European Food Research and Technology

, Volume 245, Issue 2, pp 499–509 | Cite as

Quantification of the allergen soy (Glycine max) in food using digital droplet PCR (ddPCR)

  • W. Mayer
  • M. Schuller
  • M. C. Viehauser
  • R. Hochegger
Original Paper


To meet the increasing need for quantification of allergens and to have an alternative to commercially available ELISA and PCR systems, the Austrian Agency for Health and Food Safety started establishing in-house PCR systems. To obtain low limits of detection (LOD) and quantification (LOQ), target sequences are preferably sought in multicopy genomes like mitochondrial- or chloroplast DNA. These molecules are of high but varying abundance even among tissues of the same organism. Beyond that, DNA might be degraded by processes of food manufacturing which additionally affects their quantification. Therefore, a reliable correlation of the allergen portion in a sample and its chloroplast-DNA concentration cannot be preassumed. This incoherence is not further considered (e.g., by a matrix-related reference material), and therefore, our quantitative results can only be understood as the mass of soy which maintained its biochemical activity, related to the soy content of the reference material used. To convert absolute results expressed in copies per microliter (Cp/µL) as obtained by digital droplet PCR (ddPCR) into a unit of mass fraction (e.g., milligram per kilogram), a conversion function is generated by the measurement of a reference material in the same run. For the specific detection and quantification of the allergenic ingredient soy (Glycine max) in food a primer/probe system has been developed which amplifies a 140 bp product of the ndhH gene of the chloroplast DNA. It is specific for soy and does not react with even closely related plant species. Digital droplet PCR (ddPCR) was selected for quantification for its particular advantages and the method has been validated in-house. It was found to be applicable to various matrices including meat products, flour, milk, and fatty creams, with recovery rates between 60 and 100%. The limit of detection and the limit of quantification (LOQ) are 0.16 mg/kg and 0.60 mg/kg, respectively. Repeated analysis of analyte-free food matrices spiked with reference material provided acceptable values for precision: The relative standard deviation (RSDoverall) of the whole method (including DNA extraction) is below 25%. The recovery of pure soy material (pulverized beans) was between 112.5 and 135.0%. The presented method is shown to be reliable and accurate, provided that samples and reference material are extracted and amplified in the same way.


Chloroplast DNA Soybean Quantification Digital droplet PCR Food allergens 


Compliance with ethical standards

Conflict of interest

The authors declare that they have no conflict of interest.

Compliance with ethics requirements

This article does not contain any studies with human or animal subjects.


  1. 1.
    Rudy W, Dreusch AB (2009) Problematik, Nachweis und neue Möglichkeiten für die analyse versteckter Lebensmittelallergene. J für Verbraucherschutz Lebensmittelsicherheit 4(2):13–18CrossRefGoogle Scholar
  2. 2.
    Török K, Hajas L, Horváth V, Schall E, Bugyi Z, Kemény S, Tömösközi S (2015) Identification of the factors affecting the analytical results of food allergen ELISA methods. Eur Food Res Technol 241(1):127–136CrossRefGoogle Scholar
  3. 3.
    EU Regulation No 1169/2011 of the European Parliament and of the Council. Off J EU 304:18–63Google Scholar
  4. 4.
    Morley AA (2014) Digital PCR: a brief history. Biomol Detect Quantif 1(1):1–2CrossRefGoogle Scholar
  5. 5.
    Iwobi A, Gerdes L, Busch U, Pecoraro S (2016) Droplet digital PCR for routine analysis of genetically modified foods (GMO)—a comparison with real-time quantitative PCR. Food Control 69:205–213CrossRefGoogle Scholar
  6. 6.
    René Köppel TB, Frei A, Waiblinger HU (2015) Droplet digital PCR versus multiplex real-time PCR method for the detection and quantification of DNA from the four transgenic soy traits MON87769, MON87708, MON87705 and FG72, and lectin. Eur Food Res Technol 241:521–527CrossRefGoogle Scholar
  7. 7.
    Köppel R, Dvorak V, Zimmerli F, Breitenmoser A, Eugster A, Waiblinger H-U (2010) Two tetraplex real-time PCR for the detection and quantification of DNA from eight allergens in food. Eur Food Res Technol 230(3):367CrossRefGoogle Scholar
  8. 8.
    Espiñeira M, Santaclara FJ (2017) Fast real-time PCR method for detection of soy in foods. PCR: methods and protocols. Methods Mol Biol 1620:173–181CrossRefGoogle Scholar
  9. 9.
    Murray SR, Butler RC, Timmerman-Vaughan GM (2009) Quantitative real-time PCR assays to detect DNA degradation in soy-based food products. J Sci Food Agric 89(7):1137–1144CrossRefGoogle Scholar
  10. 10.
    Lebensmittel—Nachweis von Lebensmittelallergenen mit molekularbiologischen Verfahren—Teil 5: Senf (Sinapis alba) sowie Soja (Glycine max)—Qualitativer Nachweis einer spezifischen DNA-Sequenz in Brühwürsten mittels Real-time PCR. CEN/TS 15634-5:2016Google Scholar
  11. 11.
    Bauer T, Kirschbaum K, Panter S, Kenk M, Bergemann J (2011) Sensitive detection of soy (Glycine max) by real-time polymerase chain reaction targeting the mitochondrial atpA gene. J AOAC Int 94(6):1863–1873CrossRefGoogle Scholar
  12. 12.
    Alberts B, Johnson A, Lewis J, Raff M, Roberts K, Walter P (2004) Molekularbiologie der Zelle, 4th edn. Wiley-VCH, Weinheim, p 922fGoogle Scholar
  13. 13.
    Encyclopædia B (2017) Accessed 13 Feb 2018
  14. 14.
    Biologie-Schule Das Nachschlagewerk für Biologie (2018) Accessed 13 Feb 2018
  15. 15.
    National Center for Biotechnology Information (2016) Accessed 30 Jul 2016
  16. 16.
    Pieulle L, Guedeney G, Cassier-Chauvat C, Jeanjean R, Chauvat F, Peltier G (2000) The gene encoding the NdhH subunit of type 1 NAD(P)H dehydrogenase is essential to survival of Synechocystis PCC6803. FEBS Lett 487(2):272–276CrossRefGoogle Scholar
  17. 17.
    Droplet, Digital™ PCR applications guide—Bio-Rad Accessed 13 Aug 2015
  18. 18.
    Bio-Rad Laboratories I ddPCR™ Supermix for Probes (No dUTP). Product Insert, Rev DGoogle Scholar
  19. 19.
    Dagata JA, Farkas N, Kramer J (2016) Method for measuring the volume of nominally 100 µm diameter spherical water-in-oil emulsion droplets. NIST Spec Publ 260(184):260–284Google Scholar
  20. 20.
    Košir AB, Divieto C, Pavšič J, Pavarelli S, Dobnik D, Dreo T, Bellotti R, Sassi MP, Žel J (2017) Droplet volume variability as a critical factor for accuracy of absolute quantification using droplet digital PCR. Anal Bioanal Chem 409(28):6689–6697CrossRefGoogle Scholar
  21. 21.
    Demeke T, Dobnik D (2018) Critical assessment of digital PCR for the detection and quantification of genetically modified organisms. Anal Bioanal Chem 410(17):4039–4050CrossRefGoogle Scholar
  22. 22.
    Broeders S, Huber I, Grohmann L, Berben G, Taverniers I, Mazzara M, Roosens N, Morisset D (2014) Guidelines for validation of qualitative real-time PCR methods. Trends Food Sci Technol 37(2):115–126CrossRefGoogle Scholar
  23. 23.
    Žel J, Mazzara M, Savini C, Cordeil S, Camloh M, Stebih D, Cankar K, Gruden K, Morisset D, Van den Eede G (2008) Method validation and quality management in the flexible scope of accreditation: an example of laboratories testing for genetically modified organisms. Food Anal Methods. Google Scholar
  24. 24.
    Horwitz W (1995) Protocol for the design, conduct and interpretation of method-performance studies. Pure Appl Chem 67:331–343CrossRefGoogle Scholar
  25. 25.
    Grohmann L, Broll H, Dagand E, Hildebrandt S, Hübert P, Kiesecker H, Lieske K, Mäde D, Joachim Mankertz D, Ralf Reiting D, Manuela Schulze D, Speck B, Uhlig S, Wahler D, Waiblinger H-U, Woll K, Zur K (2016) Guidelines for the single-laboratory validation of qualitative real-time PCR methods. Bundesamt für Verbraucherschutz und Lebensmittelsicherheit (BVL), BraunschweigGoogle Scholar
  26. 26.
    Definition of minimum performance requirements for analytical methods of GMO testing (2015) European network of GMO laboratories (ENGL). Accessed 14 Apr 2016
  27. 27.
    AWI 20395 Biotechnology-guidelines for evaluating the performance of targeted nucleic acid quantification methods: Part 1 qPCR and dPCR (2017). ISO AWI 20395 (v4.3), vol unpublishedGoogle Scholar
  28. 28.
    Hougs L, Zel J, Charles-Delobel C, Burns M, Charels D, Ciabatti I (2011) Verification of analytical methods for GMO testing when implementing interlaboratory validated methods. In: ENGL working group on “Method Verification” of the joint research centre of the European Commission Luxembourg: JRC Scientific and Technical Reports Publications Office of the European UnionGoogle Scholar
  29. 29.
    Huggett JF, Foy CA, Benes V, Emslie K, Garson JA, Haynes R, Hellemans J, Kubista M, Mueller RD, Nolan T (2013) The digital MIQE guidelines: minimum information for publication of quantitative digital PCR experiments. Clin Chem 59(6):892–902CrossRefGoogle Scholar
  30. 30.
    Lievens A, Jacchia S, Kagkli D, Savini C, Querci M (2016) Measuring digital PCR quality: performance parameters and their optimization. PLoS One 11(5):e0153317CrossRefGoogle Scholar
  31. 31.
    Deprez L, Corbisier P, Kortekaas AM, Mazoua S, Beaz Hidalgo R, Trapmann S, Emons H (2016) Validation of a digital PCR method for quantification of DNA copy number concentrations by using a certified reference material. Biomol Detect Quantif 9:29–39. CrossRefGoogle Scholar
  32. 32.
    Thompson M, Ellison SL, Wood R (2002) Harmonized guidelines for single-laboratory validation of methods of analysis (IUPAC Technical Report). Pure Appl Chem 74(5):835–855CrossRefGoogle Scholar
  33. 33.
    Poms R, Klein C, Anklam E (2004) Methods for allergen analysis in food: a review. Food Addit Contam 21(1):1–31CrossRefGoogle Scholar

Copyright information

© Springer-Verlag GmbH Germany, part of Springer Nature 2018

Authors and Affiliations

  • W. Mayer
    • 1
  • M. Schuller
    • 1
  • M. C. Viehauser
    • 1
  • R. Hochegger
    • 1
  1. 1.Austrian Agency for Health and Food Safety (AGES)ViennaAustria

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