Food Research International 64 (2014) 790–798

Contents lists available at ScienceDirect

Food Research International

j ourna l homepage: www.e lsev ie r .com/ locate / foodres

Improving the application of SSR polymorphism analysis coupled withLab-on-a-chip® capillary electrophoresis to assess food authenticity:Italian pigmented rice as case study

Cristiano Garino a, Maurizio Rinaldi a, Angelo de Paolis b, Fabiano Travaglia a,Jean Daniel Coïsson a, Marco Arlorio a,⁎a Dipartimento di Scienze del Farmaco & Drug and Food Biotechnology (DFB) Center, Università del Piemonte Orientale “A. Avogadro”, largo Donegani 2, 28100 Novara, Italyb Istituto di Scienze delle Produzioni Alimentari (ISPA)-CNR, Via Prov.le Monteroni, 73100 Lecce, Italy

⁎ Corresponding author. Tel.: +39 0321 375772; fax: +E-mail address: marco.arlorio@pharm.unipmn.it (M. A

http://dx.doi.org/10.1016/j.foodres.2014.08.0080963-9969/© 2014 Published by Elsevier Ltd.

a b s t r a c t

a r t i c l e i n f o

Article history:Received 3 April 2014Accepted 14 August 2014Available online 27 August 2014

Keywords:Pigmented riceSSRLab-on-a-chip®In-house developed algorithm

Genetic distances evaluated via SSR-based profiling can be usefully assessed by using capillary electrophoresis. Inorder to set up a method to distinguish pure Italian rice varieties from imported Asian blends, seven Italian ricegenotypes and seven uncharacterized rice samples coming from outside Italy were studied using a classical SSRpolymorphism analysis coupled with Lab-on-a-chip®microcapillary electrophoresis. A special algorithm for theelaboration of the raw outputs provided by the software was generated, thus overcoming the problems connect-ed to the instrument intrinsic limits of resolution. The results showed that even considering just the smallest ver-ifiable genetic distance between the employed samples, locally cultivated Italian rice varieties clusteredseparately from other foreign cultivars. Moreover, it was possible to clearly identify an artificial blend formedby Venere rice mixed with a black variety from Thailand, thus confirming the usefulness of this new post-analysis approach.

© 2014 Published by Elsevier Ltd.

1. Introduction

Italy is the main rice growing (Oryza sativa L.) country in Europe,with a paddy production of 1.582 million tonnes and a cultivated areaof about 246,500 ha (2012) (FAO database, http://faostat.fao.org),contributing for 50% of the total European rice production. Ninety-fourpercent of the rice fields are located in the north-western part of thePo Valley, in the regions of Piedmont and Lombardy, where the cultiva-tion of this crop was introduced in the fifteenth century (Mantegazzaet al., 2008). Although white rice is most commonly consumed, thereare several rice cultivars containing color pigments, usually called‘black’ and ‘red’ rice (the term ‘red rice’ is also commonly used to desig-nate aweedy rice). The color is visible when the grains are dehulled, butit can be removed by polishing to reveal the white endosperm. Duringthe past years varieties with white kernels were mostly selected, pre-sumably because of their preferred appearance, while rice with coloredcaryopsis can bemainly found among thewild species. The farming andconsumption of colored varieties is so far limited in Western countries,while it is more common in Asia, where traditional pigmented varietiesare particularly valued on local markets (Finocchiaro et al., 2007). Not-withstanding their low diffusion, in the recent years pigmented ricevarieties have received increased attention because of their antioxidant

39 0321 375621.rlorio).

properties. Several studies report that the consumption of colored ricecan promote the decrease of the oxidative stress and the simultaneousincrease of the antioxidant capacity, thus reducing the risk of develop-ing chronic diseases, like cardiovascular disease, type 2 diabetes andsome forms of cancer (Shao et al., 2011). Cultivation of pigmented ricevarieties represents still a very small slice of the Italian market, butwhat is interesting to underline is that instead of simply trying toadapt Asian varieties to our climate, local farmers engaged themselvesinto the production of real Italian colored rice cultivars, firstly by cross-ing black Asian breeds with historical white pericarp Italian cultivars,and then continuing the cross-breeding to generate new varieties. Asfor many other typical Italian food products, locally developed and pro-duced rice cultivars should be protected from imitations and commer-cial frauds: to date, three white pericarp rice varieties (‘Riso del Deltadel Po’, ‘Riso di Baraggia Biellese e Vercellese’ and ‘Riso Nano VialoneVeronese’) have already obtained a Protected designation of Origin,and although currently there are no pigmented cultivars that receivedsuch certification, the list of protected products increases every year.Some rice varieties differ in the shape and size of their grains, however,when the morphological diversity is minimal, cultivars with differentqualitative proprieties are often indistinguishable. Moreover, morpho-logical characteristics, included color, are affected by stage specific ex-pression and environmental effects. Finally, the morphologicalidentification proves to be unfeasible when the rice is ground into pow-der and added as ingredient in complex matrices. The development of

791C. Garino et al. / Food Research International 64 (2014) 790–798

methods to distinguish the rice varieties is useful to protect the con-sumers, in order to avoid fraudulent commercial activities.

Molecular markers, based on DNA sequences, are not affected by de-velopment stage, environment ormanagement practices, and the identi-fication can be based on a single seed. Thesemarkers are therefore usefulin “food traceability”, to certify the origin and quality of products on themarket and to prevent fraudulent commercial activity (Brandolini et al.,2006; Cirillo et al., 2009). Among the DNA-based molecular markers,microsatellites, also called Short Sequence Repeats (SSR), are highly pop-ular, as they possess co-dominant inheritance, high abundance, enor-mous extent of allelic diversity, high reproducibility (Mondini, Noorani,& Pagnotta, 2009). SSRs have been employed in several protocols offood authentication, to detect seed mixtures in a lot of wheat seeds(Pasqualone, Lotti, & Blanco, 1999), or to trace the origin of ground beefmixtures (Shackell, Mathias, Cave, & Dodds, 2005), or of specific oliveoil cultivars (Doveri, O'Sullivan, & Lee, 2006; Pasqualone, Montemurro,Caponio, & Blanco, 2004). Bligh (2000) utilized the SSR approach to re-veal the adulteration of Basmati rice with non-premium long-grain rice.DNAmarkers are also valuable tools to resolve the genetic structure of arice collection and to interpret the evolutionary relationships betweengroups. An extremely large population of microsatellite markers, distrib-uted across the 12 linkage groups of rice, has been made available(McCouch et al., 2002). Despite the usefulness and robustness of theSSR-based approach, downstream DNA amplification techniques likecommon electrophoretic separation on polyacrylamide gels are tedious,time-consuming and not always reproducible. Lab-on-a-chip® technolo-gy can be considered a technical improvement, even though it suffersfromother technical limitations (thatwill be further discussed), resultingin a reduced discriminating power.

The aimof our researchwas the settingupof amethod to distinguishpure Italian pigmented rice varieties from imported Asian blends,employing a classical genetic approach based on SSR generated poly-morphism analysis, coupling the Lab-on-a-chip® technology with anew post-analysis statistical processing of the data set. The electropho-retic profiles of each sample at each locus were then analyzed by pro-cessing the raw outcomes provided by the instrument through aspecial algorithm developed in-house.

2. Materials and methods

2.1. Samples

The Italian rice samples employed in the analysis were Artemide,Nerone, Otello and Venere (black pigmented caryopsis), Ermes, riso RossoItaliano and riso Russ (red pigmented caryopsis). Italian cultivars wereprovided by Organisms (public or private) located in Piedmont, who cer-tified their origin. The rice grains employed in theDNAextraction protocoldid not come froma single plant. Non-Italian rice samples employed in theanalysiswere ‘Black Thai Rice’, ‘Purple BrownRice’ and ‘Purple Rice’ (blackpigmented caryopsis), ‘Brown Rice’, ‘Red Cargo Rice’ and ‘Sung Yod’ (redpigmented caryopsis), all coming from Thailand (provided by Asian pro-ducers), and the French rice ‘Riz de Camargue’ (red pigmented caryopsis,kindly provided by a rice retail Company, located in Piedmont). All theserice varieties had no specific national or European designation of origin.The names of these samples could not always be led back to a singleknownvariety (specifiedon the label); therefore, the employed rice grainswere probably a blend of several local cultivars.

Experimental rice blendswere instead obtained bymixing in labora-tory different percentages of Venere rice and Purple rice powder (70/30,80/20, 90/10, 95/5, w/w).

2.2. Genomic DNA extraction and clean-up

Before the genomic DNA extraction, rice grainswere frozenwith liq-uid nitrogen and manually ground using a mortar, avoiding cross-contaminations. The obtained rice powders were either immediately

employed in the DNA extraction protocol, or they were stored at−80 °C.Genomic DNA was purified using the DNeasy Plant mini kit (Qiagen,Hilden,Germany) according to themanufacturers' protocols. Isolated geno-mic DNA was quantified by fluorometer (Qubit™ instrument, Invitrogen,Milan, Italy).

2.3. PCR protocol

SSR markers were selected based on previous studies on the ricegenetic diversity. A list of all the employed primer pairs is displayed inTable 1. All the markers belong to the Gramene SSRs database (http://archive.gramene.org). PCR reactions were prepared in 200 μl tubes.Final concentrations of the reagents were as follows: 1× PCR Buffer(Biotools, Madrid, Spain), 1.5 mM MgCl2 (Biotools, Madrid, Spain),200 μM of each dNTP (Biotools, Madrid, Spain), 0.5 μM of each primer,1 unit of DNA polymerase 5 U/μl (Biotools, Madrid, Spain) and ultra-pure water (Millipore Milli-Q, Vimodrone, Italy), to reach the finalvolume of 20 μl. The volume of DNA used as template was 1.5 μl.Thermal program was set as described below: initial denaturation:95 °C for 5 min; denaturation: 95 °C for 30 s; annealing: 55 °C for30 s; elongation: 72 °C for 60 s; repetition: 35 cycles. The last stepwas a final extension at 72 °C for 7 min. In order to verify the presenceof the amplicon the PCR products were run by electrophoresis on a 2%agarose gel (Biorad, Segrate (MI), Italy) before loading them on chip.

2.4. Lab-on-a-chip capillary micro-electrophoresis analysis

Amplicons separation profile was analyzed through the 2100Bioanalyzer from Agilent (Agilent Technologies, Santa Clara (CA),USA) coupledwith the DNA 1000 LabChips kit. All reagents were storedat 4 °C, allowed to reach room temperature for 30 min before use, andprepared following the manufacturers' instructions. PCR products(1 μl) were loaded on the chip following manufacturers' instructions.Outputs were generated by the 2100 Bioanalyzer Expert software.

2.5. Construction of the data frame

Products to be included in the analysis were selected based on theirexpected sizes. Only products between 100 and 300 bp were consid-ered. Electrophoretic separations where at least one peak within thechosen allelic range was present were considered eligible profiles. Incase more than one peak was visualized, the second most abundantpeak (in terms of concentration expressed as ng/μl) was also selected.In those cases where the second and the third most abundant peakhad approximately the same concentration both products were select-ed. Allele values were expressed as numbers, and an excel file with allthese data was prepared. With the exception of marker RM153 andRM277, where a single band was visualized in all rice samples, for allother markers we completed the excel file as follows: for each ‘rice-marker’ pair we filled in two cells with the same value when only onehighly concentrated peakwas present; on the contrary, for those ampli-fication profiles where the only visualized peak had a concentrationvalue much lower than the corresponding peak (with similar size, seelater) for the samemarker in other rice samples, the absence of second-ary peaks was ascribed to a low amplification efficiency, and we put anasterisk in the second cell. When two peaks were visualized we filled intwo cells with the corresponding values. In case three peaks wereselected, the second and the third value shared the second cell.

2.6. Construction of the dissimilarity matrix

For each number in a cell of the original excel file we considered anumeric interval centered at that number and whose relative size wasequal to a fixed percentage (5%) for each side (e.g.: number 150 becamethe numeric interval 142.5 –157.5). In case of an asterisk, the intervalwas the largest possible (100 –300). To each cell A we associated the

Table 1SSR markers employed in the analysis.

SSRmarker

Forward primer Reverse primer Repeat motif References

RM5 TGCAACTTCTAGCTGCTCGA

GCATCCGATCTTGATGGG (GA) Giarrocco, Marassi, and Salerno (2007); Elias, Mahbub Hasan, and Seraj (2011)

RM21 ACAGTATTCCGTAGGCACGG

GCTCCATGAGGGTGGTAGAG (GA) Giarrocco et al. (2007); Faivre-Rampant et al. (2011)

RM24 GAAGTGTGATCACTGTAACC

TACAGTGGACGGCGAAGTCG (GA) Faivre-Rampant et al. (2011)

RM25 GGAAAGAATGATCTTTTCATGG

CTACCATCAAAACCAATGTTC (GA) GRAMENE database

RM38 ACGAGCTCTCGATCAGCCTA

TCGGTCTCCATGTCCCAC (GA) Ravi, Geethanjali, Sameeyafarheen, and Maheswaran (2003)

RM44 ACGGGCAATCCGAACAACC

TCGGGAAAACCTACCCTACC (GA) Faivre-Rampant et al. (2011)

RM105 GTCGTCGACCCATCGGAGCCAC

TGGTCGAGGTGGGGATCGGGTC

(CCT) GRAMENE database

RM125 ATCAGCAGCCATGGCAGCGACC

AGGGGATCATGTGCCGAAGGCC

(GCT) GRAMENE database

RM144 TGCCCTGGCGCAAATTTGATCC

GCTAGAGGAGATCAGATGGTAGTGCATG

(ATT) Ravi et al. (2003); Ghneim Herrera et al. (2008)

RM153 GCCTCGAGCATCATCATCAG

ATCAACCTGCACTTGCCTGG (GAA) Lapitan, Brar, Abe, and Redoña (2007)

RM163 ATCCATGTGCGCCTTTATGAGGA

CGCTACCTCCTTCACTTACTAGT

(GGAGA) (GA)C(GA)

Pervaiz, Rabbani, Khaliq, Pearce, and Malik (2010)

RM215 CAAAATGGAGCAGCAAGAGC

TGAGCACCTCCTTCTCTGTAG (CT) Lapitan et al. (2007); Bounphanousay, Jaisil, McNally, Sanitchon, and Sackville Hamilton(2008); Faivre-Rampant et al. (2011)

RM218 TGGTCAAACCAAGGTCCTTC

GACATACATTCTACCCCCGG (TC) (ACT)(GT)

Faivre-Rampant et al. (2011)

RM220 GGAAGGTAACTGTTTCCAAC

GAAATGCTTCCCACATGTCT (CT) Lapitan et al. (2007); Faivre-Rampant et al. (2011)

RM228 CTGGCCATTAGTCCTTGG

GCTTGCGGCTCTGCTTAC (CA) (GA) Faivre-Rampant et al. (2011)

RM234 ACAGTATCCAAGGCCCTGG

CACGTGAGACAAAGACGGAG

(CT) Lapitan et al. (2007); Pervaiz et al. (2010); Faivre-Rampant et al. (2011)

RM237 CAAATCCCGACTGCTGTCC

TGGGAAGAGAGCACTACAGC (CT) Lapitan et al. (2007)

RM241 GAGCCAAATAAGATCGCTGA

TGCAAGCAGCAGATTTAGTG (CT) Giarrocco et al. (2007); Lapitan et al. (2007); Pervaiz et al. (2010); Faivre-Rampant et al.(2011)

RM249 GGCGTAAAGGTTTTGCATGT

ATGATGCCATGAAGGTCAGC (AG)AA(AG) Faivre-Rampant et al. (2011)

RM253 TCCTTCAAGAGTGCAAAACC

GCATTGTCATGTCGAAGCC (GA) Lapitan et al. (2007); Faivre-Rampant et al. (2011); Narshimulu, Jamaloddin, Vemireddy,Anuradha, and Siddiq (2011)

RM259 TGGAGTTTGAGAGGAGGG

CTTGTTGCATGGTGCCATGT (CT) Giarrocco et al. (2007); Lapitan et al. (2007); Bounphanousay et al. (2008)

RM263 CCCAGGCTAGCTCATGAACC

GCTACGTTTGAGCTACCACG (CT) Ghneim Herrera et al. (2008); Faivre-Rampant et al. (2011)

RM277 CGGTCAAATCATCACCTGAC

CAAGGCTTGCAAGGGAAG (GA) GRAMENE database

RM335 GTACACACCCACATCGAGAAG

GCTCTATGCGAGTATCCATGG

(CTT) Ravi et al. (2003)

RM551 AGCCCAGACTAGCATGATTG

GAAGGCGAGAAGGATCACAG

(AG) Ghneim Herrera et al. (2008)

RM3068 ACCCGAACGATATCAAGTTA

GAACCTGCTTGTAGATGCTT (AT) Narshimulu et al. (2011)

792 C. Garino et al. / Food Research International 64 (2014) 790–798

union iA of the intervals corresponding to the numbers contained in thecell. To each pair of cells (A,B) of the data matrix (relevant to a givenmarker and two different individuals) we assigned a score s(A,B) as fol-lows: The score s(A,B) = 1 if iA and iB intersected, 0 if they did not inter-sect. Then we compared two samples at a given marker: we extractedthe relevant pairs p1 = (AB) and p2 = (CD) from the data-matrix;this contributed to the similarity matrix by the maximum of s(A,C)+ s(B,D) and s(A,D)+ s(B,C). For example, if we assigned to intersectingintervals the same letter, the score s(i,j)k for the individuals i and j andmarker k equaled 2 if the individuals had a full overlapping betweentheir intervals (e.g. AA:AA or AB:AB), 1 if one individual had only one in-terval overlappingwith the other individual (e.g., AB:AA or AB:AC), and0 if the individuals had no overlaps (e.g., AA:BB, AB:CC or AB:CD). Thesimilarity matrix Swas generated by generalizing the allele sharing dis-tance (ASD) described by Bowcock et al. (1994), with entries S i; jð Þ ¼ 1

2m∑m

k¼1S i; jð Þk , where m is the number of markers for which both

individuals have been tested. The dissimilarity matrix G was insteaddefined as G = 1 − S. The classical multidimensional scaling (MDS,aka principal coordinate analysis) was applied to the matrix G in orderto visualize the calculated genetic distances.

The entire procedure was automated using R (R Core Team, 2013).

3. Results

Sizing accuracy assay: the repeatability of the instrument fragmentsizing was evaluated by the analysis of a commercial 25 bp DNA laddercontaining 11 fragments (control), loading it on different wells of thechip. In Fig. 1A it is possible to notice the gradual and constant shift ofthe upper front marker as the run proceeds from lane L to lane 12. Atthe end of each lane run the 2100 Expert software automatically alignsthe two fronts (upper and lower) in order to obtain a straight line(Fig. 1B), while the base pair length attribution of each peak is based

Table 2sizing accuracy and precision assay for fragment 50–300 bp of a 25 bpDNA ladder in 6 dif-ferent wells of a chip. MDW:maximumdrift withinwells. MDES:maximumdrift from ex-pected size.

Expected size(bp)

Well1

Well3

Well5

Well7

Well9

Well11

MDW MDES

bp % bp %

50 52 52 52 52 53 52 1 2.0 3 6.075 79 79 79 79 79 78 1 1.3 4 5.3100 104 103 103 103 103 102 2 2.0 4 4.0125 130 129 128 128 129 127 3 2.4 5 4.0150 155 154 154 154 154 152 3 2.0 5 3.3175 183 181 180 180 180 178 5 2.9 8 4.6200 209 207 207 207 206 204 5 2.5 9 4.5225 236 233 232 233 232 229 7 3.1 11 4.9250 261 259 258 258 257 254 7 2.8 11 4.4275 287 283 283 283 282 279 8 2.9 12 4.4

793C. Garino et al. / Food Research International 64 (2014) 790–798

on the internal ladder separation profile. Table 2 presents the results ob-tained for each control, as well as the expected actual size of each frag-ment, and the values of maximum drift. For each fragment the variancein length increased both with the distance between wells (highest be-tween lanes 1 and 11) and with the fragment size (highest for the300 bp fragment), while the results showed that the values of the sizedecreased from the first to the last well, the last size determinationbeing closer to the expected one. The percentage of the drift, such asthe ratio between the length variance (drift) and the total length, wasinstead quite constant, ranging from 1.3 to 3.1% over six replicates,and from 3.3 to 6% from the expected size of each fragment. Rulingout the smallest fragments at 50 and 75 bp, the largest variancebetween observed and expected values was 4.9%. These findings are inaccordance with the manufacturer's specifications of sizing reproduc-ibility (5%).

300 312 308 307 307 306 303 9 3.0 12 4.0

3.1. SSR analysis

Twenty-six microsatellite markers were employed to analyze a poolof 14 rice samples. Among them, the seven samples coming from Italywere declared homogeneous and mono-varietal, with a clear and certi-fied indication of origin, while the rest of the pool was formed by sixsamples (possibly blends of different varieties) coming from Thailandand one red rice sample coming from the French region of Camargue.One SSR marker, specifically the RM335, was not considered for theanalysis because the observed allelic range of its amplification productswas too broad, giving amplicons that exceeded 400 base pairs. Becauseof the instrument resolution limits, we decided to include and considerfor the analysis only the alleles comprised between 100 and 300 basepairs. The observed allelic range for the 25 left SSR markers was 104–299 bp.

Following the electrophoretic separation on the chip, each productwas analyzed individually, and the electropherograms were analyzedin order to select the peaks to be included in the analysis. In manycases (~38% of the total number of amplifications) it was not easy tochoose which secondary peak to include, since the second and thethird most abundant peaks had approximately the same concentration(Fig. 5A); in all these cases also the third peak was considered withinthe selected panel. With the exception of markers RM153 and RM277,where a single intense peak was present in all electropherograms, allother markers generated amplification profiles where at least twopeaks were selected for, at least, one rice variety. Only those markerswhere at least two individuals displayed amplification products thatdid not produce overlapping allelic intervals were considered informa-tive. For this reason RM153 and RM277were considered uninformativemarkers, and were not included in the genetic diversity analysis.

Fig. 1. Agilent 2100 bioanalyzer gel virtual run representation before (A) and after (B) softwareupper front marker separation profile.

3.2. Genetic diversity analysis

In order to base our analysis only on the true genetic differences, wedid not consider single alleles, but allelic intervals spanning within ±5%of the amplicon length. Two intervals were considered different onlywhen they did not overlap: in case of overlapping there is in fact thepossibility that the two amplification products are actually the same al-lele, therefore a difference cannot be safely assigned.

Twenty-three informative markers were finally included in thestudy of genetic diversity. When in a pairwise comparison an individualpresenting two visualized peaks was compared to a second individualpresenting just one peak (e.g., AB:AA or AB:CC), the assigned scorewas either 1 or 0. In 13 cases out of 322 electrophoretic runs (~4% ofthe total amplicon population) the concentration of the major peakwas comparable to that of all othermajor peaks identified in other indi-viduals for the same marker, therefore the clear absence of secondarypeaks was interpreted as a polymorphism indicator. However, in other30 cases (~9.3% of the total amplicon population), the identified majorpeak was much less concentrated respect to the other individuals'major peaks, therefore the absence of secondary peaks was attributedto a low amplification efficiency, andwas not considered as an indicatorof polymorphism (e.g., AB:A* = 2 and AB:C* = 1).

An MDS distribution showing the genetic relatedness among ourrice samples is reported in Fig. 2. In our approach, the possibility thattwo amplicons with similar size and producing overlapping clustersare different is considered null (full match), therefore the calculated ge-netic distance does not take into account the possible differences, butonly the certain ones. Hence, real genetic relatedness between the ricevarieties considered in this study could actually differ fromwhat is rep-resented in the distribution. Nevertheless, in our model two main

analysis. The drift in samples run within different wells is clearly highlighted in (A) by the

Fig. 2.MDS analysis showing genetic relatedness among 14 rice varieties measured on 23microsatellitemarkers, built using the dissimilarity matrix G. The first two dimensions (pc1 andpc2) explain together between the 54.6% and the 64% of the total variance.

Fig. 3. Heatmap generated using 6 SSR markers (RM21, RM163, RM218, RM228, RM237and RM259) on 14 rice varieties. Fillings go from white (identity) to black as the geneticdistance between individuals increases.

794 C. Garino et al. / Food Research International 64 (2014) 790–798

observations can be done: firstly, all Italian rice varieties plus the Frenchvariety Riz de Camargue are clustered together, creating a group apartfrom the Asian varieties. Secondly, rice varieties that are genetically re-lated clustered closer to each other respect to non-genetically relatedones. This was observed for Artemide and Ermes rice, both derived bycross-breeding between Venere rice and two different sub-species ofO. sativa indica, for Nerone, also coming directly from Venere, and forRosso Italiano, related to the French Riz de Camargue. Unfortunately,we do not have enough information to throw other conclusions on theobserved genetic relatedness occurring among our other rice samples.It is important to underline that this kind of evidence would have notbeen observed in case a classical allele sharing matrix had been createdstarting from the raw data of allele sizing provided by the 2100Bioanalyzer analysis software. An additional processing of the datathat keeps into account the intrinsic limit of resolution of the instrumenthad to be necessarily included. The limitation of this extra step is thatthe population of informative alleles is strongly limited; therefore,closely related individuals are not easily distinguishable. This was ob-served for the pairs Artemide–Ermes and Venere–Nerone (Fig. 2),which, probably due to their genetic proximity, could not be geneticallydifferentiated in our model. Artemide and Ermes rice samples were fur-ther testedwith another set of 20 SSRmarkers, in order to find an infor-mative microsatellite, but no differences were large enough to producenon-overlapping allelic clusters (data not shown).We can conclude thatif 43 markers had maybe been able to definemore precisely the geneticdistances between all the employed rice samples, they would not havebeen able to discriminate Artemide from Ermes. On the other hand, allthe rice cultivars, with the exception of the pairs Artemide–Ermes andVenere–Nerone, were differentiated from each other by using only 6of the 23 SSR markers (Fig. 3).

3.3. Adulteration assay

In order to verify the possibility to track the presence of a non-Italianrice cultivar in a rice blend, thus highlighting a commercial fraud,Venere rice was firstly ground into powder, then spiked with knownamounts of Purple rice powder. Total genomic DNAwas then extracted,purified and amplified using few selected SSR markers, considered themost informative ones based on our previous analysis. Amplified prod-ucts were then loaded on chip and the resulting electropherogramswere compared to the profiles obtained by amplifying DNA from non-

spiked Venere rice and Purple rice. Outcomes obtained using markersRM234 and RM263 are shown in Figs. 4 and 5. When DNA from Venererice was amplified using RM234, two peaks, at 131 and 153 bp werehighlighted (Fig. 4A), while when the DNA coming from the admixtureof the two rice powderswas amplified, a different profile, showing threepeaks, was obtained (Fig. 4C–F). The additional peak at 148 bp could beattributed to the major allele of Purple rice (Fig. 4B). The same observa-tion can be done for marker RM263, where the extra peak at 132 bp vis-ible in the amplification run of the DNA purified from the admixture(Fig. 5C–E) is absent in the Venere rice amplification profile (Fig. 5A),and can be led back to the electrophoretic separation of Purple riceamplicon (Fig. 5B). Other employed markers did not produce clear re-sults, especially because the separation profile generated using theDNA from non-spiked Venere rice was not so definite and free from

Fig. 4. Electropherograms relative to RM234 marker amplification profiles. Venere rice powder was spiked with different known amount of Purple rice powder. A: 100% Venere rice; B:100% Purple rice; C: 70/30 Venere/Purple rice blend; D: 80/20 Venere/Purple rice blend; E: 90/10 Venere/Purple rice blend; F: 95/5 Venere/Purple rice blend.

795C. Garino et al. / Food Research International 64 (2014) 790–798

background noise (data not shown). Concluding, we observed that thechoice of themarker had a great impact on the sensitivity of themethod:in these two examples the presence of Purple rice DNAwas tracked downto 5% using the RM234 (Fig. 4F), and was instead undetectable at thesame concentration using RM263 (Fig. 5F). As regards the instrumentsizing reproducibility limit of 5%, in this case it did not affect the interpre-tation of the results: the software is able to distinguish two differentamplicons within the same run as long as they differ for at least 5 bp.Therefore,when twoproducts at 148 and154bpare visualized in twodif-ferent wells of the same chip it is not safe to say that they are different(since their two allelic intervals overlap), but if they are present togetherin the same separation run (as in the example shown in Fig. 4C–F) it ispossible to distinguish them, because they differ for more than 5 bp.

4. Discussion

Microsatellitemarkers have been extensively employed over the lastyears to study the genetic diversity occurringwithin rice accession pools

(GhneimHerrera et al., 2008; Giarrocco et al., 2007; Lapitan et al., 2007;Pervaiz et al., 2010; Qi et al., 2009; Vanniarajan, Vinod, & Pereira, 2012),although fewworks include information about pigmented rice varieties(Bounphanousay et al., 2008; Elias et al., 2011; Gealy, Tai, & Sneller,2002; Gowda et al., 2012). To the best of our knowledge, this is thefirst report where SSR have been employed to study the Italianpigmented cultivars Artemide, Ermes, Nerone, Otello, Rosso Italianoand riso Russ. Venere rice was instead included in a recent study onthe Italian rice germplasm management involving 135 rice genotypesrepresenting the main cultivars either introduced or developed in Italyfrom 1850 to 2001 (Mantegazza et al., 2008).

SSR are tandem repeats of sequence units, and the polymorphismsassociated with a specific locus are due to the variation in length ofthe microsatellite, which in turn depends on the number of repetitionsof the basic unit (Mondini et al., 2009). Therefore, the accuracy of theamplicon size determination is of fundamental importance in this anal-ysis, since even a difference of 2 base pairs is considered a polymor-phism. Typically, amplicons are visualized on urea polyacrylamide gel

Fig. 5. Electropherograms relative to RM263 marker amplification profiles. Venere rice powder was spiked with different known amount of Purple rice powder. A: 100% Venere rice; B: 100%Purple rice; C: 70/30 Venere/Purple rice blend; D: 80/20 Venere/Purple rice blend; E: 90/10 Venere/Purple rice blend; F: 95/5 Venere/Purple rice blend.

796 C. Garino et al. / Food Research International 64 (2014) 790–798

electrophoresis (PAGE), and their sizes are determined through thecomparisonwith a known ladder after silver staining of the gel. The dis-crimination power of these gel runs can reach down to 2 base pairs ofdifference, provided that all the samples to be compared are loaded onthe same gel. Furthermore, this technique presents several disadvan-tages: acrylamide is a toxic compound, and both the preparationof the gel matrix and the final gel staining procedure are time-consuming and complex, and require a skilled operator. The entire pro-cess is a time-consuming approach, since the separation of DNA frag-ments on the gel may require in some cases several hours. Finally, theidentification of amplification products is subjectively performed bythe lab operator, who, by looking at the gel image, decides whetherthe band is present or not in different lanes. The use of more accurateand objective methods for product identification, such as capillary elec-trophoresis analyzers, is therefore to be preferred (de Oliveira Borba,Pereira Vianello Brondani, Hideo Nakano Rangel, & Brondani, 2009;Jiang, Xia, Basso, & Lu, 2012; Shivrain et al., 2010). However, automatic

analyzers are somewhat expensive, and require specialized hardwareand software, as well as substantial user training and experience.

Microcapillary electrophoresis based on Lab-on-a-chip® technologydevices are relatively inexpensive and simple to operate, and they rep-resent a valuable advance for the analysis of complex DNA banding pat-terns. Due to their ease of use, their speed of analysis and low sampleand reagent consumption, in the last years these systems have foundapplication in several research fields. In food authentication, they havebeen employed in protocols based on DNA bands pattern recognition(Clarke, Dooley, Garrett, & Brown, 2008; Dooley, Sage, Clark, Brown, &Garrett, 2005; Fajardo et al., 2009; Steele, Ogden, McEwing, Briggs, &Gorham, 2008).

When microsatellites are applied to study the genetic diversity oc-curring within a population, genetic distances between individuals arenormally computed based on the alleles frequencies, as reported in theformula of the genetic relative distance RDij = 1/2 [Σ (Xai − Xaj)2]1/2,where Xai is the frequency of the allele a for the individual i, and Xaj is

797C. Garino et al. / Food Research International 64 (2014) 790–798

the frequency of the allele a for the individual j (Rogers, 1972). The prob-lem that rises when the Lab-on-a-chip® technology is employed is thatthe intrinsic limit of reproducibility of the instrument does not allow toassign a definite length to each amplicon (as previously highlightedwhen a commercial 25 bp DNA ladder was loaded on the chip, Fig. 1),leading to misinterpretations of the product size. Because of this instru-ment limitation, it is not possible to describe the frequency of a givenallele in an exact manner. Therefore, we cannot assign a full identitybetween two individuals because the same product (allele) can behavedifferently when loaded on different chips and even on different wellsof the same chip (Table 2).

Contrarily, it is possible to assign a true difference between individ-uals, provided that the generated amplicons are different enough. Re-cently the Agilent 2100 Bioanalyzer has been proposed in the analysisof STR for paternity tests: in this work the Bioanalyzer did not presentthe necessary accuracy and resolution for the genotype testing, andthis limitation was intrinsically associated with the low separation res-olution. However, despite the instrument was not fit for a positive indi-cation of paternity or identification of the individuals, the Authorshighlighted how the method could be capable statistically of providingenough resolution to eliminate unequivocally one individual in the ex-clusion of paternity analysis (Fraige, Travensolo, & Carrilho, 2013).This is the same conclusion that we reached considering pigmentedrice samples, namely that this method cannot safely identify two varie-ties as equal for a given marker, but, given the right marker, it can beused to unequivocally distinguish two individuals. This could be usefulas a pre-screening test in a complex genetic analysis wheremany differ-ent non-closely related samples are present, to create clusters thatmight be, if needed, further analyzed using more performingapproaches. Although other cheaper electrophoretic techniques, suchas separation on MetaPhor™ agarose gel, could be employed (Gowdaet al., 2012; Pervaiz et al., 2010; Vanniarajan et al., 2012), the use ofBioanalyzer allows the identification of several products undetectableusing classical etidium bromide staining, thus increasing the amountof available information. This was also observed in our analysis, if wecompare the electrophoretic profiles generated by the agarose gel sepa-ration (data not shown) to those obtained by microelectrophoresis onchip: no more than one band per sample was visible after the stainingof the agarose gel, thus limiting the polymorphism analysis to themajor products of amplification (major peaks).

The instrument sizing reproducibility limit of 5%, reported by themanufacturers, was the same observed in previous works where thesame system was employed (Dooley et al., 2005; Fraige et al., 2013;Recupero et al., 2012). Such limit does not currently enable the use ofthis instrument in combination with the SSR analysis to accurately de-scribe the genetic relationships occurringwithin a population. Attemptsto improve this aspect have been successfully carried out by the AgilentTechnologies R&D Department (Aboud, Gassmann, & McCord, 2010;Rüfer, Aboud, & McCord, 2010), but their results have not yet beentranslated into a commercially available kit. Nevertheless this limitationis improvable, and in the algorithm that we generated to process theraw outputs the threshold of the comparison acceptability can be easilymodified. The main advantage of this algorithm is that none of the rawdata is excluded from the analysis, but only the comparisons carryinginformation about the polymorphism contribute to the final outcome.Hence, by decreasing the threshold of acceptability (e.g. from 5% to2%), the number of informative comparisons increases, and the descrip-tion of the genetic relatedness gets closer to the real one.

In the present study we chose the limit of 5% of the amplicon sizingaccuracy becausewewanted to be able to safely compare the results ob-tained with samples loaded on different chips. Such limit could belowered just by considering only samples loaded on the same chipand on close wells (see Fig. 1). By setting the threshold of 5% onlyabout the 11% of the total number of comparisons were considered asindicator of polymorphism by our algorithm. Nevertheless, the MDSanalysis revealed the formation of two separated groups of rice

varieties, with all the Italian cultivars belonging to the same group.Venere rice was born in 1997 thanks to Sa.Pi.Se.'s research center(Italian Company, Vercelli) through conventional crossing between anAsian variety of black rice given by IRRI (International Rice Research In-stitute) and a local variety. It belongs to the japonica group, and it has amediumsized grain (www.risovenere.it). Both Artemide and Ermes ricewere obtained by natural hybridization between Venere rice and twowhite pigmented pericarp rice cultivars, belonging to the indica group.The variety Nerone, created by the Company Lugano Leonardo srl(Tortona, Italy) is also derived by Venere through classical cross-breeding by pedigree method (personal communication from the de-veloper). Although we do not possess information about Otello rice,whose origin is protected by patent, we can assume, according to themorphology (shape, size) of the grains, that both Otello and Nerone be-long to the japonica group. Finally, both the red pigmented varietiesRosso Italiano and Russ have been originated from natural crossingamong weedy rice cultivars present in the North of Italy. Despite thelack of information regarding the genetic origin of the rice samplesemployed in the analysis, the suggested approach allowed us todistinguish a separated genetic group including all the European rice va-rieties from a larger group of Asian ones. Moreover, a few combinationsof only six SSR markers enabled us to differentiate from each other allthe rice samples, with the exception of the pairs Artemide–Ermes andVenere–Nerone.

Concerning the application of this approach to the authenticitydetermination in a “rice” model system, the microelectrophoresis Lab-on-a-chip® technology performed using the 2100 Bioanalyzer enabledus to identify the presence of the Purple rice variety artificially blendedin Venere rice, employing a single informative SSR marker. In order toapply this investigation method to other unknown cultivars, the choiceof the marker (or of the markers set) is of fundamental importance: theelectrophoretic separation profile of the ‘pure’ variety to be discriminateshould present few (better one or two), clear and intense peaks, and nobackground noise should disturb the reading (as for marker RM234 inthe example, Fig. 4A). As we observed, when the electropherogram ofthe positive control is clear and reproducible, the presence of small ‘for-eign’ peaks could testify the contamination with a template DNA from adifferent origin. Moreover, not every marker has the same detectionability; therefore, the limit of the detection might vary by changing themarker. Once the best performing markers are selected, replicates ofamplification reaction should be performed for each marker, and theresulting amplicons should be loaded in triplicates in separated wells ofthe same chip, in order to consider the ‘drift’ effect. Finally, due to the res-olution limit of the instrument, amplicons can be distinguished onlywhen they differ for at least 5 bp. Similar observations were reportedwhen this approach was applied to the detection of Barbera grape (Vitisvinifera) added to Nebbiolo grape in an experimental homemade mustin order to simulate commercial fraud (Recupero et al., 2012).

Lab-on-a-chip® technology represents a simplified platform for DNAdetection that permits the contemporary processing of 12 post-PCR sam-ples via capillary electrophoresis on a disposable chip. The advantages ofthe systemare ease of use, speed of analysis, low sample and reagent con-sumption, while its downsides are the sizing accuracy and resolutionlimit. Such restrictions limited so far its employ in complex genetic anal-yses. In thisworkwe showed how, even taking into account these systemlimitations, it is possible using the right type and amount of markers todistinguish genotypes which are not closely related, in order to createclusters that might be, if needed, further analyzed using more expensiveand performing approaches. Also, it was possible to trace the presence ofa non-Italian pigmented rice in an artificial blend of Venere rice. Finally, inthis work raw outcomes originated from the instrument software wereadditionally processed by developing a new algorithm as new post-analysis tool, improving the robustness of the method. We suggest thisnew algorithm to be used in combination with microelectrophoresis onchip, particularly as a pre-screening in complex genetic analyses and asan easy way to investigate on frauds in foods with protected origin.

798 C. Garino et al. / Food Research International 64 (2014) 790–798

Acknowledgments

The research was conducted with the financial support from theRegione Piemonte (Polo di Innovazione Agroalimentare), as a part ofthe RiOrTec innova project. The authors acknowledge Oryza S.r.l. forproviding the certified rice materials.

References

Aboud, M. J., Gassmann, M., & McCord, B. R. (2010). The development of mini pentamericSTR loci for rapid analysis of forensic DNA samples on a microfluidic system.Electrophoresis, 31, 2672–2679.

Bligh, H. F. J. (2000). Detection of adulteration of Basmati rice with non-premium long-grain rice. International Journal of Food Science and Technology, 35(3), 257–265.

Bounphanousay, C., Jaisil, P., McNally, K. L., Sanitchon, J., & Sackville Hamilton, N. R.(2008). Variation of microsatellite markers in a collection of Laos black glutinousrice (Oryza sativa L.). Asian Journal of Plant Sciences, 7(2), 140–148.

Bowcock, A. M., Ruiz-Linares, A., Tomfohrde, J., Minch, E., Kidd, J. R., & Cavalli-Sforza, L. L.(1994). High resolution of human evolutionary trees with polymorphicmicrosatellites. Nature, 368, 455–457.

Brandolini, V., Coïsson, J. D., Tedeschi, P., Barile, D., Cereti, E., Maietti, A., et al. (2006).Chemometrical characterization of four Italian rice varieties based on genetic andchemical analyses. Journal of Agricultural and Food Chemistry, 54, 9985–9991.

Cirillo, A., Del Gaudio, S., Di Bernardo, G., Galderisi, U., Cascino, A., & Cipollaro, M. (2009).Molecular characterization of Italian rice cultivars. European Food Research and Tech-nology, 228, 875–881.

Clarke,M. -A. L., Dooley, J. J., Garrett, S. D., & Brown, H. M. (2008). An investigation into theuse of PCR–RFLP profiling for the identification of fruit species in fruit juices. FSA FinalTechnical Report.

de Oliveira Borba, T. C., Pereira Vianello Brondani, R., Hideo Nakano Rangel, P., & Brondani,C. (2009). Microsatellite marker-mediated analysis of the EMBRAPA Rice Core Collec-tion genetic diversity. Genetica, 137, 293–304.

Dooley, J. J., Sage, H. D., Clark, M. -A. L., Brown, H. M., & Garrett, S. D. (2005). Fish speciesidentification using PCR–RFLP analysis and lab-on-a-chip capillary electrophoresis:Application to detect white fish species in food products and an inter-laboratorystudy. Journal of Agricultural and Food Chemistry, 53(9), 3348–3357.

Doveri, S., O'Sullivan, D. M., & Lee, D. (2006). Non-concordance between genetic profilesof olive oil and fruit: A cautionary note to the use of DNA markers for provenancetesting. Journal of Agricultural and Food Chemistry, 54(24), 9221–9226.

Elias, S. M., Mahbub Hasan, A. K. M., & Seraj, Z. I. (2011). Microsatellite marker diversityand sequence polymorphism in the red gene locus of indigenous rice populationsof Bangladesh. Plant Systematics and Evolution, 296, 157–165.

Faivre-Rampant, O., Bruschi, G., Abbruscato, P., Cavigiolo, S., Picco, A. M., Borgo, L., et al.(2011). Assessment of genetic diversity in Italian rice germplasm related to agronom-ic traits and blast resistance (Magnaporthe oryzae). Molecular Breeding, 27, 233–246.

Fajardo, V., Gonzalez, I., Dooley, J., Garret, S., Brown, H. M., Garcia, T., et al. (2009). Appli-cation of polymerase chain reaction–restriction fragment length polymorphism anal-ysis and lab-on-a-chip capillary electrophoresis for the specific identification of gameand domestic meats. Journal of the Science of Food and Agriculture, 89, 843–847.

Finocchiaro, F., Ferrari, B., Gianinetti, A., Dall'Asta, C., Galaverna, G., Scazzina, F., et al.(2007). Characterization of antioxidant compounds of red and white rice and chang-es in total antioxidant capacity during processing. Molecular Nutrition and FoodResearch, 51, 1006–1019.

Fraige, K., Travensolo, R. F., & Carrilho, E. (2013). Analysis of seven STR human loci for pa-ternity testing by microchip electrophoresis. Brazilian Archives of Biology and Technol-ogy, 56(2), 213–221.

Gealy, D. R., Tai, T. H., & Sneller, C. H. (2002). Identification of red rice, rice, and hybridpopulations using microsatellite markers. Weed Science, 50, 333–339.

Ghneim Herrera, T., Posso Duque, D., Pérez Almeida, I., Torrealba Núñez, G., Pieters, A. J.,Martinez, C. P., et al. (2008). Assessment of genetic diversity in Venezuelan rice cul-tivars using simple sequence repeats markers. Electronic Journal of Biotechnology,11(5), S1–S14.

Giarrocco, L. E., Marassi, M. A., & Salerno, G. L. (2007). Assessment of the genetic diversityin Argentine rice cultivars with SSR markers. Crop Science, 47, 853–860.

Gowda, S. J. M., Randhawa, G. J., Bisht, I. S., Firke, P. K., Singh, A. K., Abraham, Z., et al.(2012). Morpho-agronomic and simple sequence repeat-based diversity in coloredrice (Oryza sativa L.) germplasm from peninsular India. Genetic Resources and CropEvolution, 59, 179–189.

Jiang, Z., Xia, H., Basso, B., & Lu, B. -R. (2012). Introgression from cultivated rice influencesgenetic differentiation of weedy rice populations at a local spatial scale. Theoreticaland Applied Genetics, 124, 309–322.

Lapitan, V. C., Brar, D. S., Abe, T., & Redoña, E. D. (2007). Assessment of genetic diversity ofPhilippine rice cultivars carrying good quality traits using SSR markers. BreedingScience, 57, 263–270.

Mantegazza, R., Biloni, M., Grassi, F., Basso, B., Lu, B. -R., Cai, X. X., et al. (2008). Temporaltrends of variation in Italian rice germplasm over the past two centuries revealed byAFLP and SSR markers. Crop Science, 48, 1832–1840.

McCouch, S. R., Teytelman, L., Xu, Y., Lobos, K. B., Clare, K., Walton, M., et al. (2002). Devel-opment and mapping of 2240 new SSR markers for rice (Oryza sativa L.). DNAResearch, 9, 199–207.

Mondini, L., Noorani, A., & Pagnotta, M. A. (2009). Assessing plant genetic diversity bymo-lecular tools. Diversity, 1, 19–35.

Narshimulu, G., Jamaloddin, M., Vemireddy, L. R., Anuradha, G., & Siddiq, E. (2011). Poten-tiality of evenly distributed hypervariable microsatellite markers in marker-assistedbreeding of rice. Plant Breeding, 130, 314–320.

Pasqualone, A., Lotti, C., & Blanco, A. (1999). Identification of durum wheat cultivars andmonovarietal semolinas by analysis of DNA microsatellites. European Food Researchand Technology, 210(2), 144–147.

Pasqualone, A., Montemurro, C., Caponio, F., & Blanco, A. (2004). Identification of virginolive oil from different cultivars by analysis of DNA microsatellites. Journal ofAgricultural and Food Chemistry, 52(5), 1068–1071.

Pervaiz, Z. H., Rabbani, M. A., Khaliq, I., Pearce, S. R., & Malik, S. A. (2010). Genetic diversityassociated with agronomic traits using micro satellite markers in Pakistani rice land-races. Electronic Journal of Biotechnology, 13(3), 1–12.

Qi, Y., Zhang, H., Zhang, D., Wang, M., Sun, J., Ding, L., et al. (2009). Assessing indica–japon-ica differentiation of improved rice varieties using microsatellite markers. Journal ofGenetics and Genomics, 36, 305–312.

R Core Team (2013). R: A language and environment for statistical computing. Vienna,Austria: R Foundation for Statistical Computing (www.R-project.org).

Ravi, M., Geethanjali, S., Sameeyafarheen, F., & Maheswaran, M. (2003). Molecular markerbased genetic diversity analysis in rice (Oryza sativa L.) using RAPD and SSR markers.Euphytica, 133, 243–252.

Recupero, M., Garino, C., De Paolis, A., Cereti, E., Coïsson, J. D., Travaglia, F., et al. (2012). Amethod to check and discover adulteration of Nebbiolo-based monovarietal musts:Detection of Barbera and Dolcetto cv via SSR analysis coupled with Lab-On-Chip®microcapillary electrophoresis. Food Analytical Methods, 6(3), 952–962.

Rogers, J. S. (1972). Measures of genetic similarity and genetic distance. Studies in geneticsVII (pp. 145–153). Austin: University of Texas, Publication 7213.

Rüfer, A., Aboud, M., & McCord, B. (2010). A novel microfluidic approach for denaturatinghigh-resolution DNA electrophoresis. Journal of Biomolecular Techniques, 21(3 Suppl.), S30.

Shackell, G. H., Mathias, H. C., Cave, V. M., & Dodds, K. G. (2005). Evaluation ofmicrosatellites as a potential tool for product tracing of ground beef mixtures. MeatScience, 70, 337–345.

Shao, Y., Jin, L., Zhang, G., Lu, Y., Shen, Y., & Bao, J. (2011). Association mapping of graincolor, phenolic content, flavonoid content and antioxidant capacity in dehulled rice.Theoretical and Applied Genetics, 122, 1005–1016.

Shivrain, V. K., Burgos, N. R., Agrama, H. A., Lawton-Rauh, A., Lu, B., Sales, M. A., et al.(2010). Genetic diversity of weedy red rice (Oryza sativa) in Arkansas, USA. WeedResearch, 50, 289–302.

Steele, K. A., Ogden, R., McEwing, R., Briggs, H., & Gorham, J. (2008). InDel markers distin-guish Basmatis from other fragrant rice varieties. Field Crops Research, 105(1–2),81–87.

Vanniarajan, C., Vinod, K. K., & Pereira, A. (2012). Molecular evaluation of genetic diversityand association studies in rice (Oryza sativa L.). Journal of Genetics, 91(1), 9–19.