Skip to main content
SpringerLink
Log in

Spoilage detection of smart packaged chicken meat by ddPCR

  • Original Paper
  • Published:
European Food Research and Technology Aims and scope Submit manuscript

Abstract

Nowadays, it is important for the food industry and public health that food reaches the consumer from production to consumption without spoiling. Smart packaging technologies are one of the new technologies informing the manufacturer and customer. In order to prevent spoiled food from being consumed, it is necessary to identify the deterioration as quickly as possible. The aim of the study is to determine the applicability of smart packaging technology and usability of Digital Droplet PCR for quick and accurate spoilage detection by evaluating the quantity of genes involved in biogenic amines synthesis that occurs during spoilage. The accumulation of biogenic amines, which are spoilage products, in foods until they are detected, and the consumption of these foods pose a risk to public health. In this study, chicken meats were analyzed on specific days in terms of microbiological, physicochemical, and molecular aspects. The 9th day was determined to be the start of the degradation when the quantity of microorganisms exceeded 108 cfu/g, based on the microbiological data obtained from chicken meats. On the same day according to the ddPCR data, the gene duplication number was found to be over 50–60 copies. Within the light of this information, the upper limit for the detection of degradation of histamine and putrescine-producing is interpreted as 50 copies. When the results of the microbiological analyses and ddPCR data were compared, it was shown that ddPCR method when used in combination with the smart labels can be applicable for quick deterioration detection in smart packaging systems.

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.

Institutional subscriptions

Fig. 1
Fig. 2
Fig. 3
Fig. 4

Similar content being viewed by others

Data availability

The datasets generated during and/or analyzed during the current study are available from the corresponding author on reasonable request.

References

  1. Higgs JD (2000) The changing nature of red meat: 20 years improving nutritional quality. Trends Food Sci Technol 11:85–95. https://doi.org/10.1016/S0924-2244(00)00055-8

    Article  CAS  Google Scholar 

  2. Odeyemi OA, Alegbeleye OO, Strateva M, Stratev D (2020) Understanding spoilage microbial community and spoilage mechanisms in foods of animal origin. Compr Rev Food Sci Food Saf 19:311–331. https://doi.org/10.1111/1541-4337.12526

    Article  PubMed  Google Scholar 

  3. Tsafrakidou P, Sameli N, Bosnea L, Chorianopoulos N, Samelis J (2021) Assessment of the spoilage microbiota in minced free-range chicken meat during storage at 4 °C in retail modified atmosphere packages. Food Microbiol 99:1–10. https://doi.org/10.1016/j.fm.2021.103822

    Article  CAS  Google Scholar 

  4. Nychas GJE, Skandamis PN, Tassou CC, Koutsoumanis KP (2008) Meat spoilage during distribution. Meat Sci 78:77–89. https://doi.org/10.1016/j.meatsci.2007.06.020

    Article  PubMed  Google Scholar 

  5. Chaidoutis E, Migdanis A, Keramydas D, Papalexis P (2019) Biogenic amines in food as a public health concern an outline of histamine food poisoning. Arch Iatr Hetaireon 36:419–425

    Google Scholar 

  6. Linares DM, Martín MC, Ladero V, Alvarez MA, Fernández M (2011) Biogenic amines in dairy products. Crit Rev Food Sci Nutr 51:691–703. https://doi.org/10.1080/10408398.2011.582813

    Article  CAS  PubMed  Google Scholar 

  7. Önal A (2007) A review: current analytical methods for the determination of biogenic amines in foods. Food Chem 103:1475–1486. https://doi.org/10.1016/j.foodchem.2006.08.028

    Article  CAS  Google Scholar 

  8. EFSA Panel on Biological Hazards (BIOHAZ) (2011) Scientific opinion on risk based control of biogenic amine formation in fermented foods. EFSA J 9:1–93. https://doi.org/10.2903/j.efsa.2011.2393

    Article  CAS  Google Scholar 

  9. Martínez N, Martín MC, Herrero A, Fernández M, Alvarez MA, Ladero V (2011) qPCR as a powerful tool for microbial food spoilage quantification: significance for food quality. Trends Food Sci Technol 22:367–376. https://doi.org/10.1016/j.tifs.2011.04.004

    Article  CAS  Google Scholar 

  10. Marcobal A, De Las RB, Muñoz R (2006) Methods for the detection of bacteria producing biogenic amines on foods: a survey. J Verbrauch Lebensm 1:187–196. https://doi.org/10.1007/s00003-006-0035-0

    Article  CAS  Google Scholar 

  11. Barbieri F, Montanari C, Gardini F, Tabanelli G (2019) Biogenic amine production by lactic acid bacteria: a review. Foods 8:1–27. https://doi.org/10.3390/foods8010017

    Article  CAS  Google Scholar 

  12. Araújo C, Munoz-Atienza E, Ramírez M, Poeta P, Igrejas G, Hernández PE et al (2015) Safety assessment, genetic relatedness and bacteriocin activity of potential probiotic Lactococcus lactis strains from rainbow trout (Oncorhynchus mykiss, Walbaum) and rearing environment. Eur Food Res Technol 241:647–662. https://doi.org/10.1007/s00217-015-2493-z

    Article  CAS  Google Scholar 

  13. Pinheiro LB, Coleman VA, Hindson CM, Herrmann JO, Hindson BJ, Bhat S (2012) Evaluation of a droplet digital polymerase chain reaction format for DNA copy number quantification. Anal Chem 84:1003–1011. https://doi.org/10.1021/ac202578x

    Article  CAS  PubMed  Google Scholar 

  14. Falzone L, Musso N, Gattuso G, Bongiorno D, Palermo CI, Scalia G et al (2020) Sensitivity assessment of droplet digital PCR for SARS-CoV-2 detection. Int J Mol Med 46:957–964. https://doi.org/10.3892/ijmm.2020.4673

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  15. Köppel R, Bucher T, Bär D, van Velsen F, Ganeshan A (2018) Validation of 13 duplex droplet digital PCR systems for quantitative GMO analysis of most prevalent GMO traits. Eur Food Res Technol 244:313–321. https://doi.org/10.1007/s00217-017-2957-4

    Article  CAS  Google Scholar 

  16. Mayer W, Schuller M, Viehauser MC, Hochegger R (2019) Quantification of the allergen soy (Glycine max) in food using digital droplet PCR (ddPCR). Eur Food Res Technol 245:499–509. https://doi.org/10.1007/s00217-018-3182-5

    Article  CAS  Google Scholar 

  17. Öz YY, Sönmez Öİ, Karaman S, Öz E, Unal CB, Karataş AY (2020) Rapid and sensitive detection of Salmonella spp. in raw minced meat samples using droplet digital PCR. Eur Food Res Technol 246:1895–1907. https://doi.org/10.1007/s00217-020-03531-x

    Article  CAS  Google Scholar 

  18. Suo T, Liu X, Feng J, Guo M, Hu W, Guo D et al (2020) ddPCR: a more accurate tool for SARS-CoV-2 detection in low viral load specimens. Emerg Microbes Infect 9:1259–1268. https://doi.org/10.1080/22221751.2020.1772678

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  19. Morisset D, Štebih D, Milavec M, Gruden K, Žel J (2013) Quantitative analysis of food and feed samples with Droplet Digital PCR. PLoS ONE 8:1–9. https://doi.org/10.1371/journal.pone.0062583

    Article  CAS  Google Scholar 

  20. Rothrock MJ Jr, Hiett KL, Kiepper BH, Ingram K, Hinton A (2013) Quantification of zoonotic bacterial pathogens within commercial poultry processing water samples using Droplet Digital PCR. Adv Microbiol 3:403–411. https://doi.org/10.4236/aim.2013.35055

    Article  CAS  Google Scholar 

  21. Hindson BJ, Ness KD, Masquelier DA, Belgrader P, Heredia NJ, Makarewicz AJ (2011) High-throughput Droplet Digital PCR system for absolute quantitation of DNA copy number. Anal Chem 83:8604–8610. https://doi.org/10.1021/ac202028g

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  22. Kalpana S, Priyadarshini SR, Leena MM, Moses JA, Anandharamakrishnan C (2019) Intelligent packaging: trends and applications in food systems. Trends Food Sci Technol 93:145–157. https://doi.org/10.1016/j.tifs.2019.09.008

    Article  CAS  Google Scholar 

  23. Müller P, Schmid M (2019) Intelligent packaging in the food sector: a brief overview. Foods 8:1–12. https://doi.org/10.3390/foods8010016

    Article  CAS  Google Scholar 

  24. Yam KL, Takhıstov PT, Mıltz J (2005) Intelligent packaging: concepts and applications. J Food Sci 70:1–10. https://doi.org/10.1111/j.1365-2621.2005.tb09052.x

    Article  Google Scholar 

  25. De Kruijf N, van Beest M, Rijk R, Sipiläinen-Malm Losada PP, De Meulenaer B (2002) Active and intelligent packaging: applications and regulatory aspects. Food Addit Contam 19:144–162. https://doi.org/10.1080/02652030110072722

    Article  CAS  Google Scholar 

  26. Gabis DA, Vesley D, Favero MS (1976) In: Speck ML (ed) Compendium of methods for the microbiological examination of foods. American Public Health Association (APHA), Washington

  27. Biomerieux. USA (2016). http://www.biomerieux-usa.com. Accessed Jan 2016

  28. Gilliland SE, Michener HD, Kraft AA (1976) In: Speck ML (ed) Compendium of methods for the microbiological examination of foods. American Public Health Association (APHA), Washington

  29. Sandine WE, Hill WM, Thompson H (1976) In: Speck ML (ed) Compendium of methods for the microbiological examination of foods. American Public Health Association (APHA), Washington

  30. Nannelli F, Claisse O, Gindreau E, de Revel G, Lonvaud-Funel A, Lucas PM (2008) Determination of lactic acid bacteria producing biogenic amines in wine by quantitative PCR methods. Lett Appl Microbiol 47:594–599. https://doi.org/10.1111/j.1472-765X.2008.02472.x

    Article  CAS  PubMed  Google Scholar 

  31. Ferrario C, Borgo F, de las Rivas B, Munoz R, Ricci G, Fortina MG (2014) Sequencing, characterization, and gene expression analysis of the histidine decarboxylase gene cluster of Morganella morganii. Curr Microbiol 68:404–411. https://doi.org/10.1007/s00284-013-0490-7

    Article  CAS  PubMed  Google Scholar 

  32. Huggett JF, Foy CA, Benes V, Emslie K, Garson JA, Haynes R, Hellemans J, Kubista M, Mueller RD, Nolan T, Pfaffl MW, Shipley GL, Vandesompele J, Wittwer CT, Bustin SA (2013) The digital MIQE guidelines: minimum information for publication of quantitative digital PCR experiments. Clin Chem 59:892–902. https://doi.org/10.1373/clinchem.2013.206375

    Article  CAS  PubMed  Google Scholar 

  33. Chantawannakul P, Oncharoen A, Klanbut K, Chukeatirote E, Lumyong S (2002) Characterization of proteases of Bacillus subtilis strain 38 isolated from traditionally fermented soybean in Northern Thailand. Sci Asia 28:241–245

    Article  CAS  Google Scholar 

  34. Koistinen KM, Plumed-Ferrer C, Lehesranta SJ, Karenlampi SO, von Wright A (2007) Comparison of growth-phase-dependent cytosolic proteomes of two Lactobacillus plantarum strains used in food and feed fermentations. FEMS Microbiol Lett 273:12–21. https://doi.org/10.1111/j.1574-6968.2007.00775.x

    Article  CAS  PubMed  Google Scholar 

  35. Pellissery AJ, Vinayamohan PG, Amalaradjou MAR, Venkitanarayanan K (2020) In: Biswas AK, Mandal PK (eds) Meat quality analysis. Academic Press, Washington

  36. Fang Z, Zhao Y, Warner RD, Johnson SK (2017) Active and intelligent packaging in meat industry. Trends Food Sci Technol 61:60–71. https://doi.org/10.1016/j.tifs.2017.01.002

    Article  CAS  Google Scholar 

  37. Riahi M, Sadrabad EK, Ali Jebali SH, Hekmatimoghaddam FAM (2019) Development of a pH-sensitive indicator label for the real-time monitoring of chicken meat freshness. J Nutr Fasting Health. 7:213–220. https://doi.org/10.22038/jnfh.2019.42963.1222

    Article  Google Scholar 

  38. Lee K, Baek S, Kim D, Seo J (2019) A freshness indicator for monitoring chicken-breast spoilage using a Tyvek® sheet and RGB color analysis. Food Packag Shelf Life 19:40–46. https://doi.org/10.1016/j.fpsl.2018.11.016

    Article  Google Scholar 

  39. Wu D, Zhang M, Chen H, Bhandari B (2021) Freshness monitoring technology of fish products in intelligent packaging. Crit Rev Food Sci Nutr 61:1279–1292. https://doi.org/10.1080/10408398.2020.1757615

    Article  CAS  PubMed  Google Scholar 

  40. Smolander M, Hurme E, Latva-Kala K, Luoma T, Alakomi HL, Ahvenainen R (2002) Myoglobin-based indicators for the evaluation of freshness of unmarinated broiler cuts. Innov Food Sci Emerg Technol 3:279–288. https://doi.org/10.1016/S1466-8564(02)00043-7

    Article  CAS  Google Scholar 

  41. Opara UL, Fadiji T, Caleb OJ, Oluwole AO (2022) Changes in volatile composition of Cape hake fillets under modified atmosphere packaging systems during cold storage. Foods 11:1–12. https://doi.org/10.3390/foods11091292

    Article  CAS  Google Scholar 

  42. Mataragas M, Bikouli VC, Korre M, Sterioti A, Skandamis PN (2019) Development of a microbial time temperature indicator for monitoring the shelf life of meat. Innov Food Sci Emerg Technol 52:89–99. https://doi.org/10.1016/j.ifset.2018.11.003

    Article  CAS  Google Scholar 

  43. Vaikousi H, Biliaderis CG, Koutsoumanis KP (2009) Applicability of a microbial time temperature indicator (TTI) for monitoring spoilage of modified atmosphere packed minced meat. Int J Food Microbiol 133:272–278. https://doi.org/10.1016/j.ijfoodmicro.2009.05.030

    Article  CAS  PubMed  Google Scholar 

  44. Gao T, Tian Y, Zhu Z, Sun DW (2020) Modelling, responses and applications of time-temperature indicators (TTIs) in monitoring fresh food quality. Trends Food Sci Technol 99:311–322. https://doi.org/10.1016/j.tifs.2020.02.019

    Article  CAS  Google Scholar 

  45. Wojnowski W, Kalinowska K, Majchrzak T, Płotka-Wasylka J, Namieśnik J (2019) Prediction of the biogenic amines index of poultry meat using an electronic nose. Sensors 19:1–10. https://doi.org/10.3390/s19071580

    Article  CAS  Google Scholar 

  46. El-Nour KMA, Salam ETA, Soliman HM, Orabi A (2017) Gold nanoparticles as a direct and rapid sensor for sensitive analytical detection of biogenic amines. Nanoscale Res Lett 12:1–11. https://doi.org/10.1186/s11671-017-2014-z

    Article  CAS  Google Scholar 

  47. Rokka M, Eerola S, Smolander M, Alakomi HL, Ahvenainen R (2004) Monitoring of the quality of modified atmosphere packaged broiler chicken cuts stored in different temperature conditions: B. Biogenic amines as quality-indicating metabolites. Food Control 15:601–607. https://doi.org/10.1016/j.foodcont.2003.10.002

    Article  CAS  Google Scholar 

  48. Silva CM, Glória MBA (2002) Bioactive amines in chicken breast and thigh after slaughter and during storage at 4 ± 1 °C and in chicken-based meat products. Food Chem 78:241–248. https://doi.org/10.1016/S0308-8146(01)00404-6

    Article  CAS  Google Scholar 

  49. Gallas L, Standarová E, Steinhauserová I, Steinhauser L, Vorlová L (2010) Formation of biogenic amines in chicken meat stored under modified atmosphere. Acta Vet Brno 79:107–116. https://doi.org/10.2754/avb201079S9S107

    Article  CAS  Google Scholar 

  50. Balamatsia CC, Paleologos EK, Kontominas MG, Savvaidis IN (2006) Correlation between microbial flora, sensory changes and biogenic amines formation in fresh chicken meat stored aerobically or under modified atmosphere packaging at 4 °C: possible role of biogenic amines as spoilage indicators. Antonie Van Leeuwenhoek 89:9–17. https://doi.org/10.1007/s10482-005-9003-4

    Article  CAS  PubMed  Google Scholar 

  51. Baston O, Barna O (2010) Biogenic amines variation from refrigerated white and red chicken meat. Lucrari Stiintifice Agron 53:147–150

    Google Scholar 

  52. Baston O, Tofan I, Stroia AL, Moise D, Barna O (2008) Refrigerated chicken meat freshness. Correlation between easily hydrolisable nitrogen, pH value and biogenic amines contents. Ann Univ Dunarea Jos Galati Fascicle VI: Food Technol 32:37–43

    Google Scholar 

  53. Moniente M, García-Gonzalo D, Ontañón I, Pagán R, Botello-Morte L (2021) Histamine accumulation in dairy products: microbial causes, techniques for the detection of histamine-producing microbiota, and potential solutions. Compr Rev Food Sci Food Saf 20:1481–1523. https://doi.org/10.1111/1541-4337.12704

    Article  CAS  PubMed  Google Scholar 

  54. Nurilmala M, Saputri NN, Abdullah A, Nurjanah N, Yusfiandayani R, Sondita MFA (2020) Detection of histamine-producing bacteria on tuna species using histidine decarboxylase (hdc) and 16S rRNA. Squalen 15:131–139. https://doi.org/10.15578/squalen.v15i3.445

    Article  Google Scholar 

  55. Galbiati S, Damin F, Ferraro L, Soriani N, Burgio V, Ronzoni M et al (2019) Microarray approach combined with ddPCR: an useful pipeline for the detection and quantification of circulating tumour DNA mutations. Cells 8:1–14. https://doi.org/10.3390/cells8080769

    Article  CAS  Google Scholar 

  56. Ren J, Deng T, Huang W, Chen Y, Ge Y (2017) A digital PCR method for identifying and quantifying adulteration of meat species in raw and processed food. PLoS ONE 12:1–17. https://doi.org/10.1371/journal.pone.0173567

    Article  CAS  Google Scholar 

  57. Naaum AM, Shehata HR, Chen S, Li J, Tabujara N, Awmack D et al (2018) Complementary molecular methods detect undeclared species in sausage products at retail markets in Canada. Food Control 84:339–344. https://doi.org/10.1016/j.foodcont.2017.07.040

    Article  CAS  Google Scholar 

  58. Cremonesi P, Cortimiglia C, Picozzi C, Minozzi G, Malvisi M, Luini M et al (2016) Development of a droplet digital polymerase chain reaction for rapid and simultaneous identification of common foodborne pathogens in soft cheese. Front Microbiol 7:1–10. https://doi.org/10.3389/fmicb.2016.01725

    Article  Google Scholar 

  59. Pan H, Dong K, Rao L, Zhao L, Wu X, Wang Y et al (2020) Quantitative detection of viable but nonculturable state Escherichia coli O157: H7 by ddPCR combined with propidium monoazide. Food Control 112:1–8. https://doi.org/10.1016/j.foodcont.2020.107140

    Article  CAS  Google Scholar 

  60. Košir AB, Demšar T, Štebih D, Žel J, Milavec M (2019) Digital PCR as an effective tool for GMO quantification in complex matrices. Food Chem 294:73–78. https://doi.org/10.1016/j.foodchem.2019.05.029

    Article  CAS  Google Scholar 

  61. Sperber WH (2009) In: Sperber WH, Doyle MP (eds) Compendium of the microbiological spoilage of foods and beverages. Springer Science + Business Media, LLC, London

  62. Hinton A Jr, Cason JA, Ingram KD (2004) Tracking spoilage bacteria in commercial poultry processing and refrigerated storage of poultry carcasses. Int J Food Microbiol 91:155–165. https://doi.org/10.1016/S0168-1605(03)00377-5

    Article  PubMed  Google Scholar 

  63. Bruckner S, Albrecht A, Petersen B, Kreyenschmidt J (2013) Characterization and comparison of spoilage processes in fresh pork and poultry. J Food Qual 35:372–382. https://doi.org/10.1111/j.1745-4557.2012.00456.x

    Article  CAS  Google Scholar 

  64. Lauritsen CV, Kjeldgaard J, Ingmer H, Bisgaard M, Christensen H (2019) Microbiota encompassing putative spoilage bacteria in retail packaged broiler meat and commercial broiler abattoir. Int J Food Microbiol 300:14–21. https://doi.org/10.1016/j.ijfoodmicro.2019.04.003

    Article  PubMed  Google Scholar 

  65. Jaafreh S, Breuch R, Günther K, Kreyenschmidt J, Kaul P (2018) Rapid poultry spoilage evaluation using portable fiber-optic raman spectrometer. Food Anal Methods 11:2320–2328. https://doi.org/10.1007/s12161-018-1223-0

    Article  Google Scholar 

  66. Rossaint S, Klausmann S, Kreyenschmidt J (2015) Effect of high-oxygen and oxygen-free modified atmosphere packaging on the spoilage process of poultry breast fillets. Poult Sci 94:96–103. https://doi.org/10.3382/ps/peu001

    Article  CAS  PubMed  Google Scholar 

  67. Herbert U, Rossaint S, Khanna MA, Kreyenschmidt J (2013) Comparison of argon-based and nitrogen-based modified atmosphere packaging on bacterial growth and product quality of chicken breast fillets. Poult Sci 92:1348–1356. https://doi.org/10.3382/ps.2012-02590

    Article  CAS  PubMed  Google Scholar 

Download references

Acknowledgements

This study was supported by the Research Fund of the İstanbul University, İstanbul, Turkey (Project No: 47011). This manuscript has been summarized from PhD dissertation of Gülay Merve BAYRAKAL.

Author information

Authors and Affiliations

  1. Department of Food Hygiene and Technology, Faculty of Veterinary Medicine, İstanbul University-Cerrahpaşa, Istanbul, Turkey

    Gülay Merve Bayrakal

  2. Inovatif Biotechnology Chemistry and Health Ltd., İstanbul University Technopark Bld., Istanbul, Turkey

    Gürhan Çiftçioğlu

Authors
  1. Gülay Merve Bayrakal
    View author publications

    You can also search for this author in PubMed Google Scholar

  2. Gürhan Çiftçioğlu
    View author publications

    You can also search for this author in PubMed Google Scholar

Contributions

All the authors contributed to the study conception and design. GÇ and GMB designed the work, prepared samples, analyzed the experimental data and discussed the research results. GMB performed the experiments, data analysis, literature search and wrote the final manuscript. GÇ supervised the work and reviewed/edited the manuscript. All the authors read and approved the final manuscript.

Corresponding author

Correspondence to Gülay Merve Bayrakal.

Ethics declarations

Conflict of interest

The authors declare no conflict of interest.

Compliance with ethics requirements

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

Additional information

Publisher's Note

Springer Nature remains neutral with regard to jurisdictional claims in published maps and institutional affiliations.

Rights and permissions

Springer Nature or its licensor (e.g. a society or other partner) holds exclusive rights to this article under a publishing agreement with the author(s) or other rightsholder(s); author self-archiving of the accepted manuscript version of this article is solely governed by the terms of such publishing agreement and applicable law.

Reprints and permissions

About this article

Check for updates. Verify currency and authenticity via CrossMark

Cite this article

Bayrakal, G.M., Çiftçioğlu, G. Spoilage detection of smart packaged chicken meat by ddPCR. Eur Food Res Technol 249, 2635–2645 (2023). https://doi.org/10.1007/s00217-023-04321-x

Download citation

  • Received:

  • Revised:

  • Accepted:

  • Published:

  • Issue Date:

  • DOI: https://doi.org/10.1007/s00217-023-04321-x

Keywords

Search

Navigation