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Review | Publishing Language: Chinese | Open Access

Application of New Microbial Detection Techniques in Meats: An Update

Yinghan XIONG1 , Chongyan LI2, Ruolin LI2, Rao WU2, Zhiwei ZHOU2, Honghu SUN3, Qun SUN1,2 ()
College of Light Industry Science and Engineering, Sichuan University, Chengdu 610064, China
College of Life Sciences, Sichuan University, Chengdu 610064, China
Irradiation Preservation Key Laboratory of Sichuan, Chengdu Institute of Food Inspection, Chengdu 611135, China
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Abstract

Meat is a vital source of protein for consumers worldwide. Microbial spoilage not only leads to economic losses, but also may cause foodborne illnesses. Classical detection techniques are simple to operate but also have some drawbacks such as long detection time and low sensitivity, making them inadequate to meet the demands of modern food safety. With the application of new techniques, microbial detection has seen dramatic improvements in speed, sensitivity, and specificity. This review summaries the advantages and shortcomings of classical microbial detection methods, including culture, immunology, biochemical reaction, polymerase chain reaction (PCR), and fluorescence, and introduces the principles and advantages of new technologies such as high-throughput sequencing, digital PCR (dPCR), matrix-assisted laser desorption ionization time of flight mass spectrometry (MALDI-TOF MS), infrared spectroscopy, Raman spectroscopy, and hyperspectroscopy. Future prospects for their application in food safety testing are also explored, aiming to provide technical support for improving meat safety monitoring.

Keywords

meat microbiologyfood safetyclassical detection techniquesnew microbial detection techniquesapplication
CLC number: TS251.1 Document code: A Article ID: 1001-8123(2025)02-0046-09

References

[1]
Organisation for Economic Co-operation and Development and Food and Agriculture Organization of the United Nations. OECD-FAO Agricultural Outlook 2024—2033[R]. Paris, France and Rome: OECD and FAO, 2024.
[2]
Food and Agriculture Organization of the United Nations. FAO platform on food loss and waste[R]. Rome: FAO, 2024.
[3]
World Health Organization. WHO estimates of the global burden of foodborne diseases: foodborne diseases burden epidemiology reference group 2007—2015[R]. Geneva: WHO, 2015.
[4]

LI W W, PIRES S M, LIU Z T, et al. Surveillance of foodborne disease outbreaks in China, 2003—2017[J]. Food Control, 2020, 118: 107359. DOI:10.1016/j.foodcont.2020.107359.

Crossref Google Scholar
[5]

FODDAI A C G, GRANT I R. Methods for detection of viable foodborne pathogens: current state-of-art and future prospects[J]. Applied Microbiology and Biotechnology, 2020, 104(10): 4281-4288. DOI:10.1007/s00253-020-10542-x.

Crossref Google Scholar
[6]

PORTILHO A I, GIMENES LIMA G, DE GASPARI E. Enzyme-linked immunosorbent assay: an adaptable methodology to study SARS-CoV-2 humoral and cellular immune responses[J]. Journal of Clinical Medicine, 2022, 11(6): 1503. DOI:10.3390/jcm11061503.

Crossref Google Scholar
[8]
CHAUHAN A, JINDAL T. Biochemical and molecular methods for bacterial identification[M]//CHAUHAN A, JINDAL T. Microbiological methods for environment, food and pharmaceutical analysis. Cham: Springer International Publishing, 2020: 425-468. DOI:10.1007/978-3-030-52024-3_10.
Crossref
[9]

HEO E J, SONG B R, PARK H J, et al. Rapid detection of Listeria monocytogenes by real-time PCR in processed meat and dairy products[J]. Journal of Food Protection, 2014, 77(3): 453-458. DOI:10.4315/0362-028X.JFP-13-318.

Crossref Google Scholar
[11]

LIU Z N, ZHONG Y H, HU Y, et al. Fluorescence strategy for sensitive detection of adenosine triphosphate in terms of evaluating meat freshness[J]. Food Chemistry, 2019, 270: 573-578. DOI:10.1016/j.foodchem.2018.07.041.

Crossref Google Scholar
[12]

SATAM H, JOSHI K, MANGROLIA U, et al. Next-generation sequencing technology: current trends and advancements[J]. Biology, 2023, 12(7): 997. DOI:10.3390/biology12070997.

Crossref Google Scholar
[13]

WANG Q, HAN Y Y, ZHANG T J, et al. Whole-genome sequencing of Escherichia coli from retail meat in China reveals the dissemination of clinically important antimicrobial resistance genes[J]. International Journal of Food Microbiology, 2024, 415: 110634. DOI:10.1016/j.ijfoodmicro.2024.110634.

Crossref Google Scholar
[14]

DE MATOS L G, DA SILVA ABREU A C, ALONSO V P P, et al. Comparison of bacterial diversity in wet- and dry-aged beef using traditional microbiology and next generation sequencing[J]. The Microbe, 2024, 2: 100035. DOI:10.1016/j.microb.2024.100035.

Crossref Google Scholar
[15]

PARK J, BAE D, KIM S A. Microbial trace investigation throughout the entire chicken supply chain based on metagenomic high-throughput sequencing[J]. Food Research International, 2023, 169: 112775. DOI:10.1016/j.foodres.2023.112775.

Crossref Google Scholar
[17]

SANCHA DOMINGUEZ L, COTOS SUÁREZ A, SÁNCHEZ LEDESMA M, et al. Present and future applications of digital PCR in infectious diseases diagnosis[J]. Diagnostics, 2024, 14(9): 931. DOI:10.3390/diagnostics14090931.

Crossref Google Scholar
[19]

ÖZ Y Y, SÖNMEZ Ö İ, KARAMAN S, et al. Rapid and sensitive detection of Salmonella spp. in raw minced meat samples using droplet digital PCR[J]. European Food Research and Technology, 2020, 246(10): 1895-1907. DOI:10.1007/s00217-020-03531-x.

Crossref Google Scholar
[20]

KONG J Q, FAN C C, LIAO X J, et al. Accurate detection of Escherichia coli O157:H7 and Salmonella enterica serovar typhimurium based on the combination of next-generation sequencing and droplet digital PCR[J]. LWT-Food Science and Technology, 2022, 168: 113913. DOI:10.1016/j.lwt.2022.113913.

Crossref Google Scholar
[21]

SUO Y J, YIN W H, ZHU Q Y, et al. A specific and sensitive aptamer-based digital PCR chip for Salmonella typhimurium detection[J]. Biosensors, 2022, 12(7): 458. DOI:10.3390/bios12070458.

Crossref Google Scholar
[22]

BRAZELTON DE CÁRDENAS J N, HAYDEN R T. Applications of digital PCR in clinical microbiology[J]. Advanced Techniques in Diagnostic Microbiology, 2018, 2: 685-698. DOI:10.1007/978-3-319-95111-9_29.

Crossref Google Scholar
[23]

LIN S, ZHANG X, TIAN A N, et al. Boron nitride nanoplate-based strand exchange amplification with enhanced sensitivity and rapidity for quantitative detection of Staphylococcus aureus in food samples[J]. Analytical Methods, 2024, 16(6): 892-898. DOI:10.1039/d3ay02076b.

Crossref Google Scholar
[24]

DENG J, LI Y, SHI W Q, et al. Primer design strategy for denaturation bubble-mediated strand exchange amplification[J]. Analytical Biochemistry, 2020, 593: 113593. DOI:10.1016/j.ab.2020.113593.

Crossref Google Scholar
[25]

ZHANG M L, WANG X D, HAN L Z, et al. Rapid detection of foodborne pathogen Listeria monocytogenes by strand exchange amplification[J]. Analytical Biochemistry, 2018, 545: 38-42. DOI:10.1016/j.ab.2018.01.013.

Crossref Google Scholar
[26]

LIU C Y, SHI C, LI M Z, et al. Rapid and simple detection of viable foodborne pathogen Staphylococcus aureus[J]. Frontiers in Chemistry, 2019, 7: 124. DOI:10.3389/fchem.2019.00124.

Crossref Google Scholar
[27]

ZHANG J, HAN X N, WANG Y, et al. Rapid and sensitive detection of Staphylococcus aureus via an all-in-one staggered strand exchange amplification platform[J]. Analyst, 2023, 148(9): 1970-1977. DOI:10.1039/D2AN02092K.

Crossref Google Scholar
[29]

SINGHAL N, KUMAR M, KANAUJIA P K, et al. MALDI-TOF mass spectrometry: an emerging technology for microbial identification and diagnosis[J]. Frontiers in Microbiology, 2015, 6: 791. DOI:10.3389/fmicb.2015.00791.

Crossref Google Scholar
[30]

SENG P, DRANCOURT M, GOURIET F, et al. Ongoing revolution in bacteriology: routine identification of bacteria by matrix-assisted laser desorption ionization time-of-flight mass spectrometry[J]. Clinical Infectious Diseases, 2009, 49(4): 543-551. DOI:10.1086/600885.

Crossref Google Scholar
[31]

SAPUGAHAWATTE D N, LI C, ZHU C, et al. Prevalence and characteristics of extended-spectrum-β-lactamase-producing and carbapenemase-producing enterobacteriaceae from freshwater fish and pork in wet markets of Hong Kong[J]. mSphere, 2020, 5(2): 00107-20. DOI:10.1128/msphere.00107-20.

Crossref Google Scholar
[32]

WANG C, EL-TELBANY M, LIN Y Z, et al. Identification of Enterococcus spp. from food sources by matrix-assisted laser desorption ionization-time of flight mass spectrometry and characterization of virulence factors, antibiotic resistance, and biofilm formation[J]. International Journal of Food Microbiology, 2024, 420: 110768. DOI:10.1016/j.ijfoodmicro.2024.110768.

Crossref Google Scholar
[33]

ASHFAQ M Y, DA’NA D A, AL-GHOUTI M A. Application of MALDI-TOF MS for identification of environmental bacteria: a review[J]. Journal of Environmental Management, 2022, 305: 114359. DOI:10.1016/j.jenvman.2021.114359.

Crossref Google Scholar
[34]

ZHANG J H, LI F W, SHEN S X, et al. More simple, efficient and accurate food research promoted by intermolecular interaction approaches: a review[J]. Food Chemistry, 2023, 416: 135726. DOI:10.1016/j.foodchem.2023.135726.

Crossref Google Scholar
[35]

SHAPAVAL V, SCHMITT J, MØRETRØ T, et al. Characterization of food spoilage fungi by FTIR spectroscopy[J]. Journal of Applied Microbiology, 2013, 114(3): 788-796. DOI:10.1111/jam.12092.

Crossref Google Scholar
[36]

CHENG J H, SUN D W. Partial least squares regression (PLSR) applied to NIR and HSI spectral data modeling to predict chemical properties of fish muscle[J]. Food Engineering Reviews, 2017, 9(1): 36-49. DOI:10.1007/s12393-016-9147-1.

Crossref Google Scholar
[37]

ZHANG Z P, SUN Y, SANG S Y, et al. Emerging approach for fish freshness evaluation: principle, application and challenges[J]. Foods, 2022, 11(13): 1897. DOI:10.3390/foods11131897.

Crossref Google Scholar
[38]

ZHANG F, KANG T H, SUN J F, et al. Improving TVB-N prediction in pork using portable spectroscopy with just-in-time learning model updating method[J]. Meat Science, 2022, 188: 108801. DOI:10.1016/j.meatsci.2022.108801.

Crossref Google Scholar
[40]

JAAFREH S, VALLER O, KREYENSCHMIDT J, et al. In vitro discrimination and classification of microbial flora of poultry using two dispersive Raman spectrometers (microscope and Portable Fiber-Optic systems) in tandem with chemometric analysis[J]. Talanta, 2019, 202: 411-425. DOI:10.1016/j.talanta.2019.04.082.

Crossref Google Scholar
[41]

LIU Q S, DONG P C, FENGOU L C, et al. Preliminary investigation into the prediction of indicators of beef spoilage using Raman and Fourier transform infrared spectroscopy[J]. Meat Science, 2023, 200: 109168. DOI:10.1016/j.meatsci.2023.109168.

Crossref Google Scholar
[42]

WANG K Q, YUE Z L, LIN H, et al. Rapid classification of the freshness grades of sea bass (Lateolabrax japonicus) fillets using a portable Raman spectrometer with machine learning method[J]. Microchemical Journal, 2023, 192: 108948. DOI:10.1016/j.microc.2023.108948.

Crossref Google Scholar
[43]

LIU S J, ZHU Y D, ZHAO L J, et al. Characteristic substance analysis and rapid detection of bacteria spores in cooked meat products by surface enhanced Raman scattering based on Ag@AuNP array substrate[J]. Analytica Chimica Acta, 2024, 1308: 342616. DOI:10.1016/j.aca.2024.342616.

Crossref Google Scholar
[44]

WANG Y M, FANG L R, WANG Y H, et al. Current trends of Raman spectroscopy in clinic settings: opportunities and challenges[J]. Advanced Science, 2024, 11(7): e2300668. DOI:10.1002/advs.202300668.

Crossref Google Scholar
[45]

ELMASRY G, KAMRUZZAMAN M, SUN D W, et al. Principles and applications of hyperspectral imaging in quality evaluation of agro-food products: a review[J]. Critical Reviews in Food Science and Nutrition, 2012, 52(11): 999-1023. DOI:10.1080/10408398.2010.543495.

Crossref Google Scholar
[46]

SONI A, DIXIT Y, REIS M M, et al. Hyperspectral imaging and machine learning in food microbiology: developments and challenges in detection of bacterial, fungal, and viral contaminants[J]. Comprehensive Reviews in Food Science and Food Safety, 2022, 21(4): 3717-3745. DOI:10.1111/1541-4337.12983.

Crossref Google Scholar
[47]

LU G L, FEI B W. Medical hyperspectral imaging: a review[J]. Journal of Biomedical Optics, 2014, 19(1): 10901. DOI:10.1117/1.JBO.19.1.010901.

Crossref Google Scholar
[48]

LI H H, KUTSANEDZIE F, ZHAO J W, et al. Quantifying total viable count in pork meat using combined hyperspectral imaging and artificial olfaction techniques[J]. Food Analytical Methods, 2016, 9(11): 3015-3024. DOI:10.1007/s12161-016-0475-9.

Crossref Google Scholar
[49]

ZHAO J X, MEHMOOD A, DONG Q L, et al. Rapid assessment of chilled chicken spoilage based on hyperspectral imaging technology and AdaBoost-RT[J]. Food Analytical Methods, 2023, 16(9): 1504-1511. DOI:10.1007/s12161-023-02501-9.

Crossref Google Scholar
[50]

QIU R C, ZHAO Y L, KONG D D, et al. Development and comparison of classification models on VIS-NIR hyperspectral imaging spectra for qualitative detection of the Staphylococcus aureus in fresh chicken breast[J]. Spectrochimica Acta Part A: Molecular and Biomolecular Spectroscopy, 2023, 285: 121838. DOI:10.1016/j.saa.2022.121838.

Crossref Google Scholar
Meat Research
Volume 39 Issue 2,
February 2025
Pages 46-54
DOI: 10.7506/rlyj1001-8123-20240902-228
Cite this article:
XIONG Y, LI C, LI R, et al. Application of New Microbial Detection Techniques in Meats: An Update. Meat Research, 2025, 39(2): 46-54. https://doi.org/10.7506/rlyj1001-8123-20240902-228
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