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A DNA Barcoding Approach to Identify Plant Species in Multiflower Honey

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A DNA Barcoding Approach to Identify Plant Species in Multiflower Honey Food Chemistry 170 (2015) 308–315 Contents lists available at ScienceDirect Food Chemistry journal homepage: www.elsevier.com/locate/foodchem Analytical Methods A DNA barcoding approach to identify plant species in multiflower honey ⇑ I. Bruni a, A. Galimberti a, L. Caridi a, D. Scaccabarozzi b, F. De Mattia a, M. Casiraghi a, M. Labra a, a Università degli Studi di Milano-Bicocca, ZooPlantLab, Dipartimento di Biotecnologie e Bioscienze, Piazza della Scienza 2, 20126 Milano, Italy b Parco Regionale della Grigna Settentrionale, Via Fornace Merlo 2, 23816 Barzio, Italy article info abstract Article history: The purpose of this study was to test the ability of DNA barcoding to identify the plant origins of pro- Received 15 August 2013 cessed honey. Four multifloral honeys produced at different sites in a floristically rich area in the northern Received in revised form 25 May 2014 Italian Alps were examined by using the rbcL and trnH-psbA plastid regions as barcode markers. An exten- Accepted 13 August 2014 sive reference database of barcode sequences was generated for the local flora to determine the taxo- Available online 23 August 2014 nomic composition of honey. Thirty-nine plant species were identified in the four honey samples, each of which originated from a mix of common plants belonging to Castanea, Quercus, Fagus and several her- Keywords: baceous taxa. Interestingly, at least one endemic plant was found in all four honey samples, providing a Food traceability clear signature for the geographic identity of these products. DNA of the toxic plant Atropa belladonna was Honey Molecular markers detected in one sample, illustrating the usefulness of DNA barcoding for evaluating the safety of honey. Pollen identification Ó 2014 Elsevier Ltd. All rights reserved. rbcL trnH-psbA 1. Introduction vitamins and minerals) can be easily determined by analytical methods, whereas organoleptical properties are usually deter- The European Union recognises the importance of food trace- mined by sensory (i.e., visual, taste and olfactory) approaches. ability, defined as the ability to track food through all stages of pro- Although more complicated to determine, geographic provenance duction, processing and distribution (Regulation 178/2002, Official can be inferred by analysing the spectrum of pollen residuals Journal of the European Communities, 2002). Food traceability has which indicates the plants visited by bees during honey a pivotal role in global trade markets, and its relevance is growing production. every year (Galimberti et al., 2013; Karlsen, Dreyer, Olsen, & In recent years, several approaches have been set up to evaluate Elvevoll, 2013). Concerns about food traceability are even more honey characteristics (Batista et al., 2012; Camina, Pellerano, & important for foods whose area of production strongly influences Marchevsky, 2012; Manyi-Loh, Ndip, & Clarke, 2011) and to assess the quality of the final product. the quality of different products. The composition of pollen is com- For example, the quality of honey is closely related to the flower monly analysed by microscopy to determine the botanical ingredi- composition and the climatic and environmental conditions of the ents of honey. This technique, known as melissopalynology, has production area (Bogdanov, Haldimann, Luginbuhl, & Gallmann, been the most common method for identifying and counting pol- 2007; Stolzenbach, Byrne, & Bredie, 2011; Pohl, Stecka, Sergiel, & len grains in honey in the last 30 years (Bambara, 1991; Escriche, Jamroz, 2012). The Commission of the European Communities in Kadar, Juan-Borrás, & Domenech, 2011; Louveaux, Maurizio, & Council Directive DENLEG 2000/10 (Council Directive, 2000) estab- Vorwohl, 1978). Melissopalynology can determine the frequency lished guidelines for the general and specific compositions of the of pollen from different species and can be used with other main honey varieties that can be marketed in European countries. approaches, to classify honey (Persano Oddo & Piro, 2004). Euro- The principal labelling requirements that must be indicated are the pean standards define honey as ‘unifloral’ when it is from a com- floral origin, physicochemical properties, organoleptical character- pletely or partially botanical origin, including its pollen (>45%) istics and regional provenance (Council Directive, 2000). Physico- and physicochemical and sensory characteristics corresponding chemical parameters (e.g., pH, conductivity, sugar, aminoacids, to its species of origin (see Persano Oddo & Piro, 2004 for some examples). However, melissopalynology is time-consuming, requires spec- ⇑ Corresponding author. Tel.: +39 02 64483472; fax: +39 02 64483450. ialised knowledge and involves a laborious counting procedure, E-mail address: [email protected] (M. Labra). making it a challenge to interpret the results and to identify http://dx.doi.org/10.1016/j.foodchem.2014.08.060 0308-8146/Ó 2014 Elsevier Ltd. All rights reserved. I. Bruni et al. / Food Chemistry 170 (2015) 308–315 309 botanical origins. Furthermore, it may not be possible to recognise as DNA barcoding tools for identifying the botanical constituents of individual species from a micromorphological analysis of pollen honey. (Khansari et al., 2012). Although some pollen (e.g., of Eucalyptus and Castanea) show recognisable morphological traits in honey, 2. Materials and methods others (e.g., pollen of some Campanulaceae and Lamiaceae) are not well distinguishable by their micromorphological traits 2.1. Study area and honey sampling (Khansari et al., 2012; Salmaki, Jamzad, Zarre, & Bräuchler, 2008). Recently, researchers have applied molecular tools to analyse In this study, four honeys (ORT; CON, MON and BAI) produced the composition of honey by using primers and probes specifically in the regional park of Grigna Settentrionale (Northern Italy), were designed to recognise local plant species in honey (Laube et al., selected to investigate their botanical composition through a DNA 2010). DNA markers, such as nuclear 18S rDNA (Olivieri, Marota, barcoding approach. These honeys were ready to be sold in the Rollo, & Luciani, 2012) and the plastid trnL gene (Valentini, markets as ‘multifloral hone’, produced during the period of Miquel, & Taberlet, 2010), were used to test their ability to identify June–July 2012 by amateur beekeepers from four different locali- plant traces from different honey samples. This approach is based ties in the park (Fig. 1). For each sample an aliquot of 25 ml on ‘DNA barcoding’, in which the species composition of mixed (40 g) was stored at À20 °C and used for DNA extraction. matrices is determined by comparing sequences of the same The regional park of Grigna Settentrionale covers a territory of DNA region with a reference database (Casiraghi, Labra, Ferri, over 5000 hectares around the Grigna massif (Italian Alps). Besides Galimberti, & De Mattia, 2010; Galimberti et al., 2013). The selec- the rather limited altitude (the highest summit reaches the tion of universal informative markers is very important 2409 m), the protected area is characterised by a great variety of (Casiraghi et al., 2010; Sandionigi et al., 2012) to identify the habitats and climates ranging from the typical alpine to the sub- botanical composition of honey and to differentiate pollen belong- mediterranean climate caused by the strong influence of Como Lake. ing to closely related taxa. The total flora of the four honey production areas selected in The Plant Working Group of the Consortium for the Barcode this study consists of 593 vascular plants, including cultivated spe- of Life (CBOL; http://www.barcoding.si.edu/plant_working_group. cies (Rossi, 2005) and many rare and endemic taxa. The vegetation html) suggested the plastid coding regions rbcL and matK as core of the park varied according to the altitude: the lowest slopes up to barcodes for plant identification. Additional regions, such as 900 m are covered by forests (hornbeam, oak, chestnut, ash and trnH-psbA and ITS2, could be used to analyse closely related taxa linden trees) alternated by dry and pasture meadows. At higher (Hollingsworth, Graham, & Little, 2011). Although the matK gene altitudes, up to 1800–1900 m, there are forests of beech and coni- is considered a good DNA marker because it evolves rapidly (Hilu fers, especially larch. Near the summit, there are heaths with Rho- & Liang, 1997), its amplification requires specific primer combina- dodendron, mountain pine, juniper and green alder, which mark tions for different angiosperm families (Dunning & Savolainen, the limit of the trees. At the highest altitudes, next to the top of 2010). Therefore, it is not suitable for the analysis of unknown the massif, the area is dominated by the typical grasslands of cal- complex matrices, such as honey. The goal of this study was to careous substrates, characterised by annual species (e.g., Carex, evaluate the usefulness of the rbcL region and the trnH-psbA spacer Sesleria) and chasmophytic vegetation. Fig. 1. Distribution map of honey production sites within the Grigna Settentrionale Regional Park (red line). The full names and geographic coordinates for the collection sites are provided. (For interpretation of the references to colour in this figure legend, the reader is referred to the web version of this article.) 310 I. Bruni et al. / Food Chemistry 170 (2015) 308–315 The four selected honeys were produced at the medium alti- (Newmaster & Ragupathy, 2009). PCRs were performed starting tudes between 700 and 1300 m, in the meadow area, surrounded
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