European Food Research and Technology

, Volume 244, Issue 4, pp 767–774 | Cite as

Droplet digital PCR (ddPCR) method for the detection and quantification of goat and sheep derivatives in commercial meat products

  • Qiang Wang
  • Yicun Cai
  • Yuping He
  • Litao Yang
  • Jian Li
  • Liangwen Pan
Original Paper


A highly precise, quantitative method based on the droplet digital polymerase chain reaction (ddPCR) technique was developed to identify and quantify the goat and sheep content in meat products. A formula for calculating raw meat weight based on DNA copy number was established. Exclusive specificity was verified using samples from 24 different animal species, and inclusive specificity between goat and sheep was tested using five different breeds for each species. The limit of detection and the limit of quantitation for both goat and sheep were 1 and 5 copies/μL, respectively, using a cloned plasmid containing goat- and sheep-specific target DNA fragments as calibrators. The accuracy and applicability of the method were verified using mixed powder samples with known proportions of goat and sheep meat, simulate meatball samples, and commercially available products, respectively. The results confirmed that the developed ddPCR methods are highly precise for identifying and quantifying the goat and sheep meat, indicating their potential applicability in future routine analyses.


Droplet digital PCR Quantification Nuclear DNA Plasmid Meat product 



The authors are grateful to the reviewers for their careful corrections of the manuscript.

Compliance with ethical standards


Financial support from the Shanghai Science and Technology Commission Standard Special Fund (16DZ0501501), Shanghai Entry-Exit Food and Feed Safety Special Technology Service Platform Fund (17DZ2293700), Project funded by China Postdoctoral Science Foundation (2017M611628), Huaian Technical Fund (HAS201618) and Shanghai Entry-Exit Inspection and Quarantine Bureau of Science and Technology Plan Projects Fund (HK008-2017) are acknowledged with thanks.

Conflict of interest

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

Compliance with ethics requirements

This article does not contain any studies with human participants or animals performed by any of the authors.


  1. 1.
    European Commission (2002) Commission Directive 2002/86/EC. Off J Eur Commun L 305/19. BrusselsGoogle Scholar
  2. 2.
    O’Mahony PJ (2013) Finding horse meat in beef products—a global problem. QJM 106:595–597CrossRefGoogle Scholar
  3. 3.
    Nau JY (2013) Horse meat: first lessons of a scandal. Revue Medicale Suisse 376:532–533Google Scholar
  4. 4.
    Boehler P (2013) Poisoning may point to rat meat in Beijing lamb skewers. Accessed 15 June 14
  5. 5.
    Panwar N, Gahlot GC, Gahlot K, Ashraf M, Singh A (2015) Rapid identification of goat (Capra hircus) and sheep (Ovis aries) species in raw meat using duplex PCR assay. Indian J Anim Res 49:537–541CrossRefGoogle Scholar
  6. 6.
    Cai YC, Li X, Lv R, Yang JL, Li J, He YP, Pan LW (2014) Quantitative analysis of pork and chicken products by droplet digital PCR. Biomed Res Int 2014:1–6Google Scholar
  7. 7.
    Floren C, Wiedemann I, Brenig B, Schütz E, Beck J (2014) Species identification and quantification in meat and meat products using droplet digital PCR (ddPCR). Food Chem 173:1054–1058CrossRefGoogle Scholar
  8. 8.
    Laube I, Zagon J, Broll H (2007) Quantitative determination of commercially relevant species in foods by real-time PCR. Int J Food Sci Technol 42:336–341CrossRefGoogle Scholar
  9. 9.
    Druml B, Mayer W, Cichna-Markl M, Hochegger R (2015) Development and validation of a TaqMan real-time PCR assay for the identification and quantification of roe deer (Capreolus capreolus) in food to detect food adulteration. Food Chem 178:319–326CrossRefGoogle Scholar
  10. 10.
    Ballin NZ, Vogensen FK, Karlsson AH (2009) Species determination—can we detect and quantify meat adulteration? Meat Sci 83:165–174CrossRefGoogle Scholar
  11. 11.
    Kumar A, Kumar RR, Sharma BD, Gokulakrishnan P, Mendiratta SK, Sharma D (2015) Identification of species origin of meat and meat products on the DNA basis: a review. Crit Rev Food Sci Nutr 55:1340–1351CrossRefGoogle Scholar
  12. 12.
    Fang X, Zhang C (2016) Detection of adulterated murine components in meat products by TaqMan(c) real-time PCR. Food Chem 192:485–490CrossRefGoogle Scholar
  13. 13.
    Cai YC, Wang Q, He YP, Pan LW (2017) Interlaboratory validation of a real-time PCR detection method for bovine- and ovine-derived material. Meat Sci 134:119–123CrossRefGoogle Scholar
  14. 14.
    Motalib Hossain MA, Eaqub Ali Md, Abd Hamid SB, Asing Mustafa S, Mohd Desa MN, Zaidul ISM (2017) Targeting double genes in multiplex PCR for discriminating bovine, buffalo and porcine materials in food chain. Food Control 73:175–184CrossRefGoogle Scholar
  15. 15.
    Motalib Hossain MA, Eaqub Ali Md, Abd Hamid SB, Asing Mustafa S, Mohd Desa MN, Zaidul ISM (2016) Double gene targeting multiplex polymerase chain reaction–restriction fragment length polymorphism assay discriminates beef, buffalo, and pork substitution in frankfurter products. J Agric Food Chem 64:6343–6354CrossRefGoogle Scholar
  16. 16.
    Motalib Hossain MA, Eaqub Ali Md, Sultana S, Asing Bonny SQ, Abdul Kader Md, Rahman MA (2017) Quantitative tetraplex real-time polymerase chain reaction assay with TaqMan probes discriminates cattle, buffalo and porcine materials in food chain. J Agric Food Chem 65:3975–3985CrossRefGoogle Scholar
  17. 17.
    Sanders R, Huggett JF, Bushell CA, Cowen S, Scott DJ, Foy CA (2011) Evaluation of digital PCR for absolute DNA quantification. Anal Chem 83:6474–6484CrossRefGoogle Scholar
  18. 18.
    Hindson CM, Chevillet JR, Briggs HA, Gallichotte EN, Ruf IK, Hindson BJ, Vessella RL, Tewari M (2013) Absolute quantification by droplet digital PCR versus analog real-time PCR. Nat Methods 10:1003–1005CrossRefGoogle Scholar
  19. 19.
    Zhan C, Yan L, Wang L, Jin YL, Chen L, Shi Y, Wang Q (2015) The development and application of digital PCR. Fudan Univ Sci 42:786–789Google Scholar
  20. 20.
    Taylor SC, Carbonneau J, Shelton DN, Boivin G (2015) Optimization of droplet digital PCR from RNA and DNA extracts with direct comparison to RT-qPCR: clinical implications for quantification of oseltamivir-resistant subpopulations. J Virol Methods 224:58–66CrossRefGoogle Scholar
  21. 21.
    Hindson BJ, Ness KD, Masquelier DA, Belgrader P, Heredia NJ, Makarewicz AJ et al (2011) High-throughput droplet digital PCR system for absolute quantitation of DNA copy number. Anal Chem 83:8604–8610CrossRefGoogle Scholar
  22. 22.
    Pinheiro LB, Coleman VA, Hindsonetal CM (2012) Evaluation of a droplet digital polymerase chain reaction format for DNA copy number quantification. Anal Chem 84:1003–1011CrossRefGoogle Scholar
  23. 23.
    Whale AS, Cowen S, Foy CA, Huggett JF (2013) Methods for applying accurate digital PCR analysis on low copy DNA samples. PLoS One 8:1–10CrossRefGoogle Scholar
  24. 24.
    Morisset D, Štebih D, Milavec M, Gruden K, Žel J (2013) Quantitative analysis of food and feed samples with droplet digital PCR. PLoS One 8:1–9CrossRefGoogle Scholar
  25. 25.
    Dingle TC, Sedlak RH, Cook L, Jerome KR (2013) Tolerance of droplet-digital PCR versus real-time quantitative PCR to inhibitory substances. Clin Chem 59:1670–1672CrossRefGoogle Scholar
  26. 26.
    Sanmamed MF, Fernández-Landázuri S, Rodríguez C, Zárate R, Lozano MD, Zubiri L, Perez-Gracia JL, Martín-Algarra S, González A (2015) Quantitative cell-free circulating BRAFV600E mutation analysis by use of droplet digital PCR in the follow-up of patients with melanoma being treated with BRAF inhibitors. Clin Chem 61:297–304CrossRefGoogle Scholar
  27. 27.
    Rački N, Dreo T, Gutierrez-Aguirre I, Blejec A, Ravnikar M (2014) Reverse transcriptase droplet digital PCR shows high resilience to PCR inhibitors from plant, soil and water samples. Plant Methods 10:1–10CrossRefGoogle Scholar
  28. 28.
    Cai YC, He YP, Lv R, Chen HC, Wang Q, Pan LW (2017) Detection and quantification of beef and pork materials in meat products by duplex droplet digital PCR. PLoS One 12:1–12Google Scholar
  29. 29.
    Scollo F, Egea LA, Gentile A, Malfa SL, Dorado G, Hernandez P (2016) Absolute quantification of olive oil DNA by droplet digital-PCR (ddPCR): comparison of isolation and amplification methodologies. Food Chem 213:388–394CrossRefGoogle Scholar
  30. 30.
    Hu W, Chen RH, Zhang C, An ZY, Wang B, Ping Y (2014) Species identification and absolute quantification of biological samples by droplet digital PCR. Fa Yi Xue Za Zhi 30:342–345Google Scholar
  31. 31.
    ISO 21571 (2005) Foodstuffs—methods of analysis for the detection of genetically modified organisms and derived products—nucleic acid extraction. A.1: preparation of PCR-quality DNA using phenol/chloroform-based DNA extraction methods, pp 1–43Google Scholar
  32. 32.
    Lowe TM, Eddy SR (1997) tRNAscan-SE: a program for improved detection of transfer RNA genes in genomic sequence. Nucleic Acids Res 25:955–964CrossRefGoogle Scholar
  33. 33.
    Bignon C, Binart N, Ormandy C, Schuler LA, Kelly PA, Djiane J (1997) Long and short forms of the ovine prolactin receptor: cDNA cloning and genomic analysis reveal that the two forms arise by different alternative splicing mechanisms in ruminants and in rodents. J Mol Endocrinol 19:109–120CrossRefGoogle Scholar
  34. 34.
    ISO 16140-2 (2016) Microbiology of the food chain—method validation—part 2: protocol for the validation of alternative (proprietary) methods against a reference method, pp 1–62Google Scholar
  35. 35.
    Uhlig S, Frost K, Colson B, Simon K, Mäde D, Reiting R, Gowik P, Grohmann L (2015) Validation of qualitative PCR methods on the basis of mathematical–statistical modelling of the probability of detection. Accred Qual Assur 20:75–83CrossRefGoogle Scholar
  36. 36.
    Fan LL, Li P, Fu CL, Ding HL, Chen Y (2014) Detection of chicken-derived ingredients in foods by fluorescence-based quantitative real-time PCR. Food Sci 35:248–251Google Scholar
  37. 37.
    Zuo ZY, Sun DF (2016) Bovine derived materials and porcine derived materials detecting with real-time fluorescence PCR. Guizhou Agric Sci 44:130–132Google Scholar

Copyright information

© Springer-Verlag GmbH Germany 2017

Authors and Affiliations

  • Qiang Wang
    • 1
  • Yicun Cai
    • 1
  • Yuping He
    • 1
  • Litao Yang
    • 2
  • Jian Li
    • 1
  • Liangwen Pan
    • 1
  1. 1.Technical Center for Animal, Plant and Food Inspection and QuarantineShanghai Entry-Exit Inspection and Quarantine Bureau of ChinaShanghaiChina
  2. 2.School of Life Science and BiotechnologyShanghai Jiao Tong UniversityShanghaiChina

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