Advances in Agriculture

Advances in Agriculture / 2014 / Article

Review Article | Open Access

Volume 2014 |Article ID 831875 |

Andrea Galimberti, Massimo Labra, Anna Sandionigi, Antonia Bruno, Valerio Mezzasalma, Fabrizio De Mattia, "DNA Barcoding for Minor Crops and Food Traceability", Advances in Agriculture, vol. 2014, Article ID 831875, 8 pages, 2014.

DNA Barcoding for Minor Crops and Food Traceability

Academic Editor: Pawan L. Kulwal
Received27 Mar 2014
Accepted11 Jun 2014
Published23 Jun 2014


This outlook paper addresses the problem of the traceability of minor crops. These kinds of cultivations consist in a large number of plants locally distributed with a modest production in terms of cultivated acreage and quantity of final product. Because of globalization, the diffusion of minor crops is increasing due to their benefit for human health or their use as food supplements. Such a phenomenon implies a major risk for species substitution or uncontrolled admixture of manufactured plant products with severe consequences for the health of consumers. The need for a reliable identification system is therefore essential to evaluate the quality and provenance of minor agricultural products. DNA-based techniques can help in achieving this mission. In particular, the DNA barcoding approach has gained a role of primary importance thanks to its universality and versatility. Here, we present the advantages in the use of DNA barcoding for the characterization and traceability of minor crops based on our previous or ongoing studies at the ZooPlantLab (Milan, Italy). We also discuss how DNA barcoding may potentially be transferred from the laboratory to the food supply chain, from field to table.

1. DNA Barcoding for Plant Identification

Plants as primary producers are the basis of human nutrition from time immemorial. It is estimated that about 7,000 species of plants have been cultivated for consumption in human history (FAO data) and a large number of cultivars and varieties are also recognized. The Commission on Genetic Resources for Food and Agriculture ( estimated that 30 crops are usually referred currently as major agricultural products since they provide 95% of human food energy needs (e.g., rice, wheat, maize, and potato). These resources are widely monitored and well characterized with the analysis of DNA markers specifically developed for each cultivar (see, e.g., [13]). On the contrary, reliable characterization tools for the minor varieties are far from being defined. Minor crops include plants for food, pharmaceutical, cosmetic, and ornamental purposes with a modest production in terms of cultivated acreage and quantity of final product [4]. There are no fixed standard values to define a minor crop; however, conventionally, all the local varieties could be placed in this category. Most of these species or varieties show peculiar traits from the alimentary, pharmaceutical, or ornamental points of view. Some examples of minor crops that are now widely cultivated and worldwide distributed are Goji (Lycium barbarum L. [5]), Chokeberry (Aronia melanocarpa (Michx.), [6]), Peach Palm (Bactris gasipaes Kunth [7]), Teff (Eragrostis tef (Zucc.) [8]), and Okra (Abelmoschus esculentus (L.) Moench [9]). A large number of minor crops were usually produced and consumed locally [10] but, nowadays, the continuous demand by developed countries for identifying new active metabolites for human health and nutrition has increased their diffusion at global level [1114]. This phenomenon implies a major risk for species substitution or uncontrolled admixture of manufactured plant products. Substitution or adulteration can be deliberate (e.g., to maximize financial gains) or inadvertent (e.g., due to an insufficient knowledge by farmers) but they can have serious consequences for consumers at any rate [1419].

Given these premises, it is clear that the definition of a reliable traceability system is an aspect of major concern when plants, parts of plants, or plant extracts are used in food industry. The need for an unequivocal identification is also essential to start quality assurance procedures for agricultural products, to authenticate their geographical provenance (in the case of protected designation of origin), and to prevent commercial frauds and adulteration cases.

Agricultural products are subjected to strong processing and manufacturing before they are released as final products to the consumer. These processes alter the plant structure, thereby impeding the use of morphological characters to identify most of the agricultural products. To overcome this limit, the analysis of proteins and/or DNA is nowadays used as the main tool for plant traceability. However, although chemical or protein-based approaches are useful in characterizing the composition of fresh products, these methods can be biased by several factors such as the strong food manufacturing processes, the limited number of detectable isozymes, or the high tissue and developmental stage specificity of the markers [20]. DNA markers are more informative than protein or chemical based methods because DNA better resists industrial processes such as shredding, boiling, pressure cooking, or transformations mediated by chemical agents (see, e.g., [18, 21, 22]). This property allows a successful identification of plant material, even when it is present in small traces [23, 24]. Moreover, the availability of advanced technologies and efficient commercial kits for DNA extraction permits obtaining an acceptable yield of genetic material from processed or degraded plant material [25].

As a consequence, DNA markers have rapidly become the most used tools in the genetic analyses of crops and cultivars, as well as in the tracking and certification of the raw materials in food industry processes [2632]. PCR-based methods are more sensitive and faster than other technologies in characterizing agricultural products [13]. Among these, discontinuous molecular markers such as RAPDs, AFLPs, and their variants (e.g., ISSR, SSAP) have been successfully adopted for the characterization of crop species [24]. Moreover, sequencing-based systems such as single nucleotide polymorphisms (SNPs) and simple sequence repeats (SSRs) are also used because of their high level of polymorphism and high reproducibility [30]. However, being highly species specific, these approaches require access to the correct DNA sequence of the organisms and their application is often limited to a single species.

In the last decade, DNA barcoding was proposed as a universal DNA-based tool for species identification [33]. The name “DNA barcoding” figuratively refers to the way an infrared scanner univocally identifies a product by using the stripes of the universal product code (UPC). At the same time, this approach is based on the analysis of the variability within one or a few standard regions of the genome called “DNA barcode/s” [33]. The rationale of the method is that the DNA barcoding sequence/s univocally corresponds to each species (i.e., low intraspecific variability) but largely differs between taxa (i.e., high interspecific variability) [33, 34]. DNA barcoding has the advantage of combining three important innovations: molecularization of the identification approach (i.e., the investigation of DNA variability to differentiate taxa), standardization of the process (from sample collection to the analysis of molecular results), and computerization (i.e., the not redundant transposition of the data using informatics) [34].

Several plastidial and nuclear regions have been proposed as barcode regions for plants [3537] and some of them are now used for the identification of crop species, as recently reviewed by [38]. In 2009, the Plant Working Group of CBOL (consortium for the barcode of life) defined a standard core-barcode panel of markers based on the combination of portions of two coding plastidial regions: matK and rbcL [39, 40]. Despite their high universality in terms of amplification and sequencing success, the analysis of these coding regions fails in some cases due to the interspecific sharing of sequences [41]. Internal transcribed spacer regions of nuclear ribosomal DNA (ITS) were recommended as additional marker being highly variable in angiosperms [40]. ITS works well in many plant groups but, in some cases, incomplete concerted evolution and intraindividual variation make it unsuitable as universal plant barcode [40]. However, the combination of matK and rbcL with the plastidial intergenic noncoding region trnH-psbA increases the identification performance of DNA barcoding. As a consequence, the use of trnH-psbA is growing due to its easy amplification, and its high genetic variability among closely related taxa [15, 35, 42].

At the University of Milano-Bicocca (Milan, Italy), the ZooPlantLab group ( is one of the most active centers where DNA barcoding is used as a universal traceability system. The ZooPlantLab research team investigates concrete problems dealing with agricultural production of minor crops by transferring the analytical pipeline from the laboratory to food supply chain. This approach aims to overcome technical traceability problems in order to offer solid solutions to the market.

In the following sections, we present some of the potential applications and advantages of DNA barcoding for the identification and traceability along the food supply chain of minor crops. We also examine the most innovative approaches dealing with DNA barcoding that have been recently adopted to characterize these kinds of agricultural products.

2. Traceability of Minor Crops in the Supply Chain: The Case of Spices

Spices represent a clear example of minor crops. Most of these belong to Lamiaceae, a large family of 264 genera and almost 7,000 described species [78] characterized by aromatic oils and secondary metabolites. Thanks to their peculiar chemical profiles, these plants are commonly used as flavor for cooking, essences for cosmetics, and active components in medicines. Given their economical importance, many members of Lamiaceae have been investigated widely with different approaches ranging from morphology to chemistry and genetics in order to characterize their variability and improve the quality of cultivated varieties [25, 26, 79, 80].

Although some species showed distinctive morphological traits, this family encompasses many critical genera such as Thymus [43], where differences among closely related taxa are limited to few minor morphological characters. However, morphology could be ineffective for tracing spices along the supply chain (i.e., from the crop cultivation sites to the final products) which usually encompasses strong manufacturing processes such as crushing, powdering, or aqueous/alcoholic extraction of plant material.

International agencies such as the American Spice Trade Association (ASTA, and the European Spice Association (ESA, support the characterization of the phytochemical profile to assess the quality of herbs and spices. The evaluation of chemical characteristics is essential to standardize the industrial production of spices-derived products; however, in most cases, the analysis of chemical compounds is not able to univocally identify the original plants at the species level [26]. For this reason, we proposed the DNA barcoding approach as a universal and suitable tool to characterize and trace aromatic species. DNA analyses were conducted starting from different plant portions [22] or their derived products (e.g., oils, extracts) stored at different conditions (i.e., dried, frozen). In our study [22], we investigated 6 major groups of cooking spices (i.e., mint, basil, oregano, sage, thyme, and rosemary) also including their most relevant cultivars and hybrids. We collected samples at different stages of the industrial supply chain starting from seeds and plants cultivated by private farmers or in garden centers to commercial dried spices or other manufactured products. We also tested the performances of DNA barcoding starting from plant extracts. A good yield of high quality DNA was obtained through extraction protocols from all of the considered samples and then used for the next steps of the analysis (i.e., PCR and sequencing). A sufficient amount of DNA was also extracted from several of the plant extracts (Labra M., unpublished data) by using commercial kits. This first result confirmed that the industrial processes to transform the raw plant material such as drying, crushing, and aqueous or alcoholic extractions do not excessively degrade DNA. Among the four tested DNA barcoding regions (i.e., rbcL, matK, trnH-psbA, and rpoB), the trnH-psbA ranked the first in genetic divergence values among species, followed by matK and rbcL. On the contrary, rpoB showed the lowest sequence divergence among the tested taxa (see [22] for further details).

Our results partially supported the guidelines provided by the CBOL [40]. Indeed, the two core-barcode markers (i.e., matK + rbcL) properly assigned the tested spices to the expected genus and, in most cases, they also reached the species level. However, the highest identification performances were achieved by using the additional trnH-psbA barcode region. A clear example is that of basil (genus Ocimum), a group consisting of 30–160 species with many recognized cultivars [81]. In our study, exclusive trnH-psbA haplotypes, were found for almost all the tested cultivars, providing a reliable system for their identification. This result deserves to be highlighted because it is one of the first pieces of evidence supporting the usefulness of DNA barcoding in discriminating organisms at a taxonomic level lower than the species one.

Other important data revealed by our analyses concerned the capability of DNA barcoding to identify parental and hybrid species in some members of Lamiaceae. An example is represented by the case of peppermint (M. piperita L.), a sterile hybrid between M. aquatica L. × M. spicata L. [82, 83]. The plastidial markers used in this study confirmed that M. spicata L. is the maternal parental of M. piperita L. because both taxa showed the same DNA profile. However, to confirm definitively the hybrid origin of M. piperita L. and to identify the exact parental inheritance, the ITS2 codominant marker was sequenced (Labra M., unpublished data).

On the whole, the most relevant result of our work consisted in the assessment of the universality of DNA barcoding in a context of minor crops traceability. Using a single primer combination for each one of the few DNA barcoding markers and following standard laboratory protocols, it is possible to recognize the original species starting from different plant portions or derived processed materials. The same approach is also useful for validating several other herbal products commonly distributed on the market such as tea [50], saffron [44, 84], ginseng [69], black pepper [59], and many others (see also Table 1). These cases clearly emphasize the high versatility of DNA barcoding. It is an authentic functional tool for molecular traceability of agricultural products, as most of the minor crops have not yet been characterized with private markers such as SSR or SNP in order to allow a reliable DNA fingerprinting system. Moreover, DNA barcoding does not require any previous knowledge of the plant genome for the investigated species and the analytical procedures can be easily adopted by any laboratory equipped for molecular biology.

DNA barcoding applicationMinor crop/food productNotesReferences

Traceability of minor crops in the food supply chainAromatic plantsIdentification of spices from fresh samples to manufactured or processed products[22, 4347]
LegumesLegume seeds traceability[48, 49]
Herbal infusionsTraceability of tea products[50]
FruitIdentification and traceability of mango[51]
Identification of Citrus species[52]
Identification of Goji[53]
Identification of berries[54]
VegetablesIdentification of Capsicum cultivars[55]
Medical plant and food supplementsTraceability of medicinal plants[5658]

Commercial frauds and dangerous substitutionsAromatic productsIdentification of spices adulterants[59, 60]
Vegetal flourIdentification of buckwheat in commercial foodstuffs[61]
LegumesSeed admixture and adulteration[62, 63]
FruitIdentification and adulteration of fruit-based products[60, 64]
OilOil adulteration[65]
Medicinal plants/food supplementsDangerous substitution of Solanum lyratum with Aristolochia mollissima [66]
Adulteration of herbal products[67]
TeaContamination of tea products[68]

Molecular identification of minor crops in complex matricesNatural health productsIdentification of pharmaceutical plants in commercial products[69]
Juice and vegetal beveragesJuice authentication[7072]
HoneyIdentification of pollen and plant residuals[73]
Jams or yogurtIdentification of fruit in commercial products[74, 75]
Food supplementsIdentification of allergenic plants[76, 77]

3. Commercial Frauds and Dangerous Substitutions

Nowadays, the global diffusion of several minor crops in the absence of suitable traceability protocols is leading to frequent cases of plant substitution and inadvertent or deliberate adulteration. There are several documented examples of commercial frauds where minor crops were substituted with related taxa showing a higher productivity or biomass but without the agronomical and nutritional characteristics of the original species/cultivars [27, 85, 86] (see also Table 1). Astounding cases of this phenomenon were observed for some of the most common spices such as the Mediterranean oregano adulterated with Cistus incanus L., Rubus caesius L. [8789] and saffron substituted with Crocus vernus (L.) Hill, Carthamus, and Curcuma [19, 44, 84]. In this context, the use of DNA barcoding can be decisive because it can not only verify the presence/absence of the original species, but also identify the nature of the replaced species. One of the most striking substitution cases ever revealed by our investigations refers to fish meat (e.g., sold as slices, fillets, blocks, surimi, fish sticks, and fins). In this product category, the manufacturing processes often lead to the loss of any morphological diagnostic feature that may correctly identify the original species. In our molecular investigation [90], we documented the frequent substitutions of Palombo (i.e., the Italian vernacular name for Mustelus mustelus and Mustelus asterias) with other less valuable shark species. Our test showed that about 80% of the screened fish products did not correspond to these two species but to other species or genera, some of which are fished or marketed illegally. Starting from this experience, we tested the usefulness of DNA barcoding to evaluate the contamination of plant-based products. For example, in a pilot study on spices conducted by our group, we detected contaminant DNA in commercial samples of sage (i.e., Salvia) produced by local farmers. This DNA corresponded to species belonging to the family Poaceae (i.e., Festuca sp.). We hypothesized that these contaminant plants were accidentally grown together with the sage and fragments of them were erroneously collected, shredded, and consequently admixed to the final commercial products (Labra M., unpublished data). These conditions are dangerous if the contaminant taxon is toxic or allergenic for humans. A typical example is that of nuts and almonds which cause allergies in many people [91]. Several commercial foodstuffs (e.g., bakery, pastry, and snacks) showed contamination by these plants (see, e.g., [76, 92]). Also in this case, DNA barcoding acts as a very versatile tool, allowing the detection of both species (and many other allergenic taxa) also when they were present in traces [76].

Similarly, DNA barcoding can be efficient in identifying those plant species causing intoxication or poisoning in consumers. In recent years, plant exposures are among the most frequent poisoning cases reported by poison control centers [15, 93, 94]. Many of these are due to inadvertent misidentification as reported in [95] where the authors documented the exchange of spontaneous salad (Lactuca alpine (L.) Wallr.) with Aconitum spp. and wild garlic (Allium ursinum L.) with Colchicum sp. Both Aconitum and Colchicum contain toxic metabolites with severe consequences for human health after ingestion [96, 97]. Our analysis showed that DNA barcoding allowed us to detect the presence of poisonous plants and identify specific sequence-characterized amplified regions (SCARs) useful in a real-time PCR approach for rapid diagnosis in poison centers [60].

4. Plant Molecular Identification in Complex Matrices

Most food and cosmetic products are made up of a pool of plant species, major and minor crops, and spontaneous species. These are considered complex matrices [31] and, to establish traceability, the availability of universal tools able to univocally identify each plant species is needed. We underline that the assumptions for which DNA barcoding region(s) and the primers used are universal [33] imply that when the method is applied to complex matrices, PCR amplifications will produce several DNA barcoding amplicons, corresponding to different species. For this reason we tested this diagnostic method to identify the plant composition on different mixed products such as the commercial potpourris [14] and multiflower honeys (Bruni et al., submitted). For most of these herbal products, a detailed list of ingredients is not reported on the label; as a consequence, it is difficult to understand which species are used for their preparation and especially how safe these are for human health. In the case of potpourris, our results showed that the principal ingredients are simple aromatic plants (e.g., species of Lamiaceae) which are sometimes edible (e.g., Salvia officinalis L.; Ocimum basilicum L.) or ornamental (e.g., Salvia splendens Sellow ex J.A. Schultes, Lavandula angustifolia Miller) without negative effects on human health. In other cases these products revealed the presence of plants which produce natural toxic metabolites, such as alkaloids that are dangerous for human health [14, 98100]. However, the main critical element for the identification of plant-based complex matrices is the availability of DNA barcoding reference databases [101, 102]. To date, the Barcode of Life Data System (i.e., BOLD, [103]) contains 52,767 plant DNA sequences although several minor crops and local varieties are missing. Recent works, edited by our laboratory and other groups, highlighted the need for dedicated reference archives of DNA barcoding data for these kinds of plants [31, 67, 101, 102, 104, 105]. In another study, we demonstrated that, starting from a robust local database, it is possible to characterize the pollen composition of multiflower honey, one of the most complex food matrices. Our tests, conducted on honey samples produced in the Italian Alps, showed the conspicuous presence of endemic taxa. This result allowed us to assess not only the composition of honeys, but also their geographical origin (Bruni et al., submitted). See also Table 1 for further examples.

In comparison to agricultural products made by a single plant, the molecular characterization of complex matrices requires some technical advances, especially concerning the sequencing step. The traditional DNA-sequencing method [106] can only be adopted for direct sequencing of amplicons deriving from a single taxon. Complex matrices often contain mixtures of DNA from many individuals belonging to a certain taxonomic group (e.g., angiosperms) and DNA amplification may generate amplicons of the same size for a certain locus (e.g., a DNA barcode region for plant identification), therefore impeding direct sequencing with the Sanger approach. A possible solution could be the adoption of a preliminary cloning step to separate single DNA templates but this strategy has its own limitations (e.g., high costs) and can introduce biases (e.g., low representation of the sequenced colonies in the case of highly complex matrices [107, 108]). Recovering DNA sequences from the tens to thousands of specimens present in a complex food matrix requires the ability to read DNA from multiple templates in parallel. Since 2005, advances in the field of next-generation sequencing (NGS) technologies [109] have been helping in addressing this issue with ever-lowering costs. To date, several models of high-throughput sequencing devices have been commercially introduced based on different chemistries and detection techniques [108]. NGS technologies can generate up to tens of millions of sequencing reads in parallel and these approaches are being used in a variety of applications, including the traceability of food matrices containing agricultural products [73, 74, 110].

In conclusion, given the rapid evolution and standardization of NGS advances, we think that a universal approach such as DNA barcoding combined with them can offer a new opportunity for the traceability of minor crops from field to table.

Conflict of Interests

The authors declare that there is no conflict of interests regarding the publication of this paper.


  1. J. S. C. Smith, E. C. L. Chin, H. Shu et al., “An evaluation of the utility of SSR loci as molecular markers in maize (Zea mays L.): comparisons with data from RFLPS and pedigree,” Theoretical and Applied Genetics, vol. 95, no. 1-2, pp. 163–173, 1997. View at: Publisher Site | Google Scholar
  2. F. De Mattia, G. Lovicu, J. Tardaguila et al., “Genetic relationships between Sardinian and Spanish viticulture: the case of “Cannonau” and ‘Garnacha’,” Journal of Horticultural Science and Biotechnology, vol. 84, no. 1, pp. 65–71, 2009. View at: Google Scholar
  3. S. R. McCouch, K. Zhao, M. Wright et al., “Development of genome-wide SNP assays for rice,” Breeding Science, vol. 60, no. 5, pp. 524–535, 2010. View at: Publisher Site | Google Scholar
  4. J. Womach, Agriculture: A Glossary of Terms, Programs, and Laws, Congressional Research Service, Library of Congress, Washington, DC, USA, 2005.
  5. H. Amagase and N. R. Farnsworth, “A review of botanical characteristics, phytochemistry, clinical relevance in efficacy and safety of Lycium barbarum fruit (Goji),” Food Research International, vol. 44, no. 7, pp. 1702–1717, 2011. View at: Publisher Site | Google Scholar
  6. S. E. Kulling and H. M. Rawel, “Chokeberry (Aronia melanocarpa)-a review on the characteristic components and potential health effects,” Planta Medica, vol. 74, no. 13, pp. 1625–1634, 2008. View at: Publisher Site | Google Scholar
  7. J. M. Urpí, J. C. Weber, and C. R. Clement, Peach Palm, Bactris Gasipaes Kunth, vol. 20, Bioversity international, Rome, Italy, 1997.
  8. S. Ketema, Tef-Eragrostis tef (Zucc.), vol. 12, Bioversity international, Rome, Italy, 1997.
  9. M. Camciuc, M. Deplagne, G. Vilarem, and A. Gaset, “Okra—Abelmoschus esculentus L. (Moench.) a crop with economic potential for set aside acreage in France,” Industrial Crops and Products, vol. 7, no. 2-3, pp. 257–264, 1998. View at: Publisher Site | Google Scholar
  10. K. T. Moe, S. Kwon, and Y. Park, “Trends in genomics and molecular marker systems for the development of some underutilized crops,” Genes and Genomics, vol. 34, no. 5, pp. 451–466, 2012. View at: Publisher Site | Google Scholar
  11. E. Ernst, “The efficacy of herbal medicine-an overview,” Fundamental and Clinical Pharmacology, vol. 19, no. 4, pp. 405–409, 2005. View at: Publisher Site | Google Scholar
  12. H. A. Tindle, R. B. Davis, R. S. Phillips, and D. M. Eisenberg, “Trends in use of complementary and alternative medicine by us adults: 1997–2002,” Alternative Therapies in Health and Medicine, vol. 11, no. 1, pp. 42–49, 2005. View at: Google Scholar
  13. G. Heubl, “New aspects of DNA-based authentication of Chinese medicinal plants by molecular biological techniques,” Planta Medica, vol. 76, no. 17, pp. 1963–1974, 2010. View at: Publisher Site | Google Scholar
  14. L. Cornara, B. Borghesi, C. Canali et al., “Smart drugs: green shuttle or real drug?” International Journal of Legal Medicine, vol. 127, no. 6, pp. 1109–1123, 2013. View at: Publisher Site | Google Scholar
  15. I. Bruni, F. De Mattia, A. Galimberti et al., “Identification of poisonous plants by DNA barcoding approach,” International Journal of Legal Medicine, vol. 124, no. 6, pp. 595–603, 2010. View at: Publisher Site | Google Scholar
  16. S. L. Taylor and J. L. Baumert, “Cross-contamination of foods and implications for food allergic patients,” Current Allergy and Asthma Reports, vol. 10, no. 4, pp. 265–270, 2010. View at: Publisher Site | Google Scholar
  17. Z. P. Zeng and J. G. Jiang, “Analysis of the adverse reactions induced by natural product-derived drugs,” British Journal of Pharmacology, vol. 159, no. 7, pp. 1374–1391, 2010. View at: Publisher Site | Google Scholar
  18. J. Costa, I. Mafra, J. S. Amaral, and M. B. P. P. Oliveira, “Detection of genetically modified soybean DNA in refined vegetable oils,” European Food Research and Technology, vol. 230, no. 6, pp. 915–923, 2010. View at: Publisher Site | Google Scholar
  19. S. Babaei, M. Talebi, and M. Bahar, “Developing an SCAR and ITS reliable multiplex PCR-based assay forsafflower adulterant detection in saffron samples,” Food Control, vol. 35, no. 1, pp. 323–328, 2014. View at: Publisher Site | Google Scholar
  20. Y. J. Park, J. K. Lee, and N. S. Kim, “Simple sequence repeat polymorphisms (SSRPs) for evaluation of molecular diversity and germplasm classification of minor crops,” Molecules, vol. 14, no. 11, pp. 4546–4569, 2009. View at: Publisher Site | Google Scholar
  21. S. Soares, I. Mafra, J. S. Amaral, and M. B. P. P. Oliveira, “A PCR assay to detect trace amounts of soybean in meat sausages,” International Journal of Food Science and Technology, vol. 45, no. 12, pp. 2581–2588, 2010. View at: Publisher Site | Google Scholar
  22. F. De Mattia, I. Bruni, A. Galimberti, F. Cattaneo, M. Casiraghi, and M. Labra, “A comparative study of different DNA barcoding markers for the identification of some members of Lamiacaea,” Food Research International, vol. 44, no. 3, pp. 693–702, 2011. View at: Publisher Site | Google Scholar
  23. A. K. Lockley and R. G. Bardsley, “DNA-based methods for food authentication,” Trends in Food Science and Technology, vol. 11, no. 2, pp. 67–77, 2000. View at: Publisher Site | Google Scholar
  24. I. Mafra, I. M. Ferreira, and M. B. P. Oliveira, “Food authentication by PCR-based methods,” European Food Research and Technology, vol. 227, no. 3, pp. 649–665, 2008. View at: Publisher Site | Google Scholar
  25. J. Novak, S. Grausgruber-Gröger, and B. Lukas, “DNA-based authentication of plant extracts,” Food Research International, vol. 40, no. 3, pp. 388–392, 2007. View at: Publisher Site | Google Scholar
  26. M. Labra, M. Miele, B. Ledda, F. Grassi, M. Mazzei, and F. Sala, “Morphological characterization, essential oil composition and DNA genotyping of Ocimum basilicum L. cultivars,” Plant Science, vol. 167, no. 4, pp. 725–731, 2004. View at: Publisher Site | Google Scholar
  27. M. Woolfe and S. Primrose, “Food forensics: using DNA technology to combat misdescription and fraud,” Trends in Biotechnology, vol. 22, no. 5, pp. 222–226, 2004. View at: Publisher Site | Google Scholar
  28. S. Imazio, M. Labra, F. Grassi, A. Scienza, and O. Failla, “Chloroplast microsatellites to investigate the origin of grapevine,” Genetic Resources and Crop Evolution, vol. 53, no. 5, pp. 1003–1011, 2006. View at: Publisher Site | Google Scholar
  29. F. De Mattia, F. Grassi, S. Imazio, and M. Labra, “Chloroplast and nuclear DNA markers to characterize cultivated and spontaneous Ribes,” Plant Biosystems, vol. 142, no. 2, pp. 204–212, 2008. View at: Publisher Site | Google Scholar
  30. P. Kumar, V. K. Gupta, A. K. Misra, D. R. Modi, and B. K. Pandey, “Potential of molecular markers in plant biotechnology,” Plant Omics: Journal of Plant Molecular Biology & Omics, vol. 2, no. 4, pp. 141–162, 2009. View at: Google Scholar
  31. A. Galimberti, F. De Mattia, A. Losa et al., “DNA barcoding as a new tool for food traceability,” Food Research International, vol. 50, no. 1, pp. 55–63, 2013. View at: Publisher Site | Google Scholar
  32. H. Chuang, H. Lur, K. Hwu, and M. Chang, “Authentication of domestic Taiwan rice varieties based on fingerprinting analysis of microsatellite DNA markers,” Botanical Studies, vol. 52, no. 4, pp. 393–405, 2011. View at: Google Scholar
  33. P. D. Hebert, S. Ratnasingham, and J. R. de Waard, “Barcoding animal life: cytochrome c oxidase subunit 1 divergences among closely related species,” Proceedings of the Royal Society B: Biological Sciences, vol. 270, supplement 1, pp. S96–S99, 2003. View at: Google Scholar
  34. M. Casiraghi, M. Labra, E. Ferri, A. Galimberti, and F. de Mattia, “DNA barcoding: a six-question tour to improve users' awareness about the method,” Briefings in Bioinformatics, vol. 11, no. 4, Article ID bbq003, pp. 440–453, 2010. View at: Publisher Site | Google Scholar
  35. J. Shaw, E. B. Lickey, E. E. Schilling, and R. L. Small, “Comparison of whole chloroplast genome sequences to choose noncoding regions for phylogenetic studies in angiosperms: the Tortoise and the hare III,” American Journal of Botany, vol. 94, no. 3, pp. 275–288, 2007. View at: Publisher Site | Google Scholar
  36. A. J. Fazekas, K. S. Burgess, P. R. Kesanakurti et al., “Multiple multilocus DNA barcodes from the plastid genome discriminate plant species equally well,” PLoS ONE, vol. 3, no. 7, Article ID e2802, 2008. View at: Publisher Site | Google Scholar
  37. A. J. Fazekas, P. R. Kesanakurti, K. S. Burgess et al., “Are plant species inherently harder to discriminate than animal species using DNA barcoding markers?” Molecular Ecology Resources, vol. 9, no. 1, pp. 130–139, 2009. View at: Publisher Site | Google Scholar
  38. D. Mathew, “Biotechnology,” in Horticulture: Methods and Applications, K. V. Peter, Ed., chapter 2, pp. 25–50, New India Publishing Agency, New delhi, India, 1st edition, 2014. View at: Google Scholar
  39. M. L. Hollingsworth, A. Andra Clark, L. L. Forrest et al., “Selecting barcoding loci for plants: evaluation of seven candidate loci with species-level sampling in three divergent groups of land plants,” Molecular Ecology Resources, vol. 9, no. 2, pp. 439–457, 2009. View at: Publisher Site | Google Scholar
  40. P. M. Hollingsworth, S. W. Graham, and D. P. Little, “Choosing and using a plant DNA barcode,” PLoS ONE, vol. 6, no. 5, Article ID e19254, 2011. View at: Publisher Site | Google Scholar
  41. I. Bruni, F. De Mattia, S. Martellos et al., “DNA barcoding as an effective tool in improving a digital plant identification system: a case study for the area of Mt. Valerio, Trieste (NE Italy),” PloS one, vol. 7, no. 9, Article ID e43256, 2012. View at: Google Scholar
  42. W. J. Kress, D. L. Erickson, N. G. Swenson, J. Thompson, M. Uriarte, and J. K. Zimmerman, “Advances in the use of DNA barcodes to build a community phylogeny for tropical trees in a puerto rican forest dynamics plot,” PLoS ONE, vol. 5, no. 11, Article ID e15409, 2010. View at: Publisher Site | Google Scholar
  43. S. Federici, A. Galimberti, F. Bartolucci et al., “DNA barcoding to analyse taxonomically complex groups in plants: the case of Thymus (Lamiaceae),” Botanical Journal of the Linnean Society, vol. 171, no. 4, pp. 687–699, 2013. View at: Publisher Site | Google Scholar
  44. A. Gismondi, F. Fanali, J. M. M. Labarga, M. G. Caiola, and A. Canini, “Crocus sativus L. genomics and different DNA barcode applications,” Plant Systematics and Evolution, vol. 299, no. 10, pp. 1859–1863, 2013. View at: Publisher Site | Google Scholar
  45. S. Theodoridis, A. Stefanaki, M. Tezcan, C. Aki, S. Kokkini, and K. E. Vlachonasios, “DNA barcoding in native plants of the Labiatae (Lamiaceae) family from Chios Island (Greece) and the adjacent Çeşme-Karaburun Peninsula (Turkey),” Molecular Ecology Resources, vol. 12, no. 4, pp. 620–633, 2012. View at: Publisher Site | Google Scholar
  46. M. Kojoma, K. Kurihara, K. Yamada, S. Sekita, M. Satake, and O. Iida, “Genetic identification of cinnamon (Cinnamomum spp.) based on the trnL-trnF chloroplast DNA,” Planta Medica, vol. 68, no. 1, pp. 94–96, 2002. View at: Publisher Site | Google Scholar
  47. M. Wang, H. Zhao, L. Wang et al., “Potential use of DNA barcoding for the identification of Salvia based on cpDNA and nrDNA sequences,” Gene, vol. 528, no. 2, pp. 206–215, 2013. View at: Publisher Site | Google Scholar
  48. I. Ganopoulos, P. Madesis, N. Darzentas, A. Argiriou, and A. Tsaftaris, “Barcode High Resolution Melting (Bar-HRM) analysis for detection and quantification of PDO “fava Santorinis” (Lathyrus clymenum) adulterants,” Food Chemistry, vol. 133, no. 2, pp. 505–512, 2012. View at: Publisher Site | Google Scholar
  49. P. Madesis, I. Ganopoulos, A. Anagnostis, and A. Tsaftaris, “The application of Bar-HRM (Barcode DNA-High Resolution Melting) analysis for authenticity testing and quantitative detection of bean crops (Leguminosae) without prior DNA purification,” Food Control, vol. 25, no. 2, pp. 576–582, 2012. View at: Publisher Site | Google Scholar
  50. M. Y. Stoeckle, C. C. Gamble, R. Kirpekar, G. Young, S. Ahmed, and D. P. Little, “Commercial teas highlight plant DNA barcode identification successes and obstacles,” Scientific Reports, vol. 1, p. 42, 2011. View at: Publisher Site | Google Scholar
  51. T. Hidayat, A. Pancoro, and D. Kusumawaty, “Utility of matK gene to assess evolutionary relationship of genus Mangifera (anacardiaceae) in Indonesia and Thailand,” Biotropia, vol. 18, no. 2, pp. 74–80, 2011. View at: Google Scholar
  52. J. Yu, H. X. Yan, Z. H. Lu, and Z. Q. Zhou, “Screening potential DNA barcode regions of chloroplast coding genome for citrus and its related genera,” Scientia Agricultura Sinica, vol. 44, no. 2, pp. 341–348, 2011. View at: Google Scholar
  53. T. Xin, H. Yao, H. Gao et al., “Super food Lycium barbarum (Solanaceae) traceability via an internal transcribed spacer 2 barcode,” Food Research International, vol. 54, no. 2, pp. 1699–1704, 2013. View at: Google Scholar
  54. L. Jaakola, M. Suokas, and H. Häggman, “Novel approaches based on DNA barcoding and high-resolution melting of amplicons for authenticity analyses of berry species,” Food Chemistry, vol. 123, no. 2, pp. 494–500, 2010. View at: Publisher Site | Google Scholar
  55. R. L. Jarret, “DNA Barcoding in a crop genebank: the Capsicum annuum species complex,” Open Biology Journal, vol. 1, pp. 35–42, 2008. View at: Publisher Site | Google Scholar
  56. S. Chen, H. Yao, J. Han et al., “Validation of the ITS2 region as a novel DNA barcode for identifying medicinal plant species,” PLoS ONE, vol. 5, no. 1, Article ID e8613, 2010. View at: Publisher Site | Google Scholar
  57. T. Gao, H. Yao, J. Song et al., “Identification of medicinal plants in the family Fabaceae using a potential DNA barcode ITS2,” Journal of Ethnopharmacology, vol. 130, no. 1, pp. 116–121, 2010. View at: Publisher Site | Google Scholar
  58. Y. Zuo, Z. Chen, K. Kondo, T. Funamoto, J. Wen, and S. Zhou, “DNA barcoding of panax species,” Planta Medica, vol. 77, no. 2, pp. 182–187, 2011. View at: Publisher Site | Google Scholar
  59. V. A. Parvathy, V. P. Swetha, T. E. Sheeja, N. K. Leela, B. Chempakam, and B. Sasikumar, “DNA barcoding to detect chilli adulteration in traded black pepper powder,” Food Biotechnology, vol. 28, no. 1, pp. 25–40, 2014. View at: Google Scholar
  60. S. Federici, D. Fontana, A. Galimberti et al., “A rapid diagnostic approach to identify poisonous plants using DNA barcoding data,” Plant Biosystems. In press. View at: Google Scholar
  61. T. Hirao, S. Imai, H. Sawada, N. Shiomi, S. Hachimura, and H. Kato, “PCR method for detecting trace amounts of buckwheat (Fagopyrum spp.) in food,” Bioscience, Biotechnology and Biochemistry, vol. 69, no. 4, pp. 724–731, 2005. View at: Publisher Site | Google Scholar
  62. I. Ganopoulos, P. Madesis, and A. Tsaftaris, “Universal ITS2 Barcoding DNA Region Coupled with High-Resolution Melting (HRM) Analysis for Seed Authentication and Adulteration Testing in Leguminous Forage and Pasture Species,” Plant Molecular Biology Reporter, vol. 30, no. 6, pp. 1322–1328, 2012. View at: Publisher Site | Google Scholar
  63. I. Bosmali, I. Ganopoulos, P. Madesis, and A. Tsaftaris, “Microsatellite and DNA-barcode regions typing combined with High Resolution Melting (HRM) analysis for food forensic uses: a case study on lentils (Lens culinaris),” Food Research International, vol. 46, no. 1, pp. 141–147, 2012. View at: Publisher Site | Google Scholar
  64. C. C. Ng, C. Y. Lin, W. S. Tzeng, C. C. Chang, and Y. T. Shyu, “Establishment of an internal transcribed spacer (ITS) sequence-based differentiation identification procedure for mei (Prunus mume) and plum (Prunus salicina) and its use to detect adulteration in preserved fruits,” Food Research International, vol. 38, no. 1, pp. 95–101, 2005. View at: Publisher Site | Google Scholar
  65. I. Ganopoulos, C. Bazakos, P. Madesis, P. Kalaitzis, and A. Tsaftaris, “Barcode DNA high-resolution melting (Bar-HRM) analysis as a novel close-tubed and accurate tool for olive oil forensic use,” Journal of the Science of Food and Agriculture, vol. 93, no. 9, pp. 2281–2286, 2013. View at: Publisher Site | Google Scholar
  66. M. Li, K. Au, H. Lam et al., “Identification of Baiying (Herba Solani Lyrati) commodity and its toxic substitute Xungufeng (Herba Aristolochiae Mollissimae) using DNA barcoding and chemical profiling techniques,” Food Chemistry, vol. 135, no. 3, pp. 1653–1658, 2012. View at: Publisher Site | Google Scholar
  67. S. G. Newmaster, M. Grguric, D. Shanmughanandhan, S. Ramalingam, and S. Ragupathy, “DNA barcoding detects contamination and substitution in North American herbal products,” BMC Medicine, vol. 11, no. 1, p. 222, 2013. View at: Publisher Site | Google Scholar
  68. B. Dhiman and M. Singh, “Molecular detection of Cashew Husk (Anacardium occidentale) adulteration in market samples of dry tea (Camellia sinensis),” Planta Medica, vol. 69, no. 9, pp. 882–884, 2003. View at: Publisher Site | Google Scholar
  69. L. J. Wallace, S. M. A. L. Boilard, S. H. C. Eagle, J. L. Spall, S. Shokralla, and M. Hajibabaei, “DNA barcodes for everyday life: routine authentication of Natural Health Products,” Food Research International, vol. 49, no. 1, pp. 446–452, 2012. View at: Publisher Site | Google Scholar
  70. M. A. Faria, A. Magalhães, M. E. Nunes, and M. B. P. P. Oliveira, “High resolution melting of trnL amplicons in fruit juices authentication,” Food Control, vol. 33, no. 1, pp. 136–141, 2013. View at: Publisher Site | Google Scholar
  71. M. Li, K. Wong, W. Chan et al., “Establishment of DNA barcodes for the identification of the botanical sources of the Chinese “cooling” beverage,” Food Control, vol. 25, no. 2, pp. 758–766, 2012. View at: Publisher Site | Google Scholar
  72. J. Han, Y. Wu, W. Huang et al., “PCR and DHPLC methods used to detect juice ingredient from 7 fruits,” Food Control, vol. 25, no. 2, pp. 696–703, 2012. View at: Publisher Site | Google Scholar
  73. A. Valentini, C. Miquel, and P. Taberlet, “DNA barcoding for honey biodiversity,” Diversity, vol. 2, no. 4, pp. 610–617, 2010. View at: Publisher Site | Google Scholar
  74. A. Ortola-Vidal, H. Schnerr, M. Rojmyr, F. Lysholm, and A. Knight, “Quantitative identification of plant genera in food products using PCR and Pyrosequencing technology,” Food Control, vol. 18, no. 8, pp. 921–927, 2007. View at: Publisher Site | Google Scholar
  75. M. Arleo, F. Ruibal, J. Pereyra, E. Miquel, M. Fernández, and C. Martínez, “A DNA-based approach to discriminate between quince and apple in quince jams,” International Food Research Journal, vol. 19, no. 4, pp. 1471–1477, 2012. View at: Google Scholar
  76. T. Yano, Y. Sakai, K. Uchida et al., “Detection of walnut residues in processed foods by polymerase chain reaction,” Bioscience, Biotechnology and Biochemistry, vol. 71, no. 7, pp. 1793–1796, 2007. View at: Publisher Site | Google Scholar
  77. P. Madesis, I. Ganopoulos, I. Bosmali, and A. Tsaftaris, “Barcode High Resolution Melting analysis for forensic uses in nuts: a case study on allergenic hazelnuts (Corylus avellana),” Food Research International, vol. 50, no. 1, pp. 351–360, 2013. View at: Publisher Site | Google Scholar
  78. G. Singh, Plant Systematics: an Integrated Approach, Science Publishers, New York, NY, USA, 2004.
  79. H. Trindade, “Molecular biology of aromatic plants and spices. A review,” Flavour and Fragrance Journal, vol. 25, no. 5, pp. 272–281, 2010. View at: Publisher Site | Google Scholar
  80. M. Viuda-Martos, Y. Ruiz-Navajas, J. Fernández-López, and J. A. Pérez-Álvarez, “Spices as functional foods,” Critical Reviews in Food Science and Nutrition, vol. 51, no. 1, pp. 13–28, 2011. View at: Publisher Site | Google Scholar
  81. A. Paton, M. R. Harley, and M. M. Harley, “Ocimum: an overview of classification and relationships,” in Basil: The Genus Ocimum, pp. 1–38, 1999. View at: Google Scholar
  82. A. O. Tucker, “The truth about mints,” Herb Companion, vol. 4, pp. 51–52, 1992. View at: Google Scholar
  83. V. Gobert, S. Moja, M. Colson, and P. Taberlet, “Hybridization in the section Mentha (Lamiaceae) inferred from AFLP markers,” American Journal of Botany, vol. 89, no. 12, pp. 2017–2023, 2002. View at: Google Scholar
  84. A. Torelli, M. Marieschi, and R. Bruni, “Authentication of saffron (Crocus sativus L.) in different processed, retail products by means of SCAR markers,” Food Control, vol. 36, no. 1, pp. 126–131, 2014. View at: Google Scholar
  85. K. Dhanya and B. Sasikumar, “Molecular marker based adulteration detection in traded food and agricultural commodities of plant origin with special reference to spices,” Current Trends in Biotechnology and Pharmacy, vol. 4, no. 1, pp. 454–489, 2010. View at: Google Scholar
  86. P. Posadzki, L. Watson, and E. Ernst, “Contamination and adulteration of herbal medicinal products (HMPs): an overview of systematic reviews,” European Journal of Clinical Pharmacology, vol. 69, no. 3, pp. 295–307, 2013. View at: Publisher Site | Google Scholar
  87. M. Marieschi, A. Torelli, F. Poli, A. Bianchi, and R. Bruni, “Quality control of commercial Mediterranean oregano: development of SCAR markers for the detection of the adulterants Cistus incanus L., Rubus caesius L. and Rhus coriaria L.,” Food Control, vol. 21, no. 7, pp. 998–1003, 2010. View at: Publisher Site | Google Scholar
  88. M. Marieschi, A. Torelli, A. Bianchi, and R. Bruni, “Detecting Satureja montana L. and Origanum majorana L. by means of SCAR-PCR in commercial samples of Mediterranean oregano,” Food Control, vol. 22, no. 3-4, pp. 542–548, 2011. View at: Publisher Site | Google Scholar
  89. M. Marieschi, A. Torelli, A. Bianchi, and R. Bruni, “Development of a SCAR marker for the identification of Olea europaea L.: a newly detected adulterant in commercial Mediterranean oregano,” Food Chemistry, vol. 126, no. 2, pp. 705–709, 2011. View at: Publisher Site | Google Scholar
  90. M. Barbuto, A. Galimberti, E. Ferri et al., “DNA barcoding reveals fraudulent substitutions in shark seafood products: the Italian case of “palombo” (Mustelus spp.),” Food Research International, vol. 43, no. 1, pp. 376–381, 2010. View at: Publisher Site | Google Scholar
  91. Z. Hubalkova and E. Rencova, “One-step multiplex PCR method for the determination of pecan and Brazil nut allergens in food products,” Journal of the Science of Food and Agriculture, vol. 91, no. 13, pp. 2407–2411, 2011. View at: Publisher Site | Google Scholar
  92. J. Costa, I. Mafra, I. Carrapatoso, and M. B. P. P. Oliveira, “Almond allergens: molecular characterization, detection, and clinical relevance,” Journal of Agricultural and Food Chemistry, vol. 60, no. 6, pp. 1337–1349, 2012. View at: Publisher Site | Google Scholar
  93. F. M. Hammouda, A. M. Rizk, M. M. El-Missiry et al., “Poisonous plants contaminating edible ones and toxic substances in plant foods. IV. Phytochemistry and toxicity of Lolium temulentum,” International Journal of Crude Drug Research, vol. 26, no. 4, pp. 240–245, 1988. View at: Google Scholar
  94. R. Walker, “Criteria for risk assessment of botanical food supplements,” Toxicology Letters, vol. 149, no. 1–3, pp. 187–195, 2004. View at: Publisher Site | Google Scholar
  95. M. L. Colombo, F. Assisi, T. D. Puppa et al., “Most commonly plant exposures and intoxications from outdoor toxic plants,” Journal of Pharmaceutical Sciences and Research, vol. 2, no. 7, pp. 417–425, 2010. View at: Google Scholar
  96. Y. Finkelstein, S. E. Aks, J. R. Hutson et al., “Colchicine poisoning: the dark side of an ancient drug,” Clinical Toxicology, vol. 48, no. 5, pp. 407–414, 2010. View at: Publisher Site | Google Scholar
  97. M. A. Berdai, S. Labib, K. Chetouani, and M. Harandou, “Atropa Belladonna intoxication: a case report,” Pan African Medical Journal, vol. 11, p. 72, 2012. View at: Google Scholar
  98. E. Röder, “Medicinal plants in Europe containing pyrrolizidine alkaloids,” Pharmazie, vol. 50, no. 2, pp. 83–98, 1995. View at: Google Scholar
  99. C. Franz, R. Chizzola, J. Novak, and S. Sponza, “Botanical species being used for manufacturing plant food supplements (PFS) and related products in the EU member states and selected third countries,” Food and Function, vol. 2, no. 12, pp. 720–730, 2011. View at: Publisher Site | Google Scholar
  100. H. Wiedenfeld and J. Edgar, “Toxicity of pyrrolizidine alkaloids to humans and ruminants,” Phytochemistry Reviews, vol. 10, no. 1, pp. 137–151, 2011. View at: Publisher Site | Google Scholar
  101. K. S. Burgess, A. J. Fazekas, P. R. Kesanakurti et al., “Discriminating plant species in a local temperate flora using the rbcL+matK DNA barcode,” Methods in Ecology and Evolution, vol. 2, no. 4, pp. 333–340, 2011. View at: Publisher Site | Google Scholar
  102. A. Sandionigi, A. Galimberti, M. Labra et al., “Analytical approaches for DNA barcoding data-how to find a way for plants?” Plant Biosystems, vol. 146, no. 4, pp. 805–813, 2012. View at: Publisher Site | Google Scholar
  103. S. Ratnasingham and P. D. N. Hebert, “BOLD: the barcode of life data system: barcoding,” Molecular Ecology Notes, vol. 7, no. 3, pp. 355–364, 2007. View at: Publisher Site | Google Scholar
  104. F. De Mattia, R. Gentili, I. Bruni et al., “A multi-marker DNA barcoding approach to save time and resources in vegetation surveys,” Botanical Journal of the Linnean Society, vol. 169, no. 3, pp. 518–529, 2012. View at: Publisher Site | Google Scholar
  105. M. L. Kuzmina, K. L. Johnson, H. R. Barron, and P. D. N. Hebert, “Identification of the vascular plants of Churchill, Manitoba, using a DNA barcode library,” BMC Ecology, vol. 12, p. 25, 2012. View at: Publisher Site | Google Scholar
  106. F. Sanger, S. Nicklen, and A. R. Coulson, “DNA sequencing with chain-terminating inhibitors,” Proceedings of the National Academy of Sciences of the United States of America, vol. 74, no. 12, pp. 5463–5467, 1977. View at: Google Scholar
  107. M. Hajibabaei, S. Shokralla, X. Zhou, G. A. C. Singer, and D. J. Baird, “Environmental barcoding: a next-generation sequencing approach for biomonitoring applications using river benthos,” PLoS ONE, vol. 6, no. 4, Article ID e17497, 2011. View at: Publisher Site | Google Scholar
  108. S. Shokralla, J. L. Spall, J. F. Gibson, and M. Hajibabaei, “Next-generation sequencing technologies for environmental DNA research,” Molecular Ecology, vol. 21, no. 8, pp. 1794–1805, 2012. View at: Publisher Site | Google Scholar
  109. M. L. Metzker, “Sequencing technologies—the next generation,” Nature Reviews Genetics, vol. 11, no. 1, pp. 31–46, 2010. View at: Publisher Site | Google Scholar
  110. M. L. Coghlan, J. Haile, J. Houston et al., “Deep sequencing of plant and animal DNA contained within traditional Chinese medicines reveals legality issues and health safety concerns,” PLoS Genetics, vol. 8, no. 4, Article ID e1002657, 2012. View at: Publisher Site | Google Scholar

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