.014

ential for fresh and processing usage, and further breeding programs.© 2013 Elsevier B.V. All rights reserved.

(Tabart et al., 2006; Djordjevic et al., 2010; Milivojevic et al., 2012).Currants seeds contain oil which is rich in essential polyunsaturatedfatty acids and tocopherols (Goffman and Galletti, 2001). In thisregard, besides standard goals related to phenology, vigor, yield,resistance to major pests and diseases, the breeding efforts, espe-cially in black currants, is now focused on the production of fruitwith elevated levels of nutritional components.

The importance of genetic diversity is obvious, since it is esti-mated to be beneficial or even crucial to a breeding program. Theavailability and informative value of plant germplasm are becomingmore and more important for the future preservation and sustain-able use of genetic resources. Khadivi-Khub et al. (2012) stressedthe relevance of morpho-phenological variability in the identifica-tion and breeding programs of cultivars, and such analysis shouldbe made before molecular studies are carried out. Phenotypicalcharacterization of plant genetic resources, whether it is a collec-tion of cultivars, hybrids or accessions from natural populationsusually involves a wide range of data which include both qual-itative and quantitative traits. Such data sets are generally largeand multivariate, with a considerable number of descriptors mea-sured for each of many genotypes (Lacis et al., 2010). In agriculturalsciences, the application of multivariate statistics is fundamental,

and the most used techniques are the principal component analysis(PCA) and cluster analysis (CA). PCA is mostly applied to reduce thenumber of input variables (Uno et al., 2005) while CA is used to sortsamples into groups.

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The objective of this study was to describe the variability in theurrant collection, composed of 29 European cultivars, from thespect of phenology, vegetative, reproductive, and chemical prop-rties; to identify the most useful traits for discrimination and toetect associations among cultivars. Additionally, the assessed cul-ivars can be a source of valuable traits for future breeding effortsnd sustainable fruit growing in Serbian, or similar, agro-ecologicalonditions.

. Material and methods

.1. Plant material

Investigations were carried out in a currant collection orchard,he property of the “Omega” nursery (Mislodjin village, near Bel-rade). The experimental field was situated between 44◦ 30′ and4◦ 45′ N and 20◦ and 20◦ 20′ E, at an altitude between 80 and0 m. Investigations comprised 29 European currant cultivars (13lack, 11 red, and 5 white currant cultivars) presented in Table 1.he bushes were under non-irrigated standard cultural practices.he experiment was set as a randomized complete block designith five replicates. Each replicate was presented with five bushes.

o minimize environmental effects on all studied traits data wereollected over three consecutive years (2007–2009).

.2. Pomological evaluation

During studies, numerous phenological parameters were mon-tored. The dates of bud burst and inflorescence emergence wasonsidered when vegetative and reproductive bud was opened,espectively. The beginning and the full bloom was considered ashe day when approximately 10 and 80% of the flowers, respec-ively, were open. The date of the first formed berry was notedhen the first berry in the bunch was set (diameter of formed berryas 3 mm). Harvesting time was done when over 90% of berries in a

unch were technologically mature. For statistical analysis, all phe-ological traits were represented as the number of days counting

rom January 1st.Another group of traits examined in this study was vegetative

rowth potential of currant cultivars. Bush height and width waseasured by ruler in m, respectively. Number of shoots per bushas counted. Total shoot length was obtained as the sum of lengths

f all shoots in the bush. Average shoot length was calculated asotal shoot length/number of shoots. Bush volume was calculatedccording to the formula:

= � × H

3× (R2 + R + r + r2)

here Н is the bush height (m), R is the radius of the bush at theop (m), and r is the radius of the bush at the base (m).

Traits that represent generative potential were also analyzed.raits such as number of inflorescences per bush, number of flowerser inflorescence, and number of berries per bunch were counted.ll bunch observations were made on 30 bunches sampled ran-omly from all the positions of the shoots. Bunch and berry weightas scale-weighed and expressed in g, whereas bunch length waseasured with a ruler and expressed in cm. Fruit set (number of

ruits per number flowers) was determined a week before harvest-ng time and expressed as percentage. Yield per bush was presenteds kg bush.

.3. Biochemical evaluation

Studies of currant fruit and seed characterization involved theollowing compounds from a group of primary (soluble solid con-ent, total aciditivity, and ascorbic acid) and secondary metabolites

ulturae 165 (2014) 156–162 157

(contents of total phenols, total anthocyanins, and individual agly-cones of the anthocyanins) as well as quantitative and qualitativeanalysis of seed oils. Soluble solids content was determined byrefractometer (Atago, pocket PAL-1. Kyoto, Japan). Titratable acid-ity was determined by titrating with 0.1 N NaOH up to pH 7.0,and expressed as percent of malic acid. An iodometric titrationmethod was performed for the determination of ascorbic acid(Harris, 2000) and the results were expressed as mg/100 g FW. Thetotal concentration of phenols was estimated by Folin–Ciocalteu(Waterman and Mole, 1994), total anthocyanin content was inves-tigated according to the procedure described in European (2008),while quantitative analysis of anthocyanin aglycones was per-formed according to Nyman and Kumpulainen (2001), all withslight modifications done by Djordjevic et al. (2010). Oil and fattyacids content analysis was done according to Savikin et al. (2013),and converted to fatty acid methyl esters (FAMEs) followed byTraitler et al. (1984).

2.4. Statistical analysis

The data resulting from the 3-year study (2007–2009) weregrouped, and the average values were used for statistical analysis.The method of statistical analysis involved descriptive statistic andmultivariate methods such as principal component analysis (PCA)and cluster analysis (CA). Following descriptive statistic parameterswere evaluated: mean, minimum value, maximum value, range,standard deviation (SD), and coefficient of variation (CV%). PCAwas used to identify the patterns of multi-trait variation in the col-lection where values of the six PCs were compared for each trait.The Ward’s method as agglomeration rule and the Euclidean dis-tance as a measure of dissimilarity were carried out to classify thecultivars into homogenous groups by CA. Statistical analyses wereperformed using Statistica for Windows, version 5.0 (StatSoft Inc.,Tulsa, OK) statistical package.

3. Results and discussion

3.1. Pomological and biochemical characterisation

The descriptive statistical analysis values for each of the studiedtraits are reported in Table 2. Differences were observed in the max-imum and minimum values and in the max/min ratio for all traits.In particular, the highest max/min value ratio was observed by bushweight rejected by pruning (8.4) and by total acidivity (7.2). In con-trast, the lowest max/min ratio was displayed in traits related tophenology (1.2–1.4) where the lowest variability based on CV val-ues (3.6–10.9%) was also determined. The tested currant cultivarsshowed moderate to high variability in relation to the vigor charac-teristics, while traits with the highest CV were bush weight rejectedby pruning and bush volume, 50.1 and 48.2%, respectively. The stud-ied collection include that cultivars differ in bunch weight, (rangedfrom 6.5 to 15.1 g), berry weight (ranged from 0.44 to 1.85 g), andyield (ranged from 0.55 to 3.26 kg/bush). Moderate variation wasattributed to the yield components such as number of flowers perinflorescence, number of berries per bunch, number of bunchs perbush, and yield having CV 40.6, 42.2, 33.5, 39.1%, respectively.).

Except for soluble solid content (CV = 14.3%) the differencesbetween currant cultivars were observed in relation to the con-tent of bioactive substances in the fruit as indicated by the valueof max/min ratio ranged between 3.8 and 7.2 and CV values higherthan 45.1%. Very large variations in the total anthocyanins content

(CV = 103.9%) is caused by the fact that white currant cultivars donot contain these components and at the same time black currantcultivar were characterized by its high content. The data obtainedfrom our study indicated that seeds representing residue product

158 B. Djordjevic et al. / Scientia Horticulturae 165 (2014) 156–162

Table 1A list of analyzed currant cultivars, their pedigree, and geographic origin.

Cultivar Pedigree Country of origin Species

Black currantBen Sarek Goliath × Ojebyn Scotland R. nigrumBen Nevis (Brödtrop × Janslunda) × (Consort × Magnus) Scotland Interspecies hybridBen Lomond (Brödtrop × Janslunda) × (Consort × Magnus) Scotland Interspecies hybridBona Öjebyn × S/12 (Ribes dikuscha × Climax) Poland Interspecies hybridOmeta Westra × R. nigrum Switzerland R. nigrumTenah (Goliath × R.n.) × R.n.) × Brödtorp The Netherlands R. nigrumSilmu Origin unknown The Netherlands R. nigrumTsema (Goliath × R.n.) × R.n.) × Brödtorp The Netherlands R. nigrumMalling Juel Origin unknown England R. nigrumCacanska crna Seedling of Malling Jet Serbia R. nigrumOjebyn Origin unknown Sweden R. nigrumTitania Altajskaja Desertnaja × (Consort × Kajaanin Musta) Sweden R. nigrumTriton Altajskaja Desertnaja × (Consort × Kajaanin Musta) Sweden R. nigrumRed currantJunifer Seedling of Fay’s Prolific France R. rubrumJonkheer van tets Fay’s Prolific × Scotch The Netherlands R. rubrumRolan Jonkheer van tets × Rosetta The Netherlands R. rubrumRondom Multiple crossing The Netherlands Interspecies hybridRovada Fay’s Prolific × Heinemann’s Rote Spatlese The Netherlands R. rubrumStanza Origin unknown The Netherlands R. rubrumMirana Origin unknown The Netherlands R. rubrumMakosta Origin unknown The Netherlands R. rubrumRedpoll Red Lake × (R. langeracemosum × R. multiflorum) England Interspecies hybridSlovakia Origin unknown Slovakia R. rubrumLondon Market Origin unknown England R. rubrumWhite currantPrimus Heinemann’s Rote Spatlese × Red Lake Slovakia R. rubrumWhite champagne Origin unknown France R. rubrumWeisse aus Juteburg Mutant of Red Dutch Switzerland R. petraeumWitte Parel Origin unknown The Netherlands R. rubrum

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Victoria Heinemann’s Rote Spatlese × Red Lake

n fruit processing is a good source of oil and essential fatty acidshich is in agreement with Bakowska-Barczak et al. (2009). Con-

iderable variation in relation to the content of these componentsmong cultivars studied herein was established based on the valuesf the coefficients of variation (41.3–52.6%).

In general, the least variation among the studied currant culti-ars was manifested in relation to phenology, medium to the vigor,nd yield components but the highest in relation to the chemicalomposition of the fruit. According to Petruccelli et al. (2013) char-cteristics with a low CV are more homogeneous and repeatablemong cultivars, while characteristics with high CV values are moreiscriminating, and can be reliable markers for the characteriza-ion. Also, obtained results indicating the expressed variability for

ost properties demonstrate that the currant collection possessesultivars for both breeding and growing purposes.

.2. Principal component analysis - PCA

PCA used to identify the most significant variables in the dataet produced six principal components with eigenvalues greaterhan 1 (Table 3). The first principal component concentrated 41.7%f total variance, the second 15.4%, the third 12.6%, the fourth 6.3%,he fifth 5.3%, and the sixth 3.9%. On the base of correlation betweenhe original variables and these six PCs shown in Table 3, usingn absolute value greater than 0.70 as a criterion for the signifi-ance shows that these values are present only in the first four PCs.hese components are enough to explain 76.0% of the total vari-bility, having high discriminating power. This is supported by the

act that the variance explained by a single PC decreased stronglyetween PC1 and PC4, but from PC4 to PC6 much slower. Accord-

ng to Petruccelli et al. (2013), this means that we are approachinghe level of noise variance. For this reason grouping of cultivars by

Czech Republic R. rubrum

cluster analysis was performed based on 19 properties that had thegreatest impact in the first four PCs (Table 3).

Traits with higher scores on PC1, which explains the largest pro-portion of variability, are related to some phenological phases (timeof bud burst and inflorescence emergence), berry and bunch size(bunch length and berry weight), number of flowers per inflores-cence, and number of berries per bunch. Besides that, most of theparameters related to the content of bioactive components in fruitstogether with oils and fatty acid contents in seeds were also highlycorrelated with PC1. PC2, that represents the second most impor-tant factor, was negatively correlated to the number of bunches perbush (r = −0.894) and yield (r = −0.703). These results could sug-gest that the right number of bunches per bush can be consideredas the most important component in currants. PC3 was correlatedonly with bush volume (r = 0.707) and PC4 only with bunch weight(r = −0.722) suggesting that this trait was genetically affected byindependent genes not showing pleiotropic effects.

Our results corresponding those of Pluta et al. (2012) and Krügeret al. (2011) who found that the variables corresponding to pheno-logy, yield, and berry weight had high correlations to PC1 or PC2 indifferent black currant collections. According to Höfer et al. (2012),besides yield, plant habit and fruit size, the number of flowersper inflorescence and number of inflorescences per plant had amajor impact on discrimination of strawberry cultivars which isconsistent with our results. Also, similar to our findings significanteffect of bioactive components content, primarily total phenolics,anthocyanins, and ascorbic acid on the discrimination of cultivarswas determined by Bordonaba and Terry (2008) and Krüger et al.(2011) in currants, Milosevic et al. (2012) in blackberries, and Park

et al. (2012) in black raspberry. This indicates that these traitscould be sufficient for reliable characterization of different currantgermplasm collections. According to Zeinali et al. (2009), the esti-mated PCs reveal the relationships among the traits and how the

B. Djordjevic et al. / Scientia Horticulturae 165 (2014) 156–162 159

Table 2Descriptive statistical analysis of 34 agronomical and biochemical characteristics evaluated in 29 currant cultivars, during a period of three years.

Traits Mean Mina Maxb Range Max/min SDc CVd

Time of bud burste 72 61 85 24 1.4 7.9 10.9Time of inflorescence emergencee 84 76 97 21 1.3 4.5 5.3Beginning of bloominge 89 79 99 20 1.3 4.3 4.8Full bloome 101 94 111 17 1.2 4.0 4.0Time of the first berry formede 105 95 112 17 1.2 3.7 3.6Harvesting timee 172 160 185 25 1.2 6.6 3.8Bush height (m) 1.01 0.77 1.33 0.56 1.7 0.158 15.5Bush width (m) 1.01 0.72 1.58 0.86 2.2 0.213 20.4No. of shoots per bush 5.4 2.3 10.9 8.6 4.7 2.031 38.0Shoot length (cm) 69.8 42.3 101.6 59.3 2.4 7.44 25.0Total shoot length (cm) 370.8 125.0 692.2 566.8 5.5 152.22 41.0Bush volume (cm2) 0.45 0.20 1.14 0.94 5.7 0.209 48.2Bush weight rejected by pruning (g) 313.4 77.1 650.2 573.1 8.4 157.11 50.1Bunch weight (g) 9.3 6.5 15.1 8.6 2.3 2.17 23.4Bunch length (cm) 8.2 4.6 12.8 8.2 2.8 2.58 31.4No. of flowers per inflorescence 15.4 5.9 30.2 24.3 5.1 6.25 40.6No. of berries per bunch 12.6 4.4 25.5 21.1 5.8 5.33 42.2Fruit set (%) 81.4 64.6 94.3 29.7 1.5 7.14 8.8Berry weight (g) 0.88 0.44 1.85 1.41 4.2 0.334 38.2No. of bunches per bush 181.3 88.7 331.9 243.2 3.7 60.70 33.5Yield (kg/bush) 1.65 0.55 3.26 2.71 5.9 0.536 39.1Soluble solids (%) 12.6 9.7 18.2 8.5 1.9 1.80 14.3Total aciditivity (%) 5.3 1.3 9.4 8.1 7.2 3.25 60.9Ascorbic acid (mg/100 g FW) 96.0 45.8 172.2 126.4 3.8 46.85 48.8Total phenols (mg GAE/100 g FW) 137.3 60.2 278.9 218.7 4.6 62.0 45.1Total anthocyanins (mg/100 g FW) 36.4 0.0 135.4 135.4 – 37.9 103.9Delphinidin (mg/100 g FW) 16.0 0.0 53.5 53.5 – 15.7 97.9Cyanidin (mg/100 g FW) 12.3 0.0 40.9 40.9 – 9.92 80.3Oil content (%) 17.0 5.4 27.7 22.3 5.1 7.01 41.3Methyl linoleate (%) 40.6 24.9 46.9 22.0 1.9 5.01 12.3Methyl linolenate (%) 21.2 4.8 32.0 27.2 6.7 7.15 33.7Methyl oleate (%) 14.6 9.9 19.9 10.0 2.0 3.03 20.8Methyl octadecatrienoate (%) 8.9 3.3 18.5 15.2 5.6 4.71 52.6Methyl palmitate (%) 6.1 4.4 9.8 5.4 2.2 1.30 21.3

a Minimum value.b Maximum value.

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Fig. 1. Relationships among 29 currant cultivars shown in two-dimensional scatter

c Standard deviation.d Coefficient of variation expressed in percentage.e No. of days from January 1st.

haracters that affect each other could be beneficial to the breedersn their breeding programs.

The scatter plot of the first two principal components (Fig. 1)hows separation of cultivars into two groups. First one is consistedf black currant cultivars (on the right side of the diagram), and theecond group of red and white currant (on the left side of the dia-ram), which is fully consistent with the results Milivojevic et al.2012). Dispersed arrangement of cultivars within each of thesewo groups indicates the phenotypic and therefore genotypic het-rogeneity. Great diversity in this currant germplasm collection ishowing great potential for improving the agronomic traits of thepecies.

.3. Cluster analysis - CA

Hierarchical cluster analysis was used in order to divide theultivars into groups of increasing dissimilarity. Ward’s criterionas applied to sets of 19 traits selected on the basis of the PCA

nd obtained dendrogram is shown in Fig. 2. Currant cultivarsre connected in different ways, which show existence of numer-us hierarchical levels. In general, grouping of cultivars accordingo cluster analysis are in good agreement with the results of therst two principal components. Cluster analysis identified twoain clusters. Cluster I consisted of black and Cluster II com-

rised the red and white currant cultivars. Each of these twolusters was split off, into three distinct sub-clusters. The thir-een black currant cultivars were grouped into sub-clusters IA,B, and IC had each 5, 4, and 4 cultivars, respectively. In the

diagrams of the first respective principal components obtained for the 34 traits(respective signs denote cultivars assigned to sub-clusters identified by Ward’scluster analysis and shown as (�)–IA, (�)–IB, (�)–IC, (♦)–IIA, (©)–IIB, (�)–IIC.

160 B. Djordjevic et al. / Scientia Horticulturae 165 (2014) 156–162

Table 3Eigenvalues and proportion of the total variance for the 34 characteristics in 29 currant cultivars and the correlation coefficients between traits and the first six PCs.

Traits PC1 PC2 PC3 PC4 PC5 PC6

Time of bud burst −0.946a −0.018 0.146 0.117 0.127 0.026Time of inflorescence emergence −0.707a 0.402 0.329 0.307 0.189 −0.048Beginning of blooming −0.653 0.512 0.360 0.189 0.286 −0.067Full bloom −0.439 0.667 0.289 0.265 0.367 0.007Time of the first berry formed −0.522 0.550 0.474 0.223 0.241 −0.095Harvesting time −0.240 0.453 0.653 −0.122 0.115 0.151Bush height 0.256 −0.651 0.600 0.196 0.018 −0.087Bush width 0.499 −0.354 0.674 −0.024 0.023 0.064No. of shoots per bush 0.234 −0.060 −0.646 0.127 0.579 0.253Shoot length 0.472 −0.656 0.454 0.226 0.099 −0.043Total shoot length 0.464 −0.404 −0.371 0.265 0.531 0.218Bush volume 0.443 −0.453 0.707a −0.005 0.040 −0.017Bush weight rejected by pruning 0.535 −0.502 0.329 0.389 0.330 0.042Bunch weight −0.423 0.154 0.064 −0.722a 0.481 0.064Bunch length −0.803a −0.198 0.373 −0.210 0.123 0.141No. of flowers per inflorescence −0.858a −0.203 0.230 −0.172 0.003 0.229No. of berries per bunch −0.845a −0.139 0.270 −0.296 0.038 0.148Fruit set −0.110 0.235 0.227 −0.585 0.144 −0.331Berry weight 0.702a 0.262 −0.357 −0.106 0.391 −0.177No. of bunches per bush 0.049 −0.894a 0.105 −0.114 0.008 −0.211Yield −0.214 −0.703a 0.165 −0.444 0.330 −0.174Soluble solids 0.666 0.424 0.431 0.091 −0.058 0.008Total aciditivity −0.954a −0.219 −0.002 0.009 −0.066 0.087Ascorbic acid 0.918a 0.194 0.060 −0.094 0.091 −0.081Total phenols 0.827a 0.190 0.319 −0.104 −0.068 0.090Total anthocyanins 0.835a 0.266 0.339 −0.010 −0.075 0.093Delphinidin 0.846a 0.348 0.192 −0.053 −0.092 0.083Cyanidin 0.577 0.305 0.217 −0.339 −0.274 0.252Oil content 0.338 −0.132 −0.273 −0.380 0.152 0.163Methyl linoleate 0.525 0.005 0.102 −0.198 0.055 0.580Methyl linolenate −0.871a −0.132 0.124 0.054 −0.157 0.328Methyl oleate −0.806a −0.196 −0.088 0.086 −0.098 −0.257Methyl octadecatrienoate 0.887a 0.027 0.139 0.096 0.131 0.129Methyl palmitate 0.716a 0.271 0.136 −0.191 0.146 −0.445Eigenvalue 14.179 5.240 4.298 2.138 1.817 1.332

fiwcr

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% Cumulative variance 41.7 57.1

a Correlation coefficients with value greater than 0.7 in absolute value.

rst subgroup (IIA) of cluster II, two cultivars of red and onehite currant cultivar were separated. The second subgroup (IIB)

onsisted predominantly of white currant (four cultivars) and twoed currant cultivars. Sub-cluster IIC is the largest and it included

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69.7 76.0 81.3 85.2

seven red currant cultivars. Separation into sub-clusters showedlittle association between cultivars from the same geographicorigin. Within each of the selected groups are cultivars originat-ing from different European countries. Also, the separation into

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ultivars using 19 traits allocated on the basis of PCA (results presented in Table 3).

B. Djordjevic et al. / Scientia Horticulturae 165 (2014) 156–162 161

Table 4Mean comparison by t-test of 19 traits used for cluster analysis for the distinguished groups.

Traits I(C) IA IB IC II(B) IIA IIB IIC

Time of bud burst 65* 66 65 63 78* 74 79 79Time of inflorescence emergence 81* 81 83 80 86* 82 85 88Bush volume 0.52 0.43** 0.37** 0.78** 0.38 0.41** 0.48** 0.29**

Cluster weight 8.55 8.26 9.07 8.39 9.86 8.57 9.08 11.08Cluster length 6.0* 5.8 5.5 6.9 9.9* 9.5 10.0 10.1Flowers/inflorescence 9.5* 9.4 8.4 10.8 20.2* 18.7 20.0 21.0Berries/cluster 7.9* 7.6b 7.1** 9.0** 16.5* 16.0 15.8 17.2Berry weight 1.16* 1.18 1.31 0.98 0.65* 0.59 0.63 0.70Clusters/bush 168.8 171.6** 119.1** 215** 191.5 298.7** 210.4** 129.3**

Yield 1.40 1.38** 1.07** 1.77** 1.80 2.53** 1.94** 1.47**

Total aciditivity 1.8* 1.8 1.8 1.9 8.2* 7.6 8.2 8.4Ascorbic acid 145.5* 153.9 134.1 146.3 55.7* 60.6 55.3 54.0Total phenols 192.6* 169.2** 162.5** 251.9** 92.5* 106.5 84.9 92.9Total anthocyanins 70.6* 52.9** 63.0** 100.3** 8.7* 9.7** 4.3** 11.9**

Delphinidin 31.2* 23.4** 35.1** 37.2** 3.6* 3.5** 1.9** 5.2**

Methyl linolenate 14.2* 12.7 14.8 15.3 26.9* 25.3 25.6 28.8Methyl oleate 12.0* 12.4 12.4 11.0 16.7* 16.3 17.7 16.3Methyl octadecatrienoate 13.3* 12.0 13.3 14.8 5.4* 6.8 5.7 4.6Methyl palmitate (%) 7.3* 7.8 6.5 7.4 5.2* 4.9 5.1 5.4

cluste

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Numbers of cultivars 13 5 4

* Significant different means (p < 0.05) between clusters.** Significant different means (p < 0.05) between sub-clusters within cluster I and

ub-clusters was not in a function of cultivar pedigree. For exam-le, cultivars Ben Nevis and Ben Lomond, obtained from the samerossing combinations were classified into different groups. Theame conclusion is related to the cultivars Tenah and Silmu, Tita-ia and Triton, and Primus and Victoria. Classification of cultivar

with similar geographic origins and similar pedigree in differentroups confirming the wide variability was detected in the studiedurrant cultivars. Similar findings have been reported by Pluta et al.2012) and Madry et al. (2010) in black currant.

The assignment of cultivars grouped to each of two clusters andix sub-clusters is shown in Table 4. Traits heading high values oforrelation coefficients with PC1 showed the greatest influence onhe formation of the two main clusters in cluster analysis. Sig-ificant differences (p ≤ 0.05) among clusters were detected forhenological attributes (time of bud burst and inflorescence emer-ence), bunch length, number of flowers per inflorescence, numberf berries per bunch, berry weight and for the most characteristicselated to content of bioactive compounds. This indicates that blackurrant cultivars on one side and the red and white currants onhe other side are two different gene pools. Black currant cultivarsave better quality (large berries and high content of health relatedompound), while red and white currant cultivars are characterizedith later vegetation start and better yield efficiency. The traits thatere highly correlated with PC2 and PC3 (number of bunches per

ush and yield), made a clear separation of sub-clusters within bothlusters. Besides that, between sub-clusters within cluster I, a sig-ificant difference of number of berries per bunch, total phenols,otal anthocyanins, and delphinidin content was detected. Signif-cant differences in the contents of anthocyanins and delphinidinetween sub-clusters in the cluster II must be interpreted with cau-ion as in the first and the second sub-cluster white currant cultivarshat contain no anthocyanins were distributed. Thus, the cultivarsncluded in the diverse clusters and sub-clusters could be used asromising parents for hybridization to obtain heterotic response ofraits (Brennan and Gordon, 2002).

There were some mismatches in the distribution of cultivars inub-clusters detected by PCA and CA (Figs. 1 and 2) PCA did notifferentiate cultivars between IA and IB sub-cluster and it formedne disperse group with a random distribution. This suggests that

ub-clusters IA and IB might be merged into one group. Cultivars ofub-cluster IC are very well separated from other in cluster I; thenly exception is the cultivar Ometa. Cluster II divided into threeubgroup by both methods. The only exception is observed in the

4 16 3 6 7

r II.

classification of the cultivars Jonkheere van Tets (sub-cluster IIC,by cluster analysis) and London Market (sub-cluster IIA, by clusteranalysis) which in the PCA ordination showed a closer similarity tosub-cluster IIB. Some discrepancy between currant cultivars group-ing in cluster analysis and PCA that could be explained by the factthat PCA takes into account PC1 and PC2 ordination which repre-sents only 56% of variability, whereas in the cluster analysis usesthe whole variability.

Among black currant cultivars, the best characteristics showedcultivars Ben Lomond, Malling Juel, Ometa, and Tsema distributedin sub-cluster IC (Table 4). On average, those cultivars are char-acterized by the largest bush volume (0.78), the largest numberof bunches per bush (215), the highest yield (1.77 kg/bush), aswell as the highest content of total phenols (251.9 mg GAE/100 gFW), and total anthocyanins (100.3 mg/100 g FW). Within clus-ter II red currants Junifer and London Market and white currantsPrimus, separated in sub-cluster IIA, had the best performanceespecially in relation to the number of clusters per bush (297.7),yield (2.53 kg/bush) but also to ascorbic acid (60.6 mg/100 g FW),and total phenols (106.5 mg GAE/100 g FW) content. As such, thementioned cultivars can be recommended for cultivation in eco-logical conditions similar to our own.

4. Conclusion

The great phenotypic diversity was detected in studied currantcollection. Many fruit characteristics that are potentially importantto breeders and growers are present in this collection indicatingthat this germplasm collection could be a good source for thispurpose. Using the PCA, highly discriminating traits were identi-fied which were the traits related to yield efficiency, fruit quality,and content of bioactive compound. Because of that high contentin antioxidants (ascorbic acid and phenolics) particularly in blackcurrant cultivars, should be implemented as a goal in breeding pro-grams, aimed to improve the health properties of berries. Usingboth CA and PCA, two large groups of cultivars were separated, oneconsisting of black and the second group comprising the red andwhite currant cultivars. Within each of these two major groups, dis-persed arrangement of cultivars was obtained by PCA, while using

a CA more sub-clusters were separated. The cultivars included inthe diverse sub-clusters could be used as complementary parentsfor hybridization and thus could contribute to currant breedingprogress.

1 Hortic

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Waterman, P., Mole, S., 1994. Analysis of Phenolic Plant Metabolites. Blackwell Sci-entific Publication, Oxford, pp. 16.

62 B. Djordjevic et al. / Scientia

Black currants Ben Lomond, Malling Juel, Ometa, and Tsema;ed currants Junifer and London Market; and white currants Primushowed the best characteristics and such seem to be promising forrowing in Serbian, or similar, agro-ecological conditions.

cknowledgment

The authors acknowledge their gratitude to the Ministry ofducation, Science and Technological Development of Serbia fornancial support, project number 46013.

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