Skip to main content
SpringerLink
Log in

High-Throughput Sequencing for the Authentication of Food Products: Problems and Perspectives

  • Reviews and Theoretical Articles
  • Published:
Russian Journal of Genetics Aims and scope Submit manuscript

Abstract

The quality of food products is one of the key factors affecting health, life expectancy, and work capacity. An important quality parameter is compliance with the claimed composition, the violation of which can lead to negative repercussion for purchasers which is a risk of allergic reactions and toxic and other side effects. To control the composition in most cases, organoleptic, macro- and microscopic, analytical chemistry methods are used. Molecular methods based on amplification and fluorescence detection of marker DNA fragments, as well as approaches based on mass spectrometry, are also employed. However, owing to certain limitations, such as insufficient sensitivity and incompleteness of the databases used, these methods often do not allow for an accurate analysis of multicomponent mixtures. At present, this problem becomes more urgent because of the rapid development of processing technologies of raw ingredients for the food industry, as well as the globalization of food markets, which leads to the need for development of new approaches to solve this problem. An important addition to the existing methods can become high-throughput sequencing technologies (so-called new generation sequencing, NGS), which allow fast and cheap determination of hundreds of millions of DNA fragments. In this review, the possibilities and prospects of their use for controlling the composition of food products are considered.

This is a preview of subscription content, log in via an institution to check access.

Access this article

Log in via an institution

Price excludes VAT (USA)
Tax calculation will be finalised during checkout.

Instant access to the full article PDF.

Similar content being viewed by others

References

  1. Stoeckle, M.Y., Gamble, C.C., Kirpekar, R., et al., Commercial teas highlight plant DNA barcode identification successes and obstacles, Sci. Rep., 2011, vol. 1, p. 42. doi 10.1038/srep00042

    Article  PubMed  CAS  PubMed Central  Google Scholar 

  2. Newmaster, S.G., Grguric, M., Shanmughanandhan, D., et al., DNA barcoding detects contamination and substitution in North American herbal products, BMC Med., 2013, vol. 11, p. 222. doi 0.1186/1741-7015-11-222

    Article  PubMed  CAS  PubMed Central  Google Scholar 

  3. Vassou, S.L., Kusuma, G., and Parani, M., DNA barcoding for species identification from dried and powdered plant parts: a case study with authentication of the raw drug market samples of Sida cordifolia, Gene, 2015, vol. 559, no. 1, pp. 86–93. doi 10.1016/j.gene.2015.01.025

    Article  PubMed  CAS  Google Scholar 

  4. Singtonat, S. and Osathanunkul, M., Fast and reliable detection of toxic Crotalaria spectabilis Roth. in Thunbergia laurifolia Lindl. herbal products using DNA barcoding coupled with HRM analysis, BMC Complement. Altern. Med., 2015, vol. 15, p. 162. doi 10.1186/s12906-015-0692-6

    Article  PubMed  CAS  PubMed Central  Google Scholar 

  5. Kane, D.E. and Hellberg, R.S., Identification of species in ground meat products sold on the US commercial market using DNA-based methods, Food Control, 2016, vol. 59, pp. 158–163. doi 10.1016/j.foodcont. 2015.05.020

    Article  Google Scholar 

  6. Cawthorn, D.M., Steinman, H.A., and Hoffman, L.C., A high incidence of species substitution and mislabelling detected in meat products sold in South Africa, Food Control, 2013, vol. 32, no. 2, pp. 440–449. doi 10.1016/j.foodcont.2013.01.008

    Article  Google Scholar 

  7. Kozlova, T.K., Safety and quality control of raw meat and meat products in Russia, RJOAS, 2012, vol. 5, no. 5, pp. 33–38.

    Article  Google Scholar 

  8. Khaksar, R., Carlson, T., Schaffner, D.W., et al., Unmasking seafood mislabeling in U.S. markets: DNA barcoding as a unique technology for food authentication and quality control, Food Control, 2015, vol. 56, pp. 71–76.

    Article  CAS  Google Scholar 

  9. Giusti, A., Armani, A., and Sotelo, C.G., Advances in the analysis of complex food matrices: species identification in surimi-based products using Next Generation Sequencing technologies, PLoS One, 2017, vol. 12, no. 10, pp. e0185586

    Article  PubMed  CAS  PubMed Central  Google Scholar 

  10. Marko, P.B., Lee, S.C., Rice, A.M., et al., Fisheries: mislabelling of a depleted reef fish, Nature, 2004, vol. 430, no. 6997, pp. 309–310.

    Article  PubMed  CAS  Google Scholar 

  11. Masiri, J., Benoit, L., Barrios-Lopez, B., et al., Development and validation of a rapid test system for detection of pork meat and collagen residues, Meat Sci., 2016, vol. 121, pp. 397–402.

    Article  PubMed  CAS  Google Scholar 

  12. Ayaz, Y., Ayaz, N.D., and Erol, I., Detection of species in meat and meat products using enzyme-linked immunosorbent assay, J. Muscle Foods, 2006, vol. 17, no. 2, pp. 214–220.

    Article  Google Scholar 

  13. Flores-Munguia, M.E., Bermudez-Almada, M.C., and Vazquez-Moreno, L., A research note: detection of adulteration in processed traditional meat products, J. Muscle Foods, 2000, vol. 11, no. 4, pp. 319–325.

    Article  Google Scholar 

  14. Hsieh, Y.H.P., Woodward, B.B., and Ho, S.H., Detection of species substitution in raw and cooked meats using immunoassays, J. Food Prot., 1995, vol. 58, no. 5, pp. 555–559.

    Article  Google Scholar 

  15. Fang, X. and Zhang, C., Detection of adulterated murine components in meat products by TaqMan(c) real-time PCR, Food Chem., 2016, vol. 192, pp. 485–490.

    Article  PubMed  CAS  Google Scholar 

  16. Macedo-Silva, A., Shimokomakia, M., Vazb, A.J., et al., Textured soy protein quantification in commercial hamburger, J. Food Compos. Anal., 2001, vol. 14, no. 5, pp. 469–478. doi 10.1006/jfca.2001.1014

    Article  CAS  Google Scholar 

  17. Hanner, R., Becker, S., Ivanova, N.V., et al., FISHBOL and seafood identification: geographically dispersed case studies reveal systemic market substitution across Canada, Mit. DNA, 2011, vol. 22, suppl. 1, pp. 106–122. doi 10.3109/19401736.2011.588217

    Article  CAS  Google Scholar 

  18. Cawthorn, D.M., Duncan, J., Kastern, C., et al., Fish species substitution and misnaming in South Africa: an economic, safety and sustainability conundrum revisited, Food Chem., 2015, vol. 185, pp. 165–181.

    Article  PubMed  CAS  Google Scholar 

  19. Willette, D.A. and Cheng, S.H., Delivering on seafood traceability under the new U.S. import monitoring program, Ambio, 2017, vol. 47, no. 1, pp. 25–30. doi 10.1007/s13280-017-0936-4

    Article  PubMed  Google Scholar 

  20. Tremlova, B., Sarha, P., Pospiech, M., et al., Histological analysis of different kinds of mechanically recovered meat, Arch. Lebensmittelhygiene, 2006, vol. 57, no. 3, pp. 85–91.

    Google Scholar 

  21. Prosser, S.W.J. and Hebert, P.D.N., Rapid identification of the botanical and entomological sources of honey using DNA metabarcoding, Food Chem., 2017, vol. 214, pp. 183–191. doi 10.1016/j.foodchem.2016. 07.077

    Article  PubMed  CAS  Google Scholar 

  22. Ramon-Sierra, J.M., Ruiz-Ruiz, J.C., and de la Luz Ortiz-Vazquez, E., Electrophoresis characterisation of protein as a method to establish the entomological origin of stingless bee honeys, Food Chem., 2015, vol. 183, pp. 43–48. doi 10.1016/j.foodchem.2015.03.015

    Article  PubMed  CAS  Google Scholar 

  23. Khoddami, A., Wilkes, M.A., and Roberts, T.H., Techniques for analysis of plant phenolic compounds, Molecules, 2013, vol. 18, no. 2, pp. 2328–2375.

    Article  PubMed  CAS  Google Scholar 

  24. Cubero-Leon, E., Penalver, R., and Maquet, A., Review on metabolomics for food authentication, Food Res. Int., 2014, vol. 60, pp. 95–107. doi 10.1016/j. foodres.2013.11.041

    Article  CAS  Google Scholar 

  25. Laursen, K.H., Schjoerringa, J.K., Kelly, S.D., et al., Authentication of organically grown plants–advantages and limitations of atomic spectroscopy for multielement and stable isotope analysis, Trends Anal. Chem., 2014, vol. 59, pp. 73–82. doi 10.1016/j.trac.2014.04.008

    Article  CAS  Google Scholar 

  26. Balog, J., Perenyi, D., Guallar-Hoyas, C., et al., Identification of the species of origin for meat products by rapid evaporative ionization mass spectrometry, J. Agric. Food Chem., 2016, vol. 64, no. 23, pp. 4793–4800. doi 10.1021/acs.jafc.6b01041

    Article  PubMed  CAS  Google Scholar 

  27. Sciuto, S., Esposito, G., Dell’Atti, L., et al., Rapid screening technique to identify Sudan dyes (I to IV) in adulterated tomato sauce, chili powder, and palm oil by innovative high-resolution mass spectrometry, J. Food Prot., 2017, vol. 80, no. 4, pp. 640–644. doi 10.4315/0362-028X.JFP-16-313

    Article  PubMed  CAS  Google Scholar 

  28. Klyuev, N.A. and Brodskii, E.S., Modern methods for mass spectromety analysis of organic compounds, Ross. Khim. Zh., 2002, vol. 50, pp. 57–63.

    Google Scholar 

  29. Hao, N. and Wang, K., Recent development of electrochemiluminescence sensors for food analysis, Anal. Bioanal. Chem., 2016, vol. 408, no. 25, pp. 7035–7048. doi 10.1007/s00216-016-9548-2

    Article  PubMed  CAS  Google Scholar 

  30. Asensio, L., Gonzalez, I., Garcia, T., et al., Determination of food authenticity by enzyme-linked immunosorbent assay (ELISA), Food Control, 2008, vol. 19, no. 1, pp. 1–8. doi 10.1016/j.foodcont.2007.02.010

    Article  CAS  Google Scholar 

  31. Puchades, R. and Maquieira, A., ELISA tools for food PDO authentication, Comprehensive Analyt. Chem., 2013, vol. 60, pp. 145–193. doi 10.1016/B978-0-444-59562-1.00007-4

    Article  CAS  Google Scholar 

  32. Ivens, K.O., Baumert, J.L., Hutkins, R.L., et al., The effect of different methods of fermentation on the detection of milk protein residues in retail cheese by Enzyme-Linked Immunosorbent Assay (ELISA), J. Food Sci., 2017, vol. 82, no. 11, pp. 2752–2758.

    Article  PubMed  CAS  Google Scholar 

  33. Macedo-Silva, A., Barbosa, S.F., Alkmin, M.G., et al., Hamburger meat identification by dot-ELISA, Meat Sci., 2000, vol. 56, no. 2, pp. 189–192.

    Article  PubMed  CAS  Google Scholar 

  34. Liu, L.H., Chen, F.C., Dorsey, J.L., et al., Sensitive monoclonal antibody-based sandwich ELISA for the detection of porcine skeletal muscle in meat and feed products, J. Food Sci., 2006, vol. 71, no. 1, pp. M1–M6. doi 10.1111/j.1365-2621.2006.tb12393.x

    Article  CAS  Google Scholar 

  35. Geng, T., Liu, K., Frazier, R., et al., Development of a sandwich ELISA for quantification of Gly m 4, a soybean allergen, J. Agric. Food Chem., 2015, vol. 63, no. 20, pp. 4947–4953.

    Article  PubMed  CAS  Google Scholar 

  36. Castillo, D.S. and Cassola, A., Novel sensitive monoclonal antibody based competitive enzyme-linked immunosorbent assay for the detection of raw and processed bovine beta-casein, PLoS One, 2017, vol. 12, no. 7. doi 10.1371/journal.pone.0182447

    Google Scholar 

  37. Kiening, M., Niessner, R., Drs, E., et al., Sandwich immunoassays for the determination of peanut and hazelnut traces in foods, J. Agric. Food Chem., 2005, vol. 53, no. 9, pp. 3321–3327.

    Article  PubMed  CAS  Google Scholar 

  38. Williams, K.M., Westphal, C.D., and Shriver-Lake, L.C., Determination of egg proteins in snack food and noodles, J. AOAC Int., 2004, vol. 87, no. 6, pp. 1485–1491.

    PubMed  CAS  Google Scholar 

  39. Santiago-Felipe, S., Tortajada-Genaro, L.A., Puchades, R., et al., Recombinase polymerase and enzyme-linked immunosorbent assay as a DNA amplification-detection strategy for food analysis, Anal. Chim. Acta, 2014, vol. 811, pp. 81–87. doi 10.1016/j.aca.2013.12.017

    Article  PubMed  CAS  Google Scholar 

  40. Hossain, M.A.M., Ali, M.E., Sultana, S., et al., Quantitative tetraplex real-time polymerase chain reaction assay with TaqMan probes discriminates cattle, buffalo, and porcine materials in food chain, J. Agric. Food Chem., 2017, vol. 65, no. 19, pp. 3975–3985. doi 10.1021/acs.jafc.7b00730

    Article  PubMed  CAS  Google Scholar 

  41. Dalama, J., Vieites, J.M., and Espineira, M., Detection of the causal agents of keriorrhea (Lepidocybium flavobrunneum and Ruvettus pretiosus) by means of real time PCR, Food Chem., 2015, vol. 174, pp. 326–329. doi 10.1016/j.foodchem.2014.11.070

    Article  PubMed  CAS  Google Scholar 

  42. Taboada, L., Sanchez, A., and Sotelo, C.G., A new real-time PCR method for rapid and specific detection of ling (Molva molva), Food Chem., 2017, vol. 228, pp. 469–475. doi 10.1016/j.foodchem.2017.01.117

    Article  PubMed  CAS  Google Scholar 

  43. Roder, M., Vieths, S., and Holzhauser, T., Sensitive and specific detection of potentially allergenic almond (Prunus dulcis) in complex food matrices by Taqman (R) real-time polymerase chain reaction in comparison to commercially available protein-based enzymelinked immunosorbent assay, Anal. Chim. Acta, 2011, vol. 685, no. 1, pp. 74–83. doi 10.1016/j.aca.2010.11.019

    Article  PubMed  CAS  Google Scholar 

  44. Madesis, P., Ganopoulos, I., Anagnostis, A., et al., 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, 2012, vol. 25, no. 2, pp. 576–582. doi 10.1016/j.foodcont. 2011.11.034

    Article  CAS  Google Scholar 

  45. Bosmali, I., Ganopoulos, I., Madesisc, P., et al., 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 Res. Int., 2012, vol. 46, no. 1, pp. 141–147. doi 10.1016/j.foodres.2011.12.013

    Article  CAS  Google Scholar 

  46. Osathanunkul, M., Madesis, P., Ounjai, S., Somboonchai, R., Lithanatudom, P., Chaowasku, T., Wipasa, J., and Suwannapoom, C., Identification of Uvaria sp. by barcoding coupled with high-resolution melting analysis (Bar-HRM), Genet. Mol. Res., 2016, vol. 15, no. 1. doi 10.4238/gmr.15017405

    Google Scholar 

  47. Jaakola, L., Suokas, M., and Haggman, H., Novel approaches based on DNA barcoding and high-resolution melting of amplicons for authenticity analyses of berry species, Food Chem., 2010, vol. 123, no. 2, pp. 494–500. doi 10.1016/j.foodchem.2010.04.069

    Article  CAS  Google Scholar 

  48. Mader, E., Lohwasser, U., Borner, A., et al., Population structures of genebank accessions of Salvia officinalis L. (Lamiaceae) revealed by high resolution melting analysis, Biochem. Systemat. Ecol., 2010, vol. 38, no. 2, pp. 178–186. doi 10.1016/j.bse.2010.01.001

    Article  CAS  Google Scholar 

  49. Hwang, J.H., Ahn, S.G., Oh, J.Y., et al., Functional characterization of watermelon (Citrullus lanatus L.) EST-SSR by gel electrophoresis and high resolution melting analysis, Sci. Hortic., 2011, vol. 130, no. 4, pp. 715–724. doi 10.1016/j.scienta.2011.08.014

    Article  CAS  Google Scholar 

  50. Goodwin, S., McPherson, J.D., and McCombie, W.R., Coming of age: ten years of next-generation sequencing technologies, Nat. Rev. Genet., 2016, vol. 17, no. 6, pp. 333–351. doi 10.1038/nrg.2016.49

    Article  PubMed  CAS  Google Scholar 

  51. Mardis, E.R., DNA sequencing technologies: 2006–2016, Nat. Protoc., 2017, vol. 12, no. 2, pp. 213–218. doi 10.1038/nprot.2016.182

    Article  PubMed  CAS  Google Scholar 

  52. Krasnov, Ya.M., Guseva, N.P., Sharapova, N.A., et al., Modern methods of DNA sequencing (a review), Probl. Osobo Opasnykh Infekts., 2014, no. 2, pp. 73–79. doi 10.21055/0370-1069-2014-2-73-79

    Article  Google Scholar 

  53. Hawkins, J., de Vere, N., Griffith, A., Ford, C.R., et al., Using DNA metabarcoding to identify the floral composition of honey: a new tool for investigating honey bee foraging preferences, PLoS One, 2015, vol. 10, no. 8. e0134735. doi 10.1371/journal.pone. 0134735

    Article  PubMed  CAS  PubMed Central  Google Scholar 

  54. Munoz-Colmenero, M., Martinez, J.L., Roca, A., et al., NGS tools for traceability in candies as high processed food products: Ion Torrent PGM versus conventional PCR-cloning, Food Chem., 2017, vol. 214, pp. 631–636. doi 10.1016/j.foodchem.2016.07.121

    Article  PubMed  CAS  Google Scholar 

  55. Ivanova, N.V., Kuzmina, M.L., Braukmann, T.W., et al., Authentication of herbal supplements using nextgeneration sequencing, PLoS One, 2016, vol. 11, no. 5. e0156426. doi 10.1371/journal.pone.0156426

    Article  PubMed  CAS  PubMed Central  Google Scholar 

  56. Raclariu, A.C., Paltinean, R., Vlase, L., et al., Comparative authentication of Hypericum perforatum herbal products using DNA metabarcoding, TLC and HPLCMS, Sci. Rep., 2017, vol. 7, no. 1, p. 1291. doi 10.1038/s41598-017-01389-w

    Article  PubMed  CAS  PubMed Central  Google Scholar 

  57. Littlefair, J.E. and Clare, E.L., Barcoding the food chain: from Sanger to high-throughput sequencing, Genome, 2016, vol. 59, no. 11, pp. 946–958. doi 10.1139/gen-2016-0028

    Article  PubMed  CAS  Google Scholar 

  58. Arulandhu, A.J., Staats, M., Hagelaar, R., et al., Development and validation of a multi-locus DNA metabarcoding method to identify endangered species in complex samples, Gigascience, 2017, vol. 6, no. 10, pp. 1–18. doi 10.1093/gigascience/gix080

    Article  PubMed  PubMed Central  Google Scholar 

  59. Taberlet, P., Coissac, E., Pompanon, F., et al., Towards next-generation biodiversity assessment using DNA metabarcoding, Mol. Ecol., 2012, vol. 21, no. 8, pp. 2045–2050. doi 10.1111/j.1365-294X.2012.05470.x

    Article  PubMed  CAS  Google Scholar 

  60. Taberlet, P., Prud’Homme, S.M., Campione, E., et al., Soil sampling and isolation of extracellular DNA from large amount of starting material suitable for metabarcoding studies, Mol. Ecol., 2012, vol. 21, vol. 8, pp. 1816–1820. doi 10.1111/j.1365-294X.2011.05317.x

    Article  PubMed  CAS  Google Scholar 

  61. Hebert, P.D., Ratnasingham, S., and de Waard, J.R., Barcoding animal life: cytochrome c oxidase subunit 1 divergences among closely related species, Proc. Biol. Sci., 2003, vol. 270, suppl. 1, pp. S96–S99.

    Article  PubMed  CAS  PubMed Central  Google Scholar 

  62. Staats, M., Arulandhu, A.J., Gravendeel, B., et al., Advances in DNA metabarcoding for food and wildlife forensic species identification, Anal. Bioanal. Chem., 2016, vol. 408, no. 17, pp. 4615–4630. doi 10.1007/s00216-016-9595-8

    Article  PubMed  CAS  PubMed Central  Google Scholar 

  63. Shneer, V.S., DNA-barcoding of animal and plant species–a way to their molecular identification and study of biodiversity, Zh. Obshch. Biol., 2009, vol. 70, no. 4, pp. 296–315.

    PubMed  CAS  Google Scholar 

  64. Neretina, T.V. and Myuge, N.S., DNA-barcoding of living organisms, Priroda, 2013, no. 2, pp. 73–75.

    Google Scholar 

  65. Ward, R.D., Hanner, R., and Hebert, P.D., The campaign to DNA barcode all fishes, FISH-BOL, J. Fish Biol., 2009, vol. 74, no. 2, pp. 329–356. doi 10.1111/j.1095-8649.2008.02080.x

    Article  PubMed  CAS  Google Scholar 

  66. Zemlak, T.S., Ward, R.D., Connell, A.D., et al., DNA barcoding reveals overlooked marine fishes, Mol. Ecol. Res., 2009, vol. 9, no. s1, pp. 237–242. doi 10.1111/j.1755-0998.2009.02649.x

    Article  CAS  Google Scholar 

  67. Pecnikar, Z.F. and Buzan, E.V., 20 years since the introduction of DNA barcoding: from theory to application, J. Appl. Genet., 2014, vol. 55, no. 1, pp. 43–52. doi 10.1007/s13353-013-0180-y

    Article  CAS  Google Scholar 

  68. Deagle, B.E., Jarman, S.N., Coissac, E., et al., DNA metabarcoding and the cytochrome c oxidase subunit I marker: not a perfect match, Biol. Lett., 2014, vol. 10, no. 9, p. 20140562. doi 10.1098/rsbl.2014.0562

    Article  PubMed  CAS  PubMed Central  Google Scholar 

  69. Kappel, K., Haase, I., Kappel, C., et al., Species identification in mixed tuna samples with next-generation sequencing targeting two short cytochrome b gene fragments, Food Chem., 2017, vol. 234, pp. 212–219. doi 10.1016/j.foodchem.2017.04.178

    Article  PubMed  CAS  Google Scholar 

  70. Fazekas, A.J., Burgess, K.S., Kesanakurti, P.R., et al., Multiple multilocus DNA barcodes from the plastid genome discriminate plant species equally well, PLoS One, 2008, vol. 3, no. 7. e2802. doi 10.1371/journal. pone.0002802

    Article  PubMed  CAS  PubMed Central  Google Scholar 

  71. Kress, W.J. and Erickson, D.L., A two-locus global DNA barcode for land plants: the coding rbcL gene complements the non-coding trnH-psbA spacer region, PLoS One, 2007, vol. 2, no. 6. e508. doi 10.1371/journal.pone.0000508

    Article  PubMed  CAS  PubMed Central  Google Scholar 

  72. Saarela, J.M., Sokoloff, P.C., Gillespie, L.J., DNA barcoding the Canadian Arctic flora: core plastid barcodes (rbcL + matK) for 490 vascular plant species, PLoS One, 2013, vol. 8, no. 10. e77982. doi 10.1371/journal.pone.0077982

    Article  PubMed  CAS  PubMed Central  Google Scholar 

  73. Hollingsworth, P.M., Graham, S.W., and Little, D.P., Choosing and using a plant DNA barcode, PLoS One, 2011, vol. 6, no. 5. e19254. doi 10.1371/journal. pone.0019254

    Article  PubMed  CAS  PubMed Central  Google Scholar 

  74. Kress, W.J., Wurdack, K.J., Zimmer, E.A., et al., Use of DNA barcodes to identify flowering plants, Proc. Natl. Acad. Sci. U.S.A., 2005, vol. 102, no. 23, pp. 8369–8374. doi 10.1073/pnas.0503123102

    Article  PubMed  CAS  PubMed Central  Google Scholar 

  75. Logacheva, M.D., Samigullin, T.H., Dhingra, A., et al., Comparative chloroplast genomics and phylogenetics of Fagopyrum esculentum ssp. ancestrale–a wild ancestor of cultivated buckwheat, BMC Plant Biol., 2008, vol. 8, no. 1, p. 59. doi 10.1186/1471-2229-8-59

    Article  PubMed  CAS  PubMed Central  Google Scholar 

  76. Schelkunov, M.I., Shtratnikova, V.Y., Nuraliev, M.S., et al., Exploring the limits for reduction of plastid genomes: a case study of the mycoheterotrophic orchids Epipogium aphyllum and Epipogium roseum, Genome Biol. Evol., 2015, vol. 7, no. 4, pp. 1179–1191. doi 10.1093/gbe/evv019

    Article  PubMed  CAS  PubMed Central  Google Scholar 

  77. Clement, W.L. and Donoghue, M.J., Barcoding success as a function of phylogenetic relatedness in Viburnum, a clade of woody angiosperms, BMC Evol. Biol., 2012, vol. 12, no. 1, p. 73. doi 10.1186/1471-2148-12-73

    Article  PubMed  PubMed Central  Google Scholar 

  78. Hollingswortha, P.M., Forresta, L.L., Spougeb, J.L., et al., A DNA barcode for land plants, Proc. Natl. Acad. Sci. U.S.A., 2009, vol. 106, no. 31, pp. 12794–12797. doi 10.1073/pnas.0905845106

    Article  Google Scholar 

  79. Mishra, P., Kumar, A., Nagireddy, A., et al., DNA barcoding: an efficient tool to overcome authentication challenges in the herbal market, Plant Biotechnol. J., 2016, vol. 14, no. 1, pp. 8–21. doi 10.1111/pbi.12419

    Article  PubMed  CAS  Google Scholar 

  80. Parveen, I., Gafner, S., Techen, N., et al., DNA barcoding for the identification of botanicals in herbal medicine and dietary supplements: strengths and limitations, Planta Med., 2016, vol. 82, no. 14, pp. 1225–1235. doi 10.1055/s-0042-111208

    Article  PubMed  CAS  Google Scholar 

  81. Schoch, C.L., Seifert, K.A., Huhndorf, S., Robert, V., Spouge, J.L., Levesque, C.A., and Chen, W., and Fungal Barcoding Consortium, Nuclear ribosomal internal transcribed spacer (ITS) region as a universal DNA barcode marker for Fungi, Proc. Natl. Acad. Sci. U.S.A., 2012, vol. 109, no. 16, pp. 6241–6246. doi 10.1073/pnas.1117018109

    Article  PubMed  PubMed Central  Google Scholar 

  82. Kiss, L., Limits of nuclear ribosomal DNA internal transcribed spacer (ITS) sequences as species barcodes for Fungi, Proc. Natl. Acad. Sci. U.S.A., 2012, vol. 109, no. 27. E1811. doi 10.1073/pnas.1207143109

    Article  PubMed  PubMed Central  Google Scholar 

  83. Simon, U.K., Trajanoski, S., Kroneis, T., et al., Accession-specific haplotypes of the internal transcribed spacer region in Arabidopsis thaliana–a means for barcoding populations, Mol. Biol. Evol., 2012, vol. 29, no. 9, pp. 2231–2239. doi 10.1093/molbev/mss093

    Article  PubMed  CAS  Google Scholar 

  84. Weitemier, K., Straub, S.C., Fishbein, M., et al., Intragenomic polymorphisms among high-copy loci: a genus-wide study of nuclear ribosomal DNA in Asclepias (Apocynaceae), Peer. J., 2015, vol. 3. e718. doi 10.7717/peerj.718

    Article  PubMed  CAS  Google Scholar 

  85. Nithaniyal, S., Vassou, S.L., Poovitha, S., et al., Identification of species adulteration in traded medicinal plant raw drugs using DNA barcoding, Genome, 2016, vol. 60, no. 2, pp. 139–146. doi 10.1139/gen-2015-0225

    Article  PubMed  CAS  Google Scholar 

  86. Paracchini, V., Petrillo, M., Lievens, A., et al., Novel nuclear barcode regions for the identification of flatfish species, Food Control, 2017, vol. 79, pp. 297–308. doi 10.1016/j.foodcont.2017.04.009

    Article  PubMed  CAS  PubMed Central  Google Scholar 

  87. Salter, S.J., Cox, M.J., Turek, E.M., et al., Reagent and laboratory contamination can critically impact sequence-based microbiome analyses, BMC Biol., 2014, vol. 12, no. 1, p. 87. doi 10.1186/s12915-014-0087-z

    Article  PubMed  CAS  PubMed Central  Google Scholar 

  88. Longo, M.S., O’Neill, M.J., and O’Neill, R.J., Abundant human DNA contamination identified in nonprimate genome databases, PLoS One, 2011, vol. 6, no. 2. e16410. doi 10.1371/journal.pone.0016410

    Article  PubMed  CAS  PubMed Central  Google Scholar 

  89. Panchin, A.Y., Spirin, S.A., Lukyanov, S.A., et al., Human trash ESTs–sequences from cDNA collection that are not aligned to genome assembly, J. Bioinf. Comput. Biol., 2008, vol. 6, no. 4, pp. 759–773. doi 10.1142/S0219720008003709

    Article  CAS  Google Scholar 

  90. Gryson, N., Messens, K., and Dewettinck, K., PCR detection of soy ingredients in bread, Eur. Food Res. Technol., 2008, vol. 227, no. 2, pp. 345–351. doi 10.1007/s00217-007-0727-4

    Article  CAS  Google Scholar 

  91. Ballari, R.V. and Martin, A., Assessment of DNA degradation induced by thermal and UV radiation processing: implications for quantification of genetically modified organisms, Food Chem., 2013, vol. 141, no. 3, pp. 2130–2136. doi 10.1016/j.foodchem.2013.05.032

    Article  PubMed  CAS  Google Scholar 

  92. Gryson, N., Effect of food processing on plant DNA degradation and PCR-based GMO analysis: a review, Anal. Bioanal. Chem., 2010, vol. 396, no. 6, pp. 2003–2022. doi 10.1007/s00216-009-3343-2

    Article  PubMed  CAS  Google Scholar 

  93. Hajibabaei, M., Singer, G.A.C., and Hickey, D.A., Benchmarking DNA barcodes: an assessment using available primate sequences, Genome, 2006, vol. 49, no. 7, pp. 851–854. doi 10.1139/g06-025

    Article  PubMed  CAS  Google Scholar 

  94. Meusnier, I., Singer, G.A., Landry, J.F., et al., A universal DNA mini-barcode for biodiversity analysis, BMC Genomics, 2008, vol. 9, no. 1, p. 214. doi 10.1186/1471-2164-9-214

    Article  PubMed  CAS  PubMed Central  Google Scholar 

  95. Shokralla, S., Hellberg, R.S., Handy, S.M., et al., A DNA mini-barcoding system for authentication of processed fish products, Sci. Rep., 2015, vol. 5, p. 15894. doi 10.1038/srep15894

    Article  PubMed  CAS  PubMed Central  Google Scholar 

  96. Cole, J.R., Wang, Q., Fish, J.A., et al., Ribosomal Database Project: data and tools for high throughput rRNA analysis, Nucleic Acids Res., 2014, vol. 42, no. D1, pp. D633–D642. doi 10.1093/nar/gkt1244

    Article  PubMed  CAS  Google Scholar 

  97. Balint, M., Schmidt, P.A., Sharma, R., et al., An Illumina metabarcoding pipeline for fungi, Ecol. Evol., 2014, vol. 4, no. 13, pp. 2642–2653. doi 10.1002/ece3.1107

    Article  PubMed  PubMed Central  Google Scholar 

  98. Nilsson, R.H., Ryberg, M., Kristiansson, E., et al., Taxonomic reliability of DNA sequences in public sequence databases: a fungal perspective, PLoS One, 2006, vol. 1, no. 1. e59. doi 10.1371/journal. pone.0000059

    Article  PubMed  CAS  PubMed Central  Google Scholar 

  99. Thomas, A.C., Deagle, B.E., Eveson, J.P., et al., Quantitative DNA metabarcoding: improved estimates of species proportional biomass using correction factors derived from control material, Mol. Ecol. Res., 2016, vol. 16, no. 3, pp. 714–726. doi 10.1111/1755-0998.12490

    Article  CAS  Google Scholar 

Download references

Author information

Authors and Affiliations

  1. Central Research Institute of Epidemiology of Rospotrebnadzor, Moscow, 111123, Russia

    A. S. Speranskaya, G. A. Shipulin & K. F. Khafizov

  2. Departament of Higher Plants, The A. N. Belozersky Institute Of Physico-Chemical Biology, Moscow State University, Moscow, 119991, Russia

    A. S. Speranskaya, A. A. Krinitsina & M. D. Logacheva

  3. Skolkovo Institute of Science and Technology, Moscow, 143026, Russia

    A. S. Speranskaya, A. A. Krinitsina, K. F. Khafizov & M. D. Logacheva

Authors
  1. A. S. Speranskaya
    View author publications

    You can also search for this author in PubMed Google Scholar

  2. A. A. Krinitsina
    View author publications

    You can also search for this author in PubMed Google Scholar

  3. G. A. Shipulin
    View author publications

    You can also search for this author in PubMed Google Scholar

  4. K. F. Khafizov
    View author publications

    You can also search for this author in PubMed Google Scholar

  5. M. D. Logacheva
    View author publications

    You can also search for this author in PubMed Google Scholar

Corresponding author

Correspondence to A. S. Speranskaya.

Additional information

Original Russian Text © A.S. Speranskaya, A.A. Krinitsina, G.A. Shipulin, K.F. Khafizov, M.D. Logacheva, 2018, published in Genetika, 2018, Vol. 54, No. 9, pp. 988–998.

Rights and permissions

Reprints and permissions

About this article

Check for updates. Verify currency and authenticity via CrossMark

Cite this article

Speranskaya, A.S., Krinitsina, A.A., Shipulin, G.A. et al. High-Throughput Sequencing for the Authentication of Food Products: Problems and Perspectives. Russ J Genet 54, 1003–1012 (2018). https://doi.org/10.1134/S1022795418090132

Download citation

  • Received:

  • Accepted:

  • Published:

  • Issue Date:

  • DOI: https://doi.org/10.1134/S1022795418090132

Keywords

Search

Navigation