Analytical and Bioanalytical Chemistry

, Volume 411, Issue 11, pp 2461–2469 | Cite as

Performance assessment of digital PCR for the quantification of GM-maize and GM-soya events

  • Geoffrey CottenetEmail author
  • Carine Blancpain
  • Poh Fong Chuah
Research Paper


Accurate quantitative methods are needed to determine the amount of transgenic material in ingredients and comply with labelling GMO thresholds. Quantitative real-time PCR methods are usually applied for GMO quantification, but since a few years, digital PCR (dPCR) has been described as a potential alternative by quantifying DNA molecules directly without any standard curves. In this study, the performance of dPCR to quantify 9 GM-soya events and 15 GM-maize events was assessed. Following GMO validation guidelines, the trueness and precision were determined on high, medium and low levels of transgenic content. Results showed biases below ± 25% and satisfactory precision data. Limits of quantification were determined for each GM-event and were between 12 and 31 target copies. The reliability of GMO quantification by dPCR was further confirmed by analysing several proficiency test samples. Overall, dPCR showed accurate and precise GMO quantification on all the tested GM-events, from high to low transgenic amount. With its ease-of-use, dPCR was found to be an appealing alternative technology for routine GMO testing laboratories.

Graphical abstract


GMO Transgenic Quantification Digital PCR 



Genetically modified organisms


Polymerase chain reaction


Digital PCR


Certified reference material


No template control


Limit of quantification



The authors would like to thank Pia Scheu and Cyril Dubuck from Bio-Rad for their technical and scientific support during our study.

Compliance with ethical standards

Conflict of interest

The authors declare that they have no conflict of interest.


  1. 1.
    Cottenet G, Blancpain C, Sonnard V, Chuah PF. Two FAST multiplex real-time PCR reactions to assess the presence of genetically modified organisms in food. Food Chem. 2019;274:760–5.CrossRefGoogle Scholar
  2. 2.
    Cottenet G, Blancpain C, Sonnard V, Chuah PF. Development and validation of a multiplex real-time PCR method to simultaneously detect 47 targets for the identification of genetically modified organisms. Anal Bioanal Chem. 2013;405:6831–44.CrossRefGoogle Scholar
  3. 3.
    Bonfini L, Van Den Bulcke MH, Mazzara M, Ben E, Patak A. GMOMETHODS: the European Union database of reference methods for GMO analysis. J AOAC Int. 2012;95:1713–9.CrossRefGoogle Scholar
  4. 4.
    Quan PL, Sauzade M, Brouzes E. dPCR : a technology review. Sensors. 2018.
  5. 5.
    Ren J, Deng T, Huang W, Chen Y, Ge Y. A digital PCR method for identifying and quantifying adulteration of meat species in raw and processed food. PLoS One. 2017.
  6. 6.
    Corbisier P, Bhat S, Partis L, Xie VR, Emslie KR. Absolute quantification of genetically modified MON810 maize (Zea mays L.) by digital polymerase chain reaction. Anal Bioanal Chem. 2010;396:2143–50.CrossRefGoogle Scholar
  7. 7.
    Iwobi A, Gerdes L, Busch U, Pecoraro S. Droplet digital PCR for routine analysis of genetically modified organisms (GMO) – a comparison with real-time quantitative PCR. Food Control. 2016;69:205–13.CrossRefGoogle Scholar
  8. 8.
    Köppel R, Bucher T. Rapid establishment of droplet digital PCR for quantitative GMO analysis. Eur Food Res Technol. 2015;241:427–39.CrossRefGoogle Scholar
  9. 9.
    Whale AS, Huggett JF, Tzonev S. Fundamentals of multiplexing with digital PCR. Biomol Detect Quantif. 2016;10:15–23.CrossRefGoogle Scholar
  10. 10.
    Dobnik D, Spilsberg B, Kosir AB, Holst-Jensen A, Zel J. Multiplex quantification of 12 European Union authorized genetically modified maize lines with droplet digital polymerase chain reaction. Anal Chem. 2015;87:8218–26.CrossRefGoogle Scholar
  11. 11.
    Kosir AB, Spilsberg B, Holst-Jensen A, Zel J, Dobnik D. Development and inter-laboratory assessment of droplet digital PCR assays for multiplex quantification of 15 genetically modified soybean lines. Sci Rep. 2017;7:8601.CrossRefGoogle Scholar
  12. 12.
    Köppel R, Bucher T, Bär D, Van Velsen F, Ganeshan A. Validation of 13 duplex droplet digital PCR systems for quantitative GMO analysis of most prevalent GMO traits. Eur Food Res Technol. 2017;244:313–21.CrossRefGoogle Scholar
  13. 13.
    GMOMETHODS. EU Database of Reference Methods for GMO Analysis. 2018. Accessed 16 February 2018.
  14. 14.
    Demeke T, Dobnik D. Critical assessment of digital PCR for the detection and quantification of genetically modified organisms. Anal Bioanal Chem. 2018;410:4039–50.CrossRefGoogle Scholar
  15. 15.
    Jacchia S, Kagkli D-M, Lievens A, Angers-Loustau A, Savini C, Emons H, et al. Identification of single target taxon-specific reference assays for the most commonly genetically transformed crops using digital droplet PCR. Food Control. 2018;93:191–200.CrossRefGoogle Scholar
  16. 16.
    ERM, European reference materials. Application note 4: use of certified reference materials for the quantification of GMO in food and feed. 2006. Accessed on 26 January 2018.
  17. 17.
    ENGL, European network of GMO laboratories. Definition of minimum performance requirements for analytical methods of GMO testing. 2015. Accessed on 26 January 2018.
  18. 18.
    Corbisier P, Barbante A, Berben G, Broothaerts W, De Loose M, Emons H, Georgieva TZ, Lievens A, Mazzara M, Papazova N, Perri E, Sowa S, Stebih D, Terzi V, Trapmann S. Recommendation for the unit of measurement and the measuring system to report traceable and comparable results expressing GM content in accordance with EU legislation. EUR28536 EN. 2017;
  19. 19.
    Arumuganathan K, Earle ED. Nuclear content of some important plant species. Plant Mol Biol Rep. 1991;9:208–18.CrossRefGoogle Scholar
  20. 20.
    Majumdar N, Wessel T, Marks J. Digital PCR modeling for maximal sensitivity, dynamic range and measurement precision. PLOSone. 2015.
  21. 21.
    Dobnik D, Demsar T, Huber I, Gerdes L, Broeders S, Roosens N, et al. Inter-laboratory analysis of selected genetically modified plant reference materials with digital PCR. Anal Bioanal Chem. 2018;410:211–21.CrossRefGoogle Scholar
  22. 22.
    Chaouachi M, Berard A, Saïd K. Relative quantification in seed GMO analysis: state of art and bottlenecks. Transgenic Res. 2013;22:461–76.CrossRefGoogle Scholar

Copyright information

© Springer-Verlag GmbH Germany, part of Springer Nature 2019

Authors and Affiliations

  • Geoffrey Cottenet
    • 1
    Email author
  • Carine Blancpain
    • 1
  • Poh Fong Chuah
    • 2
  1. 1.Institute of Food Safety & Analytical Sciences - Nestlé ResearchLausanne 26Switzerland
  2. 2.Nestlé Quality Assurance CenterSingaporeSingapore

Personalised recommendations