1. Introduction
Food free from harmful contaminants is vital for the well-being of humans. Food safety and human health are therefore priorities in most countries. Foodborne pathogens can contaminate food supplies leading to sudden and unexpected food outbreaks. Preventive measures, such as health publicity and education, effective import control, continuous food inspection, and good sanitation in manufacturing facilities often need to be supplemented with effective food monitoring and surveillance procedures. Those procedures must be capable of detecting pathogens before or shortly after they enter the food chain in order to isolate the source of the infection before it spreads to the wider population.
According to WHO, foodborne pathogens that contaminate human food and water became a major cause of human illness and mortality globally [
1]. Accidental consumption of them, either directly from livestock and poultry or directly from plant-based food, can result in food poisoning. Common foodborne pathogens include
Salmonella spp.,
Staphylococcus aureus,
Bacillus cereus,
Vibrio parahaemolyticus, Campylobacter spp., and
Shigella spp. [
2,
3].
In the United States, approximately 76 million people became infected annually with foodborne diseases. Outbreaks of foodborne diseases increased recently in China [
4], in the European Union (EU) [
5,
6,
7], and in Bari. The detection and management of foodborne pathogens are a crucially important step for preventing the disease, illness, and mortality they cause.
Traditional microbiological quantification techniques rely on the culture of the pathogens on specialized media, their isolation, followed by biochemical identification, which generally takes about 5~7 days, during which time the pathogens are spreading further from the source of contamination making effective management of them difficult. The development and implementation of rapid, simple, sensitive, and species-specific methods for detecting foodborne pathogens is urgently needed in the food industry and by public health protection agencies that aim to maximize the health and safety of consumers.
Recently developed technologies can be summarized into two categories, namely molecular methods and biosensors. Biosensors have benefits over conventional approaches by being faster, more cost effective, easy to carry out, and less labor-intensive. However, the results of biosensors are not very reliable, and there might be a need to develop a specific sensor for each food or specific analytical tools and sampling methods [
8,
9]. Therefore, various quantitative and molecular methods were developed that are suitable for the detection of foodborne pathogens [
10,
11]. This review summarizes the principles and characteristics of those methods, discusses their advantages and disadvantages, and assesses their suitability for the detection and identification of foodborne pathogens.
2. Polymerase Chain Reaction
Polymerase chain reaction (PCR) is a molecular method developed more than 30 years ago (Mullins et al. 1986) [
12] to rapidly increase copies of all or part of a DNA sequence specific to a particular pathogen that allows further analysis of that genetic sequence. As PCR can only detect a particular DNA sequence at a time, and there are often many pathogens in a contaminated food sample, many PCR methods were developed, including multiplex PCR (mPCR), nested PCR, reverse transcription PCR, and real-time fluorescent quantitative PCR (RT-qPCR). Our paper summarizes mPCR and RT-qPCR, as they are the most widely used for the pathogen detection and identification of foodborne pathogens.
2.1. Multiplex PCR
The advantages of mPCR are being highly species-specific, highly sensitive, and the capability of detecting different species of pathogenic organisms simultaneously [
13]. This technology enables the rapid detection of multiple microorganisms in a single reaction that can simultaneously amplify multiple sites. The principle of this technology is that multiple pairs of primers present in the reaction mixture amplify different target gene fragments in parallel. mPCR is mainly used for gene knockout, mutation analysis, and RNA detection. Thereby, this technology can improve the health and safety of food, as shown in
Figure 1.
As examples, Molina et al. (2015) [
14] used mPCR to identify Escherichia coli in a foodborne disease outbreak by targeting the lacZ and yaiO genes. Rosimin et al. (2016) [
15] used mPCR to rapidly identify Listeria monocytogenes in vegetables. Yang et al. (2021) [
16] used mPCR together with membrane chip technology to simultaneously detect nine species of foodborne pathogens in food. Li et al. (2019) [
17] used mPCR technology to detect the tlh, tdh and trh genes of
Vibrio parahaemolyticus. In this study, the prevalence rate of
Vibrio parahaemolyticus was 19%, indicating that this bacterium was highly pathogenic.
mPCR can therefore identify many different species of pathogens that commonly contaminate food and that cause similar poisoning symptoms in humans. However, since the design of primers is the key factor in developing mPCR determination, there may be some interactions between multiple primer sets, resulting in low amplification efficiency. Therefore, primer sets should be designed with similar annealing temperature, and provide a method to distinguish amplicons after a thermal cycle [
18]. Additionally, it can lead to a false positive result as living and dead bacteria cannot be distinguished [
19]. Therefore, mPCR can often lead to unsatisfactory results.
2.2. Real-Time Fluorescent Quantitative PCR
RT-qPCR includes chemicals that fluoresce in the PCR reaction system. The presence of pathogenic DNA causes the mixture to fluoresce, thereby enabling pathogen presence to be monitored in real time. The level of fluoresce can be used to quantify the concentration of pathogenic organisms present in the sample when compared with a standard curve [
20].
RT-qPCR is highly specific and sensitive. Amplified products are detected in real time without the need for post-PCR DNA analysis. For those reasons, RT-qPCR became one of the most preferred methods for detecting and identifying foodborne pathogens. The most commonly used RT-qPCR methods utilize TaqMan™ and LightCycler™ probes [
21].
Tetsuya et al. (2015) [
22] used RT-qPCR technology to detect three Stx1 subtypes and seven Sta2 subtypes linked to Shigella toxin. Nadin-Davis et al. (2018) [
23] used RT-qPCR technology to detect Salmonella in samples obtained from 239 poultry facilities.
Alia et al. (2020) [
24] developed quadruple probe RT-qPCR technology to distinguish between four serotypes of Listeria monocytogenes found in processed and prepared meat products. The probes are highly sensitive, species-specific, and rapid. Their research results were important for minimizing the risk of L. monocytogenes contamination in meat products and for establishing the routine surveillance of persistent strains of that organism in ready-to-eat meat products.
Zhang et al. (2020) [
25] compared RT-qPCR technology with the national standard method used to simultaneously detect foodborne pathogens in 60 animal, aquatic, and dairy products. Although RT-qPCR technology detected pathogens at a higher rate than the national standard method, it failed to detect the important pathogen Staphylococcus aureus.
Martin et al. (2013) [
26] detected Salmonella in cooked ham by RT-qPCR, and the results show that the detection limit was 103 CFU/g. Ma et al. (2013) [
27] detected Staphylococcus aureus, Salmonella, and Shigella in the detection of fresh pork, and their detection time was no more than 8 h, which was shorter than the conventional PCR detection time. The research results show that the detection limit of Staphylococcus aureus was 9.6 CFU/g, that of Salmonella was 2.0 CFU/g, and that of Shigella was 6.8 CFU/g. Ranjba et al. (2016) [
28] detected
E. coli O157:H7, and the detection efficiency was greatly improved. The detection could be completed in less than 30 min. The results show that the detection limit was 78 pg/tube.
RT-qPCR and ordinary culture methods both produced consistent results, but RT-qPCR produced results in less time. As RT-qPCR technology is carried out in a closed system, false positives caused by contaminants at the time of analysis can be avoided. As electrophoresis is not required after amplification, the time to carry out RT-qPCR is much shorter than the standard method. However, the cost of RT-qPCR technology is high, the equipment is expensive, and multiple probe RT-qPCR is difficult to undertake, as operators require a high level of technical skill [
29]. The summary of this part is shown in
Table 1.
3. Isothermal Amplification
Isothermal amplification is a novel nucleic acid amplification method that provides a rapid, sensitive, accurate, and specific test for the detection of foodborne pathogens. Specialized and expensive equipment is not required, which is appealing to many food production industries.
The main isothermal amplification technologies are loop-mediated isothermal amplification (LAMP) and nucleic acid sequence-based amplification (NASBA).
3.1. Loop-Mediated Isothermal Amplification
Notomi et al. (2000) [
32] first reported the molecular technique of nucleic acid amplification, termed loop-mediated isothermal amplification (LAMP), where a set of four (or six) different primers bind to six (or eight) different regions on the target gene, making it highly specific. All the reactions can be carried out under isothermal conditions ranging from 60 to 65 °C. Post-amplification electrophoresis is not required, as detection is simply by visual judgment with the unaided eye [
33]. The process of LAMP is as shown in
Figure 2.
The first application of LAMP for foodborne pathogens is to detect
stxA2 in
Escherichia coli O157:H7 cells. The mild permeability conditions and low isothermal temperature used in the in LAMP are less harmful than those caused by in situ PCR. The results show that the image contrast obtained by this method is higher than that of in situ PCR [
34].
Xu et al. (2021) [
35] used LAMP to detect common foodborne pathogens in dairy products. LAMP is very sensitive and rapid, as it can detect just a single copy of a gene within 30 min.
The LAMP method for rapid detection of the foodborne
Salmonella strains was developed and evaluated by Zhao et al. (2010) [
36] The optimal reaction condition was found to be 65 °C for 45 min, with the detection limit as 1 pg DNA/tube and 100 CFU/reaction. This application of LAMP assays was performed on 214 foodborne
Salmonella strains using a rapid procedure and easy result confirmation, where the specificity of LAMP and PCR assays was 97.7% (209/214) and 91.6% (196/214), respectively; with a 100% specificity for both assays. Simultaneously, high specificity was acquired when the LAMP assay was subjected to 39 reference strains, with no false positive amplification observed. In conclusion, this LAMP assay was demonstrated to be a useful and powerful tool for rapid detection of
Salmonella strains. Compared with previously reported LAMP assays for the detection of
Salmonella strain, the improved LAMP method in the present study offers advantages on easiness in operation and time consumption. The total detection time, including DNA preparation, LAMP reaction, and results determination, was approximately 60 min. Undoubtedly, rapidness, easiness, and cost-effectiveness of LAMP assay will aid in the broad application of bacteriological detection of
Salmonella.
LAMP is a relatively simple method for detecting specific pathogens. The amplification reaction can be completed at a constant temperature. LAMP is 1~2 orders of magnitude more sensitive than conventional PCR technology. The test can be undertaken in 30~60 min [
37]. However, designing the primers is difficult and costly; the length of an amplified sequence cannot exceed 300 bp; non-specific pairing between loop primers will lead to false positives; and cross-contamination is likely unless precautions to avoid contamination are put in place [
38].
3.2. Nucleic Acid Sequence-Based Amplification
Compared with traditional PCR that detects DNA, NASBA relies on the isothermal amplification of RNA in the target organism. Thermal cycling equipment is not required. NASBA contains a primer that binds to the target RNA sequence, and a cDNA strand is produced with reverse transcriptase. RNase H is then used to digest the template RNA and the cDNA is bound to a second primer for the production of double-stranded cDNA using reverse transcriptase. Finally, T7RNA polymerase is used to produce RNA transcripts via an amplification process.
This method is particularly suited to the detection of RNA viruses because an RNA polymerase is used to amplify RNA without conversion to cDNA [
39,
40,
41]. NASBA was proven to be a sensitive, specific, and rapid analysis method for the detection of several foodborne pathogens [
42]. Please see the
Table 2 below for specific examples.
NASBA is a sensitive transcription-based amplification system that uses a battery of three enzymes leading to a main amplification product of single-stranded RNA, and is specifically designed for the detection of RNA. NASBA is an established diagnostic tool in clinical use, with a theoretically bigger analytical sensitivity than reverse RT-PCR for pathogens detection, but is not progressing toward implementation in food analysis. This is unfortunate, because it has a potential for detection of viable cells through selective amplification of messenger RNA, even in a background of genomic DNA, which PCR does not possess. However, in some instances, an unexpected amplification of genomic DNA was observed using the NASBA technique. The availability of methods for rapid, sensitive, and selective detection of viable microbial pathogens in foods is a goal worth pursuing, and a developmental effort to explore and capitalize on NASBA’s potential in this regard could be worthwhile [
45].
4. Gene Chip Technology
GC technology was first proposed for biological detection more than 30 years ago (Southern et al. 1989) [
46]. This technology uses the principle of nucleic acid hybridization to detect the gene of the sample. A large number of DNA or RNA fragments are hybridized on the surface of the solid carrier in the way of base pairing, and then the hybridization signal and intensity are detected by the scanning system, so as to obtain the gene information of the sample and realize qualitative and quantitative, as shown in
Figure 3.
GC fixed the probe molecule on the surface of the chip, and then hybridized with the marked sample molecule. The sample molecular data were obtained by monitoring the strength of the hybridization signal to distinguish the bacterial species. In recent years, the research on the detection of foodborne pathogens by GC gradually increased. GC is the first developed, the earliest in research and development, and the most used technology in biochip technology. In the detection process of foodborne pathogens, this technology can effectively distinguish living bacteria.
EOM et al. (2007) [
44] achieved the simultaneous detection of seven strains, such as
Shigella by 16S rDNA-based gene chip technology, but the disadvantage is that false positive results very easily appear in the process of multiple detections of actual samples. Researchers used gene chip technology to comprehensively compare the whole genome sequences of 11 species of
Campylobacter jejuni, and obtained specific indicators of the pathogenicity of
Campylobacter jejuni, which effectively verified the biological characteristics of the strain [
45].
GC is a highly specific and sensitive molecular technology that can process samples at a high throughput and detect many pathogens at the same time. The technology is simple to operate and produces accurate data rapidly. However, the equipment is expensive and it requires a high level of skill to operate. GC preparation and the hybridization process to detect relatively few pathogens is time-consuming, costly, and has poor repeatability [
46,
47]. Therefore, the food testing industry needs to expand the application potential of GC through technological research and development, and better ensure the safety of food production and processing from two aspects: the detection of food raw materials and the detection of pathogenic microorganisms.
5. Gene Probe Technology
Gene probe technology was first proposed more than 45 years ago (Southern 1975). This technology depends on being able to make a probe using the DNA sequence of a known gene. When that sequence comes into contact with denatured single-stranded genomic DNA, and if the bases of the two are completely paired, they complement each other to form a double strand, and thereby show that the genomic DNA tested contains a known gene sequence. A complementary DNA single strand can be combined into a double strand under certain conditions. This combination is specific and is carried out according to base complementarity not only between DNA and DNA, but also between DNA and RNA. There are two necessary conditions for gene detection: one is the necessary specific DNA probe; and the second is essential genomic DNA. When both are denatured into a single chain state, molecular hybridization occurs [
48].
Gene probe technology uses the characteristics of gene denaturation and repeatability to compare and study the gene sequences in food, so as to ensure the safety of food. In the current gene probe technology, two methods are mainly used: heterogeneous hybridization and in-phase hybridization. These two methods are based on gene probes to detect
Staphylococcus aureus,
Salmonella, and other bacteria harmful to human health in food to judge whether the quality of food meets the standards of the relevant departments [
49].
Gene probe technology was used to rapidly detect a variety of foodborne pathogens for many years [
50,
51]. This technology, when combined with PCR, can detect many pathogen species simultaneously. However, the non-specific fluorescent probe can interact with complex components present in food, leading to inaccurate results. That limitation can be overcome by selecting biological molecules that have a high degree of specificity.
6. Summary
Although the traditional detection method of foodborne pathogens is sensitive enough, it is often too time-consuming for practical use, and it takes a few days to a week to complete. Therefore, a new method to overcome this performance limitation is needed. Recently, several methods for rapid detection of foodborne pathogens were explored and developed. However, most of them still need to improve sensitivity, selectivity, or accuracy to be used for any practical purpose. We summarized several various detection methods, as shown in the
Table 3.
Nucleic acid-based methods have high sensitivity and require less time to detect foodborne pathogens and toxins than traditional culture-based techniques, but most of them require trained personnel and expensive instruments, which limits their use in the actual environment. The rapid and accurate detection of foodborne pathogens harmful to humans in the food chain is an important first step toward controlling those pathogens and then eliminating them.
Molecular technology was used for almost 50 years to detect the most common foodborne pathogens. Although it can rapidly detect specific species of pathogens present at low levels in food, the specialized equipment needed to undertake the tests can be expensive, often the gene sequencing of the target pathogens was completed, which limits the use of the technology, and some, such as mPCR and RT-qPCR, require a high level of operational skill. Others, such as the LAMP, are faster than other molecular technologies at detecting and identifying soilborne pathogens accurately and reliably. The accuracy and sensitivity of the technology for rapidly detecting foodborne pathogens will inevitably improve, and the detection cost will reduce to the point where it can be applied routinely as part of a foodborne pathogen surveillance programme. In recent years, biosensors were developed as new diagnostic methods to minimize the limitations of common pathogen detection methods. However, the results of biosensors can be false negatives by using phage or plasmid host ranges that are either too specific or too extensive. Therefore, a reliable, accurate, fast, simple, sensitive, selective, and cost-effective detection method will be ideal. This pathogen detection method will provide great commercial advantages in the food industry and related fields. Moreover, the trend across various methods will produce new devices or methods to enhance the advantages of rapid detection methods.
Our research provides, as a basis for further exploring other foodborne pathogen detection and identification, methods that have the potential to be developed for that purpose. High-throughput, cost-effective, low-skill methods have yet to be developed for many of the common foodborne pathogens. Molecular technologies will need to be continuously improved to reduce the risk of foodborne pathogens affecting human health. In conclusion, there are many promising applications in the field of rapid and automated detection of foodborne pathogens. In view of the wide applicability and great potential of these methods, there is still a great opportunity for further development in the near future.
Author Contributions
Conceptualization, Y.L.; methodology, Q.W.; validation, J.W.; formal analysis, D.Y.; investigation, Z.S.; resources, A.C.; data analysis, W.F.; writing—original draft preparation, M.Z.; writing—review and editing, Y.L.; visualization, J.W.; supervision, Y.L.; project administration, A.C.; funding acquisition, Y.L. All authors have read and agreed to the published version of the manuscript.
Funding
This research was funded by Beijing Innovation Consortium of Agriculture Research System (BAIC01-2022), National Key Research and Development Program of China (2018YFD0201300), Climate Smart Grassland Ecosystem Management Project (CSMG-C-8) and a fund supported by Hebei Technology Innovation Center for Green Management of Soil-Borne Diseases, Baoding University.
Institutional Review Board Statement
Not applicable.
Informed Consent Statement
Not applicable.
Data Availability Statement
All data are contained within the manuscript.
Acknowledgments
The authors thank Tom Batchelor for providing helpful comments and editing on early drafts of the manuscript.
Conflicts of Interest
The authors declare no competing interest.
References
- Ye, M.Q. Study on the Detection of Food Borne Pathogens by Immunofluorescence and Infrared Spectroscopy; Shanghai Normal University: Shanghai, China, 2009. [Google Scholar]
- Velusamy, V.; Arshak, K.; Korostynska, O.; Oliwa, K.; Adley, C. An overview of foodborne pathogen detection: In the perspective of biosensors. Biotechnol. Adv. 2010, 28, 232–254. [Google Scholar] [CrossRef]
- Franz, C.M.A.P.; den Besten, H.M.W.; Böhnlein, C.; Gareis, M.; Zwietering, M.H.; Fusco, V. Reprint of: Microbial food safety in the 21st century: Emerging challenges and foodborne pathogenic bacteria. Trends Food Sci. Technol. 2019, 84, 34–37. [Google Scholar] [CrossRef]
- Prieto, M.; Colin, P.; Fernandez, P.S.; Alvarez-Ordóñez, A. Epidemiology, Detection, and Control of Foodborne Microbial Pathogens. BioMed. Res. Int. 2015, 2015, 617417. [Google Scholar] [CrossRef] [Green Version]
- EFSA (European Food Safety Authority); ECDC (European Centre for Disease Prevention and Control). The European Union summary report on trends and sources of zoonoses, zoonotic agents and food-borne outbreaks in 2015. EFSA J. 2016, 14, 4634–4865. [Google Scholar]
- Martinelli, D.; Fortunato, F.; Tafuri, S.; Cozza, V.; Chironna, M.; Germinario, C.A.; Pedalino, B.; Prato, R. Lessons learnt from a birthday party: A Bacillus cereus outbreak, Bari, Italy, January 2012. Ann. Ist. Super. Sanità 2013, 49, 391–394. [Google Scholar]
- Lahti, E.; Löfdahl, M.; Ågren, J.; Hansson, I.; Engvall, E.O. Confirmation of a Campylobacteriosis Outbreak Associated with Chicken Liver Pâté Using PFGE and WGS. Zoonoses Public Health 2016, 64, 14–20. [Google Scholar] [CrossRef]
- Aliakbar Ahovan, Z.; Hashemi, A.; De Plano, L.M.; Gholipourmalekabadi, M.; Seifalian, A. Bacteriophage based biosensors: Trends, outcomes and challenges. Nanomaterials 2020, 10, 501. [Google Scholar] [CrossRef] [Green Version]
- Ali, A.A.; Altemimi, A.B.; Alhelfi, N.; Ibrahim, S.A. Application of Biosensors for Detection of Pathogenic Food Bacteria: A Review. Biosensors 2020, 10, 58. [Google Scholar] [CrossRef]
- Moreira, M.A.B.; Luvizotto, M.C.R.; Garcia, J.F.; Corbett, C.E.P.; Laurenti, M.D. Comparison of parasitological, immunological and molecular methods for the diagnosis of leishmaniasis in dogs with different clinical signs. Vet. Parasitol. 2007, 145, 245–252. [Google Scholar] [CrossRef]
- Rompre, A.; Servais, P.; Baudart, J.; De-Roubin, M.R.; Laurent, P. Detection and enumeration of coliforms in drinking water: Current methods and emerging approaches. J. Microbiol. Methods 2002, 49, 31–54. [Google Scholar] [CrossRef] [PubMed]
- Mullis, K.; Faloona, F.; Scharf, S.; Saiki, R.; Horn, G.; Erlich, H. Specific enzymatic amplification of DNA in vitro: The polymerase chain reaction. Cold Spring Harb. Symp. Quant. Biol. 1986, 51, 263–273. [Google Scholar] [CrossRef] [PubMed] [Green Version]
- Yu, Q.; Huang, M.N. Research on rapid detection of foodborne pathogenic bacteria by multiplex PCR. Food Res. Dev. 2014, 35, 125–127. [Google Scholar]
- Molina, F.; López-Acedo, E.; Tabla, R.; Roa, I.; Gómez, A.; Rebollo, J.E. Improved detection of Escherichia coli and coliform bacteria by multiplex PCR. BMC Biotechnol. 2015, 15, 48. [Google Scholar] [CrossRef]
- Rosimin, A.A.; Kim, M.-J.; Joo, I.-S.; Suh, S.-H.; Kim, K.-S. Simultaneous detection of pathogenic Listeria including atypical Listeria innocua in vegetables by a quadruplex PCR method. LWT 2016, 69, 601–607. [Google Scholar] [CrossRef]
- Yang, D.N.; Liu, M.H.; Zhang, X.J. Rapid screening of food-borne pathogens in non-prepackaged ready-to-eat foods by PCR-membrane chip technology. J. Food Saf. Qual. Insp. 2021, 12, 560–568. [Google Scholar]
- Li, Y.; Pei, X.; Yan, J.; Liu, D.; Zhang, H.; Yu, B.; Li, N.; Yang, D. Prevalence of foodborne pathogens isolated from retail freshwater fish and shellfish in China. Food Control 2018, 99, 131–136. [Google Scholar] [CrossRef]
- Mukhopadhyay, A.; Mukhopadhyay, U.K. Novel multiplex PCR approaches for the simultaneous detection of human patho-gens: Escherichia coli 0157: H7 and Listeria monocytogene. J. Microbiol. Methods 2007, 68, 193–200. [Google Scholar] [CrossRef]
- Tang, T.T.; Han, G.Q.; Wang, L.N. Research progress of molecular biology technology in detection of foodborne pathogens. J. Food Saf. Qual. 2016, 7, 3497–3502. [Google Scholar]
- Santaclara, F.J.; Velasco, A. Development of a multiplex PCR-ELISA method for the genetic authentication of Thunnus, species and Katsuwonus pelamis, in food products. Food Chem. 2015, 180, 9–16. [Google Scholar] [CrossRef]
- Zhang, W.C. Biology Lab Series Real-Time PCR; Chemical Industry Press: Beijing, China, 2013. [Google Scholar]
- Tetsuya, H.; Atsusi, I.; Sunao, I. Multiplex real-time pcr assays for screening of shiga toxin 1 and 2 Genes, Including All Known Subtypes, and Escherichia coli O26-, O111, and O157-specific genes in beef and sprout enrichment cultures. Food Prot. 2015, 78, 1800–1811. [Google Scholar]
- Nadin-Davis, S.; Pope, L.; Ogunremi, D.; Brooks, B.; Devenish, J. A real-time PCR regimen for testing environmental samples for Salmonella enterica subsp. enterica serovars of concern to the poultry industry, with special focus on Salmonella Enteritidis. Can. J. Microbiol. 2018, 65, 162–173. [Google Scholar] [CrossRef]
- Alía, A.; Andrade, M.J.; Córdoba, J.J. Development of a multiplex real-time PCR to differentiate the four major Listeria monocytogenes serotypes in isolates from meat processing plants. Food Microbiol. 2020, 87, 103367. [Google Scholar] [CrossRef]
- Zhang, Y.X.; Lu, Y.; Wang, X.Y.; Cen, Y.; Liu, H.T.; Wang, F.; Xue, X.J. Application and research of real-time fluorescent polymerase chain reaction technology and national standard method in detection of pathogenic bacteria. J. Food Saf. Qual. 2020, 6, 1941–1946. [Google Scholar]
- Martin, B.; Raurich, S.; Garriga, M. Effect of amplicon length in propidiummonoazidequantitative PCR for the enumeration of viable cells of Salmonella in cooked ham. Food Anal. Methods 2013, 6, 683–690. [Google Scholar] [CrossRef]
- Ma, K.; Deng, Y.; Bai, Y. Rapid and simultaneous detection of Salmonella, Shigella, and Staphylococcus aureus in fresh pork using a multiplex PCR assay based on immunomagnetic separation. Food Control 2014, 42, 87–93. [Google Scholar] [CrossRef]
- Ranjbar, R.; Erfanmanesh, M.; Afshar, D.; Mohammadi, M.; Ghaderi, O.; Haghnazari, A. Visual Detection of Enterohemorrhagic Escherichia coli O157:H7 Using Loop-Mediated Isothermal Amplification. Electron. Physician 2016, 8, 2576–2585. [Google Scholar] [CrossRef] [PubMed] [Green Version]
- Freeman, W.M.; Walker, S.J.; Vrana, K.E. Quantitative RT-PCR: Pitfalls and potential. Biotechniques 1999, 26, 112–125. [Google Scholar] [CrossRef] [PubMed]
- Vojkovska, H.; Myšková, P.; Gelbíčová, T.; Skočková, A.; Koláčková, I.; Karpíšková, R. Occurrence and characterization of food-borne pathogens isolated from fruit, vegetables and sprouts retailed in the Czech Republic. Food Microbiol. 2017, 63, 147–152. [Google Scholar] [CrossRef] [PubMed]
- Saw, S.H.; Mak, J.; Tan, M.; Teo, S.; Tan, T.; Cheow, M.; Ong, C.; Chen, S.; Yeo, S.K.; Kuan, C.S.; et al. Detection and quantification of Salmonella in fresh vegetables in Perak, Malaysia. Food Res. 2019, 4, 441–448. [Google Scholar] [CrossRef] [PubMed]
- Notomi, T.; Okayama, H.; Masubuchi, H.; Yonekawa, T.; Watanabe, K.; Amino, N.; Hase, T. Loop-mediated isothermal amplification of DNA. Nucl. Acids Res. 2000, 28, E63. [Google Scholar] [CrossRef] [Green Version]
- Song, T.; Toma, C.; Nakasone, N. Sensitive and rapid detection of Shigella and enteroinvasive Escherichia coli by a loop-mediated isothermal amplification method. FEMS Microbiol. Lett. 2005, 243, 259–263. [Google Scholar] [CrossRef] [Green Version]
- Maruyama, F.; Kenzaka, T.; Yamaguchi, N.; Tani, K.; Nasu, M. Detection of Bacteria Carrying the stx 2 Gene by In Situ Loop-Mediated Isothermal Amplification. Appl. Environ. Microbiol. 2003, 69, 5023–5028. [Google Scholar] [CrossRef] [Green Version]
- Xu, W.W.; Song, H.Y.; Liang, Y.L.; Liu, X.; Yin, J.J.; Song, Q.H.; Ding, M.X.; Zhou, P.F. Detection of common food borne pathogens in different dairy products by loop-mediated isothermal amplification. J. Food Saf. Qual. Insp. 2021, 12, 546–551. [Google Scholar]
- Zhao, X.; Wang, L.; Chu, J.; Li, Y.; Li, Y.; Xu, Z.; Li, L.; Shirtliff, M.; He, X.; Liu, Y.; et al. Development and application of a rapid and simple loop-mediated isothermal amplification method for food-borne Salmonella detection. Food Sci. Biotechnol. 2010, 19, 1655–1659. [Google Scholar] [CrossRef]
- Liang, Y.L.; Liu, X.; Ding, M.X. Application progress of loop-mediated isothermal amplification in the detection of typical foodborne pathogens. Food Res. Dev. 2017, 18, 229–234. [Google Scholar]
- Ge, H.; Wu, M.C.; Zhang, M.Z.; Yu, X.P. Research progress of isothermal amplification detection technology for foodborne pathogens. J. Anal. Test. 2019, 38, 874–881. [Google Scholar]
- Nadal, A.; Coll, A.; Cook, N.; Pla, M. A molecular beacon-based real time NASBA assay for detection of Listeria monocytogenes in food products: Role of target mRNA secondary structure on NASBA design. J. Microbiol. Methods 2007, 68, 623–632. [Google Scholar] [CrossRef]
- Loens, K.; Beck, T.; Goossens, H.; Ursi, D.; Overdijk, M.; Sillekens, P.; Ieven, M. Development of Conventional and Real-Time Nucleic Acid Sequence-Based Amplification Assays for Detection of Chlamydophila pneumoniae in Respiratory Specimens. J. Clin. Microbiol. 2006, 44, 1241–1244. [Google Scholar] [CrossRef] [Green Version]
- Yoo, J.-H.; Choi, S.-M.; Choi, J.-H.; Kwon, E.-Y.; Park, C.; Shin, W.-S. Construction of internal control for the quantitative assay of Aspergillus fumigatus using real-time nucleic acid sequence-based amplification. Diagn. Microbiol. Infect. Dis. 2008, 60, 121–124. [Google Scholar] [CrossRef]
- Rodríguez-Lázaro, D.; Hernández, M.; D’Agostino, M.; Cook, N. Application of nucleic acid sequence-based amplification for the detection of viable foodborne pathogens: Progress and challenges. J. Rapid Methods Autom. Microbiol. 2006, 14, 218–236. [Google Scholar] [CrossRef]
- Zhang, Q.; Sheng, J. Development and application of gene chip technology. Acta Acad. Med. Sin. 2008, 30, 344–347. [Google Scholar]
- Eom, H.S.; Hwang, B.H.; Kim, D.H. Multiple detection of food-borne pathogenic bacteria using a novel 16S rDNA-based oligonucleotide signature chip. Biosens. Bioelectron. 2007, 22, 845–853. [Google Scholar] [CrossRef]
- Sun, H.; Mo, Q.-H.; Lin, J.-C.; Yang, Z.; Tu, C.-N.; Gu, D.-Y.; Shi, L.; Lu, W.-P. Rapid simultaneous screening of seven clinically important enteric pathogens using a magnetic bead based DNA microarray. World J. Microbiol. Biotechnol. 2010, 27, 163–169. [Google Scholar] [CrossRef]
- Shi, X.-M.; Long, F.; Suo, B. Molecular methods for the detection and characterization of foodborne pathogens. Pure Appl. Chem. 2010, 82, 69–79. [Google Scholar] [CrossRef]
- Heller, M.J. DNA microarray technology: Devices, systems, and applications. Annu. Rev. Biomed. Eng. 2002, 4, 129–153. [Google Scholar] [CrossRef] [Green Version]
- Xu, M.J. Gene probe technology and its application in food hygiene detection. Food Ferment Ind. 2001, 27, 66–71. [Google Scholar]
- Zhang, X.M. Rapid detection of Staphylococcus aureus in food by gene probe method. Biotech. World 2015, 9, 12. [Google Scholar]
- Zhao, J.; Rong, J.Q.; Ye, J.L. Identification of pathogenic Escherichia coli toxin gene by Allglo probe technique. Zhejiang Pre Med. 2014, 26, 646–648. [Google Scholar]
- Severgnini, M.; Cremonesi, P.; Consolandi, C. Advances in DNA microarray technology for the detection of foodborne patho-gens. Food Bioprocess Technol. 2011, 4, 936–953. [Google Scholar] [CrossRef]
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