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Review

Molecular Methods for Detecting Microorganisms in Beverages

by
Ekaterina Nesterova
1,2,
Polina Morozova
1,2,
Mariya Gladkikh
1,
Shima Kazemzadeh
1 and
Mikhail Syromyatnikov
1,2,*
1
Laboratory of Metagenomics and Food Biotechnology, Voronezh State University of Engineering Technologies, 394036 Voronezh, Russia
2
Department of Genetics, Cytology and Bioengineering, Voronezh State University, 394036 Voronezh, Russia
*
Author to whom correspondence should be addressed.
Beverages 2024, 10(2), 46; https://doi.org/10.3390/beverages10020046
Submission received: 30 April 2024 / Revised: 7 June 2024 / Accepted: 12 June 2024 / Published: 17 June 2024
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Abstract

Beverages are an integral component of a person’s food package. Various types of microorganisms widely contaminate beverages. This review presents current research data aimed at identifying dominant microorganisms in beverages and molecular methods for their detection. Wine, beer, dairy drinks, and fruit juices were selected as the main objects of the study. The most contaminated beverage turned out to be fruit juice. As a result of a large number of independent studies, about 23 species of microorganisms were identified in it. At the same time, they are represented not only by bacterial and fungal organisms, but also by protozoa. Milk turned out to be the least contaminated in terms of detected bacteria. The most common pollutants of these beverages were Staphylococcus aureus, Bacillus cereus, and Vibrio parahaemolyticus. It has been established that among pathogenic genera, Salmonella sp., Campylobacter sp. and Shigella sp. are often present in beverages. One of the main tools for the quality control of beverages at all stages of their production is different types of polymerase chain reaction. The sequencing method is used to screen for microorganisms in beverages. The range of variations of this technology makes it possible to identify microorganisms in alcoholic and non-alcoholic beverages. The high specificity of methods such as PCR-RFLP, Rep-PCR, qPCR, End-point PCR, qLAMP, the molecular beacon method, and RAPD enables fast and reliable quality control in beverage production. Sequencing allows researchers to evaluate the microbiological diversity of all the studied beverages, while PCR varieties have demonstrated different fields of application. For example, PCR-RFLP, RAPD-PCR, and PCR allowed the identification of microorganisms in fruit juices, qPCR, LAMP, and the molecular beacon method in wine, LAMP and multiplex PCR in milk, and End-point PCR and Rep-PCR in beer. However, it is worth noting that many methods developed for the detection of microbial contaminants in beverages were developed 10–20 years ago; modern modifications of PCR and isothermal amplification are still poorly implemented in this area.

1. Introduction

The main aspect of food production is the safety of food for human health. Drinks are an integral component of an individual’s diet. It has been proven that pathogenic microorganisms widely contaminate beverages [1]. The primary source of pollution is the production inventory and the room as a whole. Secondary contaminants include violations of the packaging, storage, and transportation conditions of food [2,3]. The following species are considered to be the main food pathogens: Staphylococcus aureus, Bacillus cereus, and Vibrio parahaemolyticus. Pathogenic genera include Salmonella, Campylobacter, and Shigella [4,5].
To assess the spoilage of manufactured products, the main visual parameters of quality deterioration are color change, turbidity, a specific non-characteristic odor, swollen packaging, and an altered aftertaste [6,7]. A lack of a timely comprehensive approach to quality control at all stages of food production can lead to contaminated products entering the market. Some methods of monitoring and recognizing the sources of pathogens in food and beverages do not always correspond to modern production technologies and are therefore considered outdated. In this regard, any organization involved in the manufacture of food products should have or cooperate with laboratories, where there is a complex interaction of traditional routine microbiological techniques and molecular genetic analyses [8,9,10].
Approaches for detecting microorganisms are usually based on traditional cultivation methods and their modifications. Modern analytical methods are based on PCR, immunoassay, and high-throughput sequencing [11]. The method of surface-enhanced Raman spectroscopy (SERS) in combination with the use of solid-phase substrates based on Au nanostars makes it possible to quickly and effectively detect pathogens in beverages [12]. The detection of pathogens in milk using QCM immunosensors is possible, but only if high concentrations are reached after several hours of incubation [13].
The high-throughput sequencing of fresh milk samples obtained by mechanical and manual milking showed a high level of contamination of products with various species of fungi of the genera Candida, Kluyveromyces, Pichia, and Kodamaea [14]. Full-metagenomic sequencing is a culturally independent technique for determining potential bacterial pathogens in foods and beverages. This highly sensitive approach allows detecting microorganisms whose extraction from the sample is difficult. Based on it, Brucella spp., Salmonella enterica, and E. coli were found in raw milk without using the enrichment procedure [15]. Metagenomic sequencing using long reads for the identification of eae-positive STEC strains is a relatively new developing technique aimed at solving the problem of the identification of pathogenic microorganisms in food. With its help, the presence of shigatoxins produced by E. coli was revealed in samples of raw milk from cattle [16].
The polymerase chain reaction (PCR) method has become one of the main tools for the quality control of beverages and food at all stages of production [17,18]. Figure 1 shows the main molecular methods for identifying microorganisms in beverages found in the analysis publications in the open databases PubMed, Web of Science, and Scopus.
The main advantages of amplification are high specificity, low detection limit, fast results, and the automation of the process, starting from the sample preparation stage [19,20,21,22]. For example, the developed analysis based on multiplex PCR (mPCR) makes it possible to detect several pathogenic microorganisms at once in one reaction, which speeds up the time to obtain reliable results [23,24,25]. Unlike the culture method, which made it possible to determine the presence of staphylococci in 23% of samples, the qPCR method identified the S. aureus pathogen in 60% of the studied milk samples, which characterizes PCR as a highly sensitive technique [26].
It should be noted that raw foods, such as fruits and vegetables, are a source of a diverse microbiome, and their poor quality can lead to an increase in the likelihood of contamination of the final product of food production [27]. Due to the sugars, vitamins, antioxidants, and polyphenols included in the composition, fruits have become an optimal and balanced environment for the growth of bacteria and fungi [28]. Microorganisms contaminate food and beverages due to their specific metabolic activities, accompanied by physicochemical reactions. Pathogens have high rates of adaptation to the environment, surviving in extreme conditions [28]. Members of the genera Pichia, Candida, Saccharomyces, and Rhodotorula have increased resistance to an environment with high acidity and a large amount of sugars. Pasteurized soft drinks packaged in plastic are particularly susceptible to contamination by such yeasts [29,30]. It is known that representatives of lactic acid bacteria Lactobacillus and Leuconostoc found in fruit juices demonstrated their resistance to such preservatives as benzoic and sorbic acid [31,32]. Fermented foods, such as wine, are a favorable environment for the growth of fungal microorganisms, among which there are also yeasts. They change the organoleptic properties of the drink due to excessive gas formation and the production of lipases, cellulases, and proteases [33]. A similar problem may affect not only wine and cider, but also traditional ethnogeographic drinks, including hardaliye [34], tepache [35], and khadi [36].
In addition to changing the taste, smell, and color of food products, contaminating fungi are sources of the low-toxic compounds patulin, aflatoxins, and ochratoxin A [37]. These toxic metabolic products have a negative impact on all organ systems of humans and animals. Therefore, mycotoxins are included in the list of teratogenic and carcinogenic compounds [38,39]. The ochratoxin A contamination of juice and wine during grape harvest has been proven [40]. In this regard, this study is devoted to the assessment of data from the existing literature related to the identification of microbiological contaminants of beer, wine, juice, and dairy drinks using molecular methods.

2. Microorganisms in Beer

Beer is a foamy drink made mainly from barley malt and hops [41]. This product has received its unique taste due to the peculiarities of its preparation and fermentation, in which microorganisms are directly involved [42]. It is known that beer is a microbiologically stable drink [43]. But, despite this, breweries face problems associated with the increased activity of lactic acid bacteria, including Lactobacillus brevis, Lactobacillus lindneri, and Pediococcus damnosus, as well as some Gram-negative bacteria such as Pectinatus cerevisiiphilus, Pectinatus frisingensis, and Megasphaera cerevisiae [44,45]. The main pollutants most frequently detected in beer drinks are shown in Figure 2A.
Competition of bacteria with yeast for the substrate consequently reduces the yield of ethanol, affects the taste characteristics of the finished product (high concentrations of lactic acid and diacetyl) and its quality (discoloration of the drink and turbidity), and causes premature spoilage [46]. It has been proven that the presence of bacteria of the genus Pectinatus and Lactobacillus in beer leads to the turbidity of the liquid and the formation of an unpleasant taste [47,48].
The analysis of the quality of beer drinks can be carried out by methods and technologies based on the use of molecular genetic developments, mainly PCR, in combination with cultural methods. The PCR-DGGE method made it possible to identify Lactobacillus brevis in finished products and in components used to make beer. The research conducted by Cristiana Garofalo et al. tracked Lactobacillus brevis within a brewery and during the craft beer production process by analyzing samples from indoor air, surfaces, yeast, and beer. Using the PCR-DGGE technique along with culture-dependent methods, Lactobacillus brevis was identified as responsible for beer spoilage. The study of samples obtained from the working surfaces in the brewery revealed the presence of lactic acid bacteria and bacteria of the genera Staphylococcus and Acetobacter, as well as bacteria of the Enterobaceriaceae family, which are potential pollutants of beer [49]. PCR analysis and the sequencing of the conservative 16S rRNA region of “light” beer samples showed a low content of lactic acid bacteria in beer. They turned out to be Lactobacillus brevis, Lactobacillus backii, and Lactobacillus harbinensis [46,50]. However, it should be noted that L. brevis is a common cause of beer spoilage [51]. Researchers from Argentina used ribosomal gene sequencing (16S ribosomal subunit) to identify the beer microbiome. PCR was performed with primers 27F/1495R for bacterial isolates and ITS1/ITS4 for fungal isolates in beer [52,53]. Members of the following genera of lactic acid bacteria were found: Levilactobacillus, Lactobacillus, and Pediococcus; and acetic acid bacteria: Flavobacterium and Proteus. The fungal isolates were ascomycetes of the genera Wickerhamomyces, Clavispora, Wickerhamiella, and brazidiomycetes of the genera Trichosporon and Naganishia [53].
The results of Geissler et al.’s study indicate the important role of bacterial plasmid DNA in the contamination of brewing equipment, raw materials, and finished products. Thanks to sequencing technology, complete representations of such beer-contaminant lactic acid bacteria as Lactobacillus paracollinoides, Lactobacillus lindneri, and Pediococcus claussenii have been obtained [54]. The sequencing and genome assembly of two bacterial strains of Loigolactobacillus backii KKP 3565 and KKP 3566, found in production and in beer itself, revealed the presence of hop resistance genes hitA, horA, and horC. The genome of bacterial strains contained inserts from other contagious species. These genetic constructs contribute to the adaptation of representatives of lactic acid bacteria to the harsh environment of the brewery, including carrying out vital activities at high concentrations of hops [55]. A similar study, in which lactic acid bacteria were identified in beer, was carried out using the PCR method with primers for the hop resistance genes horA and horC. Twelve species of bacteria associated with beer spoilage belonging to the genera Lactobacillus, Pectinatus, Pediococcus, and Megasphaera were detected [56]. In Haakensen’s work, the species Bacillus cereus, Bacillus licheniformis, Staphylococcus epidermidis, and Paenibacillus humicus, which were not previously associated with beer spoilage, were identified. Their identification was carried out using PCR with primers aimed at the multiple resistance gene MDR horA. It was revealed that this gene in the genera Lactobacillus and Pediococcus is responsible for active growth in beer drinks [57]. Because of the developed PCR analysis of the variable region of the 16S rRNA gene, with a subsequent restriction reaction with KpnI, XmnI, BssHI, and ScaI nucleases, it was possible to identify representatives of anaerobic contaminants of the genera Pectinatus, Megasphaera, Selenomonas, and Zymophilus in beer [47,58,59].

3. Microorganisms in Wine

Wine is one of the most popular alcoholic beverages obtained as a result of the full or partial alcoholic fermentation of grapes [60]. Grape must is an intermediate stage in wine production, but despite this, in some regions, it is used as an independent product [61,62]. It does not have the noble organoleptic combinations characteristic of wine, is low in quality, and may contain microbial contaminants in its composition (Aspergillus spp. and Penicillium spp.) due to the fact that it is a by-product of production [63]. For example, the nested PCR method, based on the identification of representatives of the genus Penicillium due to specific primers to the gene encoding the β-tubulin protein, makes it possible to successfully identify this contagious microorganism not only in wine but also in all intermediate products of winemaking [60,64].
Depending on grape variety, fermentation conditions, and the amount of sugar syrup, the wine can be divided into white, red, dry, sweet, sparkling, and still [65]. A mandatory indicator of high-quality sparkling wine is its ability to foam. This issue is subject to active study in connection with the emergence of the problem of the reduced carbonation and volatility of wines [66,67,68]. It has been proven that metabolic products such as proteases produced by the pathogenic fungus Botrytis cinerea affect the quality and quantity of foaming sparkling wines. Proteases have a destructive effect on the vast majority of macromolecules of grape juice during fermentation reactions [69]. The SDS-PAGE method demonstrated the enzymatic activity of members of the genus Botrytis [70]. The contamination of wines of various varieties with non-saccharomycete yeasts can be controlled using the RFLP-PCR method. Such a study was conducted on Brazilian wines, where HinfI and HaeIII restrictases acted as restriction enzymes. The unique restriction pattern corresponded to representatives of Brettanomyces/Dekkera bruxelliensis, Pichia guillermondii, Candida wickerhamii, and Trigonopsis cantarelli. The bioinformatic analysis of the sequenced ITS1-5 site.8S-ITS2 and the D1/D2 domain confirmed the results obtained [71].
Members of the genus Brettanomyces, for example, Brettanomyces bruxellensis, are difficult-to-cultivate fungi and cause the spoilage of red wines [72]. They produce ethylphenols and ethyl guaiacols, which lead to the formation of biogenic amines in the product [73]. To control the fungal contamination of wines, a PCR method with primers to the D1/D2 domains of the 26S rRNA gene was developed, which demonstrated high efficiency in detecting S. cerevisiae, Hanseniaspora uvarum, and Dekkera bruxellensis species [74,75]. An optimized amplification reaction technique with DBRUX F/R primers (26S rRNA gene), Brett F/R and Rad F/R (RAD4 gene), and Act F/R (actin gene) for the identification of members of Brettanomyces and Dekkera spp. has also been demonstrated in wine [76,77,78,79].
In wine production, high concentrations of weak acids are often used as preservatives; however, even in such environmental conditions, the species Zygosaccharomyces bailii is actively growing and multiplying [80]. PCR analysis with ZB F1/R1 primers effectively identifies this pathogen in wine samples, even in the presence of non-target DNA [81]. Fungi, which often burden food products, including wine, include representatives of the genera Aspergillus and Penicillium. They are producers of the carcinogen ochratoxin A (OTA), so it is necessary to control their presence in food. The identification of ATA in wine is effectively carried out using the molecular beacon method [63].
Another indicator of the quality of predominantly red wine is taste astringency [82]. The unpleasant sour taste and smell of acetic acid indicate the growth of acetic acid bacteria of the genus Acetobacter in the drink [83]. Even trace concentrations of such microorganisms cause wine spoilage. A real-time loop isothermal amplification (qLAMP) platform for the 16S rRNA gene has been invented, capable of effectively detecting Acetobacter aceti in red wine in a short time [84]. In addition, the identification of acetic acid bacteria in red wine by real-time PCR was demonstrated, thanks to the successful selection of primers for the 16S rRNA gene [85,86]. The main pollutants most frequently detected in wine drinks are shown in Figure 2B.

4. Microorganisms in Fruit Juices

The main source of vitamins, minerals, and antioxidants are fruits and their processed products, for example, juices [87]. Fruit juices, with their high biologically active potential and valuable dietary fibers, are included in proper human nutrition [88,89]. The short shelf life increases the risk of contamination of such a product by pathogenic microorganisms. Special attention is paid to freshly squeezed juices, since they do not undergo a pasteurization process and are infected with microbial pathogens more often than others [90,91]. The main pathogenic bacterial species polluting fruit juices include Escherichia coli, Listeria monocytogenes, Vibrio cholerae, Salmonella typhi, and Staphylococcus aureus, as well as members of the genera Shigella, Pseudomonas, and Alicyclobacillus (Figure 2C) [92,93]. The sources of juice contamination can be the fruits themselves, as well as the conditions of their production, transportation, and storage [94]. The pH value of pasteurized products is acidic, which is a barrier to the growth and reproduction of microorganisms [95]. However, in the case of thermoacidophiles, whose prominent representatives are the genus Alicyclobacillus, a low pH does not interfere with their vital activity. This is possible due to the ability of bacteria to sporulate. The violation of storage conditions, for example, damage to packaging, induces the growth of thermoacidophiles and leads to the spoilage of fruit juices [96,97].
Representatives of the genus Alicyclobacillus form phenolic impurities, including guaiacol, which cause a specific “medicinal” taste and odor in the juice [98,99,100]. Their vital functions and the ability to reproduce in fruit juices pose a problem for producers in the food industry. This contributed to the development of various approaches to the identification of Alicyclobacillus based on molecular methods [101,102]. For example, A. acidoterrestris from acidic juice was identified using RAPD PCR for 6 h [103]. Unique species patterns of the 16S rRNA gene were found in all bacterial isolates of A. acidoterrestris, different from the bands on the electrophoregram for A. acidocaldarius and A. hesperidum [104]. The PCR method with primers and TaqMan probes aimed at the gene encoding 16S rRNA has shown its effectiveness in identifying one of the main contaminants of juices, Alicyclobacillus spp. [105]. The RFLP analysis of 16S rRNA was used to characterize Alicyclobacillus strains from concentrated apple and orange juices [104,106,107]. Based on the selection of the target sequence of 16S ribosomal DNA, PCR technology was developed to identify the species A. acidoterrestris, A. acidiphilus, A. cycloheptanicus, and A. herbarius with a sensitivity of 2.6 × 102 CFU/mL, 74 × 102 CFU/mL, 2.8 × 102 CFU/mL, and 3.1 × 102 CFU/mL, respectively [100]. The RFLP analysis of the 16S rRNA and rpoB genes, as well as the vdc region, can be successfully used to identify and study the intraspecific heterogeneity of Alicyclobacillus species. The Hin6I enzyme for 16S rRNA provides the formation of special restriction patterns that allow for the species-specific differentiation of Alicyclobacillus. The analysis of the rpoB and vdc genes also revealed two main types among A. acidoterrestris isolates, one of which is similar to the reference strain A. acidoterrestris DSM 2498, and the other is similar to the reference strain A. acidoterrestris ATCC 49025 [107]. The vdcC gene is present in all strains of Alicyclobacillus that produce guaiacol, but is absent in strains that do not produce guaiacol, with the exception of A. fastidiosus DSM 17978. Based on the sequence of the vdcC gene, a pair of primers specific to A. acidoterrestris was constructed and real-time PCR was performed using SYBR Green I to directly quantify A. acidoterrestris in apple juice. A developed real-time PCR system was used to detect A. acidoterrestris in 36 artificially infected apple juice samples [108]. Effective real-time PCR analysis has been demonstrated for the identification of Alicyclobacillus bacteria in kiwi juice. In total, 86 samples were examined; 69 of them were taken on production lines, and 17 were bought in a supermarket in China. The control of the specificity of amplification reactions using SYBR Green I was carried out by analyzing the melting curve. A melting point of 80.5 °C was observed for all Alicyclobacillus species, with average Ct values of 26.0 ± 1.0 [109].
Fruit juices were tested for the most frequently polluting bacterial pathogens to control and ensure the safety of production using multiplex PCR. Conditions were optimized and PMAxx technologies were developed to eliminate dead bacterial cells. This analysis allowed the identification of the following contaminants: Escherichia coli, Staphylococcus aureus, Shigella, Pseudomonas aeruginosa, and Klebsiella pneumoniae. In a study conducted by Tiantian Huang et al., fruit juice samples were tested for the most frequent bacterial pathogen-pollutants, namely, Escherichia coli, Staphylococcus aureus, Shigella, Pseudomonas aeruginosa, and Klebsiella pneumoniae. The light-induced PMAxx technologies were developed with optimized treatment conditions to eliminate dead bacterial cells, followed by multiplex PCR. According to the results, the applied technique is an effective method for the simultaneous detection of living pathogenic bacteria in fruit juice samples [110]. Fruit juices and nectars are the optimal environment not only for the growth of bacteria but also yeast. This explains the reason for the frequent contamination of juices with fungi. PCR-RFLP methods with ITS primers for the 5.8S rRNA subunit and high-throughput sequencing have revealed taxa such as Candida, Lodderomyces, Wickerhamomyces, Yarrowia, Zygosaccharomyces, Zygoascus, Cryptococcus, Filobasidium, Rhodotorula/Cystobasidium, and Trichosporon, which polluted production inventory and equipment, as well as the fungi Zygosaccharomyces bailii, Z. bisporus, Zygoascus hellenicus, and Saccharomyces cerevisiae, which directly contaminated fruit juices. Widespread industrial distribution is generally characteristic of Candida intermedia, C. parapsilosis, and Lodderomyces elongisporus [111]. Real-time PCR with specific primers for the citrate synthase gene made it possible to establish the species of the fungus Candida krusei, which causes the spoilage of juices. Also, Zygosaccharomyces bailii, Z. rouxii, Rhodotorula glutinis, and Saccharomyces cerevisiae were identified in apple juice based on the values of melting curves by PCR with primers to 5.8S of the rRNA and ITS2 [112].
In Brazil, sugarcane juice is a popular dietary supplement and is actively consumed by the population as an independent drink [113]. In addition, sugarcane juice is considered the most popular freshly squeezed juice in Egypt. Indians and Pakistanis share the same habit as Egyptians regarding chewing raw sugarcane and consuming its juice [114,115]. Its inclusion in the diet often provokes the development of Chagas disease due to Trypanosoma cruzi. The effectiveness of the real-time PCR method with the developed specific pair of primers Cruzi32/Cruzi148 for the identification of the parasite in acai pulp and sugarcane juice was shown [116].

5. Microorganisms in Dairy Beverages

Dairy beverages are represented by a wide range of products: from milk and kefir to drinking yoghurts [117]. They contain a large number of elements necessary for the proper growth, development, and functioning of organisms, such as fatty acids, vitamins, micro-, and macro-elements [118]. A review of studies on milk microflora clearly shows that the most common genera of lactic acid bacteria in milk are Lactococcus, Lactobacillus, Leuconostoc, Streptococcus, and Enterococcus. There are also psychrotrophic populations that became established during refrigerated storage, such as Pseudomonas and Acinetobacter spp. Other genera distinct from lactic acid bacteria, as well as various yeasts and molds, are also found in milk (Figure 2D) [119]. However, milk can often contain various microbiological contaminations (Campylobacter spp., Salmonella spp., Brucella melitensis, and Mycobacterium bovis) [120]. Such contamination can seriously threaten health, which is why molecular methods for detecting pathogens in dairy drinks are needed [121]. For example, methods based on nucleic acid amplification (NAA), including polymerase chain reaction (PCR), loop isothermal amplification (LAMP), recombinase polymerase amplification (RPA), rotating circle amplification (RCA), enzyme-free amplification, and others, are widely used to detect foodborne pathogens in milk [122].
LAMP was the first to detect the important environmental pathogen Streptococcus uberis in raw milk. To do this, bacteria were grown on sheep blood heart agar plates for 18 h at 37 °C under aerobic conditions. For DNA extraction, bacteria were collected by scraping the surface of the plate [123]. Three genes were selected as targets for the PCR detection of Streptococcus uberis: sodA, pore, and cpn60 [124,125].
About 300 suspected isolates of Staphylococcus aureus have been confirmed using MALDI-TOF MS and real-time PCR. In addition, the pathogen was detected in several swabs from a bucket of milk, as well as in swabs from the nose and hands of milkers [126]. A multiplex PCR method has also been developed for the detection of Staphylococcus aureus, Streptococcus agalactiae, and Escherichia coli using species-specific primers [127] aimed at species-specific DNA sites encoding 16S and 23S rRNA [128,129,130], as well as encoding the sip (surface immunogenic protein) sequence-specific (SSS) gene for GBS (Lancefield Group B Streptococcus). Checking the effectiveness of the primers showed that the SAU1 and SAU2, SAGA1 and SAGA2 kits, and Ecol1 and Ecol2 primers detected at least 8000 bacteria.mL−1 (Staphylococcus aureus), 3000 bacteria.mL−1 (Streptococcus agalactiae), and 3000 bacteria.mL−1 (E. coli) [130]. Standardized multiplex PCR showed good accuracy of detection of Staphylococcus aureus, Streptococcus agalactiae, and Escherichia coli in goat’s milk [127].
Magnetic beads, which are coated with monoclonal antibodies serve as another molecular technique to detect foodborne pathogens (such as Escherichia coli) in dairy products [131]. Also of scientific interest is the method of detecting Salmonella using magnetic capture probes by modifying oligonucleotides complementary to sequences on the surface of magnetic nanoparticles with an amino-modified silica coating. The sensitivity of detection was 104 CFU/mL, which could be increased to 10 CFU/mL after a 12 h enrichment step. Magnetic capture probes were used to separate invA mRNA, with a novel step of placing complexes of magnetic capture probes and invA mRNA in an RT-qPCR mixture without any denaturation, and purification steps to detect Salmonella in milk [132].
The molecular method of detecting Cronobacter sakazakii in raw milk is also interesting. The method is a combination of quantitative LAMP and propidium bromide (PMA-QLAMP). The gyrB gene is targeted for the development of primers. The DNA of six out of twenty-four strains of C. sakazakii was amplified using PMA-QLAMP. The ability of PMA-QLAMP to quantitatively detect live C. sakazakii in a 10% solution of dry infant formula (PIF) was limited to 4.3 × 102 CFU/mL concentrations and above. Pasteurizing raw milk containing 106 CFU/mL of viable C. sakazakii resulted in the maximum VBNC ratio of C. sakazakii. The PMA-QLAMP detection revealed that even though around 13% of the 60 samples were positive for C. sakazakii viability, the titers of C. sakazakii in these positive samples were low, and none of them entered the VBNC state during pasteurization. PMA-QLAMP has demonstrated potential as a specific and reliable method for detecting VBNC-C. sakazakii in pasteurized raw milk, thereby providing an early warning system indicating the potential contamination of PIF [133]. GyrB is a conserved gene that is a molecular marker of bacterial phylogenetic analysis [134].
Table 1 summarizes information from the available literature and publications showing primer sequences used to identify pathogenic microorganisms in beer, wine, juice, and milk beverages.

6. Conclusions

The unique composition of drinks, rich in micro- and macronutrients, is a good environment for the growth and development of various microorganisms that cause the spoilage of products and even the poisoning of consumers. The most common pollutants are Staphylococcus aureus, Bacillus cereus, Salmonella sp., Campylobacter sp., Shigella sp., and Vibrio parahaemolyticus. Nonetheless, several studies using molecular and PCR-based technique have identified other species of microorganisms in beverages. In this regard, Rosalinda Urso et al. investigated yeast biodiversity during sweet wine production by performing PCR-DGGE and detected species of Candida zemplinina and Hanseniaspora uvarum [135]. Moreover, Fatemeh Zendeboodi et al. identified the dominant spoilage fungal species in non-alcoholic beer manufacturing by performing PCR. These species include Saccharomyces, Pichia, Rhodotorula, Alternaria, Hansenia, Wickerhamomyces, and Cladosporium [136]. The study conducted by Phattaraporn Sarikkha et al. detected bacterial and yeast species in sugary kefir, which is an acid-alcoholic fermented beverage, using PCR-DGGE. This research identified the species Gluconobacter japonicus, Bacillus cereus, Lactobacillus rhamnosus, Saccharomyces cerevisiae, and Candida ethanolica [137]. Sources of pollution can be both production equipment and premises, as well as violations of the packaging, storage conditions, and transportation of products.
The most contaminated drink turned out to be fruit juice. A large number of independent studies have made it possible to identify about 23 types of juice microbiological contaminants. Milk turned out to be the least contaminated in terms of the detected types of bacteria. An analysis of the data from the literature on the molecular detection of wine and beer microbiological contaminants revealed a relatively equal number of pathogenic species for both drinks. One of the main tools for quality control of beverages at all stages of their production is the polymerase chain reaction method. A wide range of variations of this technology makes it possible to identify microbiological pollutants in alcoholic and non-alcoholic beverages. The high specificity of such methods as PCR-RFLP, RAPD-PCR, qPCR, End-point PCR, LAMP, and the molecular beacon method allows for fast and reliable quality control in production. Sequencing allows evaluating the microbial diversity of all the beverages we study. PCR-RFLP, RAPD-PCR, and PCR allowed the identification of bacterial contaminants in fruit juices, qPCR, LAMP, and the molecular beacon method in wine, LAMP and multiplex PCR in milk, and End-point PCR and Rep-PCR in beer.
The results that we obtained were formulated in relation to the analysis of publicly available scientific publications and are incomplete due to the small number of modern studies in this field. Due to the persistent concern about contamination and food safety, molecular genetic methods can become effective and promising methods for solving these problems. However, it is crucial to consider all the challenges encountered when implementing these molecular genetic techniques in beverage production practices. First and foremost is the concern regarding the cost-effectiveness of PCR methods, since PCR-based techniques require advanced equipment, specific machinery, and high-quality reagents. Additionally, procedures such as the regular calibration of PCR instruments as well as the maintenance of equipment, in order to keep the instruments reliable for a high degree of data accuracy over time, along with the training of expert technicians, increase overall expenses. Another technical limitation of the PCR-based methodology is detection sensitivity. Due to the presence of some specific chemical components acting as PCR inhibitors in beverages, and also the low concentration of microorganisms in these samples, attaining optimal sensitivity, which is critical for clinical applications, is more challenging. Moreover, the adaptability of these PCR-based methods with different types of beverages should be carefully considered, as they have diverse chemical compositions, highlighting the necessity of implementing customized PCR protocols. It is noteworthy to mention that developing sensitive and high-throughput molecular approaches for screening large-scale beverage production is a significant challenge.
Many of the methods developed to detect microbiological contaminants in beverages were developed 10–20 years ago. It is necessary to develop new modern modifications of PCR and isothermal amplification for the detection of microorganisms in beverages.

Author Contributions

Conceptualization, M.S.; methodology, E.N. and M.G.; investigation, P.M., E.N. and M.G.; resources, E.N. and M.G.; data curation, M.S.; writing—original draft, P.M., E.N. and M.G.; writing—review and editing S.K. and M.S.; supervision, M.S.; project administration, M.S. All authors have read and agreed to the published version of the manuscript.

Funding

This work was supported by the Ministry of Science and Higher Education of the Russian Federation in the framework of the national project “Science and Universities” (project FZGW-2024-0003).

Conflicts of Interest

The authors declare no conflicts of interest.

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Beverages 10 00046 g001
Figure 1. Molecular methods for the identification of microorganisms in beverages.
Figure 1. Molecular methods for the identification of microorganisms in beverages.
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Beverages 10 00046 g002
Figure 2. The main microbiological pollutants of beverages. (A) beer, (B) wine, (C) fruit juices, (D) milk beverages.
Figure 2. The main microbiological pollutants of beverages. (A) beer, (B) wine, (C) fruit juices, (D) milk beverages.
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Table 1. Detection methods and primers for the identification of microorganisms in beverages.
Table 1. Detection methods and primers for the identification of microorganisms in beverages.
№ObjectTargetMethodPrimerSubsequence (5′-3′)Literature
1BeerLactobacillusRep-PCRREP1R-IIIIICGICGICATCIGGC[46,50]
REP2-IIIICGNCGNCATCNGGC
BOXA1RCTACGGCAAGGCGACGCTGACG
PRIMER(GTG)5 (5GTGGTGGTGGTGGTG3)
2PectinatusqPCRFGCTTTTAGCTGTCGCTTGGA[47]
RTGCATCTCTGCATACGTCAA
3BacteriaqPCR27FGAGAGTTTGATCCTGGCTCAG[53]
1495rCTACGGCTACCTTGTTACGA
4YeastsqPCRITS1TCCGTAGGTGAACCTGCGG[52,53]
ITS4TCCTCCGCTTATTGATATGC
5Resistance gene HorA/HorCqPCRLbHC-1ATCCGGCGGTGGCAAATCA[56]
LbHC-2AATCGCCAATCGTTGGCG
Lactobacillus brevisqPCRLBP2CTGATTTCAACAATGAAGC
UNP1CCGTCAATTCCTTTGAGTTT
Pectinatus cerevisiiphilusqPCR16C-FCGTATGCAGAGATGCATATT
IC-RCACTCTTACAAGTATCTAC
Pediococcus damnosus/Pediococcus inopinatusqPCRPIDF1TGTGAGAGTAACTGCTCATG
PIDR8ACGCCTAATCTCTTTGGTTA
Megasphaera cerevisiaeqPCRMc-f4ACCGAATACGATCTAAAG
Mc-r4TTAAGACCGACTTACCGA
6ClostridiaEnd-point PCRAn-0279fACGATCAGTAGCCGGT[59]
An-0603rAGCCCCGCACTTTTAAG
7Megasphaera cerevisiaeqPCRFCACTGAATAGTCTATCGC[58]
RAAGACCGACTTACCGAAC
1WinePenicillium expansumqPCRPE F ATCGGCTGCGGATTGAAAG[64]
PE RAGTCACGGGTTTGGAGGGA
2Acetobacter acetiqLAMPF3AGGTGGGGATGACGTCAAG[84]
B3CGGGAACGTATTCACCGC
FIPCTAGCTTCCCACTGTCACCG
TCCTCATGGCCCTTATGTC
BIPAACCGTCTCAGTTCGGATTGCATCCGCGATTACTAGCGATTC
LFAGCACGTGTGTAGCCCA
LBCTCTGCAACTCGAGTGCATG
FCGGAATGACTGGGCGTAAG
RCAGTAATGAGCCAGGTTGCC
PROBE6FAMCGGGCTTAACCTGGGAGCTGCATTBHQ1
3Acetic acid bacteriaqPCRAAB FTGAGAGGATGATCAGCCACACT[85]
AAB RTCACACACGCGGCATTG
4YeastsqPCRYEAST FGAGTCGAGTTGTTTGGGAATGC[74]
YEAST RTCTCTTTCCAAAGTTCTTTTCATCTTT
5Brettanomyces/Dekkera spp.qPCRDBRUX FGGATGGGTGCACCTGGTTTACAC[76,78,79]
DBRUXRGAAGGGCCACATTCACGAACCCCG
6Brettanomyces bruxellensisqPCRBRETT 1 CGAAGAAGTTGAACGGCCGCATTTG[77]
BRETT 2TCTTCGATATGCCGTCCAAAAGCTC
RAD 1 GTTCACACAATCCCCTCGATCAAC[78]
RAD 2TGCCAACTGCCGAATGTTCTC
ACT 1 TGTCAGAGACATCAAGGAGAAGCT[75]
ACT 2CGTCTGCATTTCCTGGTCAA
7Zygosaccharomyces bailiiqPCRZB F1 CATGGTGTTTTGCGCC[81]
ZB R1CGTCCGCCACGAAGTGGTAG A
8Ochratoxin AMolecular Beacon MethodAPTABEACON6FAMCGCGCTGGATCGGGTGTGGGTGGCGTAAAGGGAGCATCGGACACAGCGCGBHQ1[63]
1Fruit juicesAlicyclobacillus acidoterrestrisPCRA1-92-3 FTCGCAACCTGCTTCTCCA[100]
A1-92-3 RTGGTGGACGGGATTGTTT
Alicyclobacillus acidiphilusPCRA2-16S-1 FATGCAAGTCGAGCGAAC
A2-16S-1 RGCAACTTTCCTCAACGG
Alicyclobacillus cycloheptanicusPCRA3-16S-3 FTGCAAATGCACCGCAGAT
A3-16S-3 RGGCTTTCCACTCCCCTTG
Alicyclobacillus herbariusPCRA4-5472 FTGAGTCGCTTCTTCGTTCTT
A4-5472 RCTACGGGATGACGGAAGC
2Alicyclobacillus acidoterrestrisRAPDBa-lOAACGCGCAAC[103]
F -61CCTGTGATGGGC
F-64GCCGCGCCAGTA
3Alicyclobacillus acidoterrestrisPCR-RFLP
Restriction endonuclease: HhaI		P1GCGGCGTGCCTAATACATGC[104]
P4ATCTACGCATTTCACCGCTAC
4AlicyclobacillusqPCRCC16S-FCGTAGTTCGGATTGCAGGC[105]
CC16S-RGTGTTGCCGACTCTCGTG
CC16S-ProbeQUASAR670CGGAATTGCTAGTAATCGCBHQ-2
5AlicyclobacillusPCR–RFLP
Restriction endonucleases: BsuR I, Hinf I, Msp I, Rsa I		P1CGGGATCCAGAGTTTGATCCTGCGTCAGAACGAACGCT[106]
P2CGGGATCCTAGGGCTACCTTGTTACGACTTCACCCC
6Alicyclobacillus acidoterrestrisPCR-RFLP
Restriction endonuclease: HhaI		P1GCGGCGTGCCTAATACATGC[104]
P4ATCTACGCATTTCACCGCTAC
7AlicyclobacillusPCR-RFLP
Restriction endonucleases: BsuRI, Hin6I, HphI		vdc frCTGTTGGCTCAATGGCGGCTGAGCGAT[107]
vdc revTTATCAGCGGTTTATCCGCGGTGGAACAGTC
vdc1 frAACGACGCAGGTGTGGAAAC
vdc1 revAGCGTGGGCAAGTTGTCATGTG
vdc KTTGGCAACGGAGAAGTGGGAG
and vdc SAATCACGCGCTGATGATGGG
Bur 5GCCGACGTGATGCTCAARGAGCGCA
Bur 6GTSGCRTCGAGAATCATCTTGTG
Gru3CGYGACGTDCACTAYTCBCACTA
Gru4GCCCANACYTCCATCTCRCCRAA
Gru5CGCGACGTACACTATTCGCACTA
Gru6GCCCAAACCTCCATCTCACCAAA
8Alicyclobacillus acidoterrestrisqPCRvdcCF1TAYGAAATGGCMGGTGC[108]
vdcCR1GGAAGGTTGAAYGGATC
9AlicyclobacillusqPCRFATGCGTAGATATGTGGAGGA[109]
RCAGGCGGAGTGCTTATTG
10YeastPCR-RFLP
Restriction endonucleases: CfoI, HaeIII, HinfI		ITS1TCCGTAGGTGAACCTGCGG[111]
ITS4TCCTCCGCTTATTGATATGC
11Zygosaccharomyces bailii, Zygosaccharomyces rouxii, Candida krusei, Rhodotorula glutinis, Saccharomyces cerevisiaeqPCRITS3GCATCGATGAAGAACGCAGC[112]
ITS4TCCTCCGCTTATTGATATGC
CS FwdGCATATGGTGGTTATGAGAGG
CS RevAGCAGAAACATTACCACCTTC
12Trypanosoma cruziqPCR32FTTTGGGAGGGGCGTTCA[116]
148RATATTACACCAACCCCAATCGAA
probe71FAMCATCTCACCCGTACATT3NFQ
1 Streptococcus uberisLAMPSu sodA F3TGGCGTTATTATCTGATGTGT[124]
Su sodA B3AGAYCCAAAACGTCCCGT
Su sodA FIPATGGTTAAGATGTCCGCCTCCCATCAATTCCAGAAGATATTCGT
Su sodA BIPTTCACCTGAGAAAACAGAAATCACTTCTTTAAATGCATCAAAAGAACC
Su sodA BloopCGGAAGTAGCTTCTGCTATTGAT
Su sodA FIPDIG-ATGGTTAAGATGTCCGCCTCCCATCAATTCCAGAAGATATTCGT
Milk beverages Su sodA BIPBiotin-TTCACCTGAGAAAACAGAAATCACTTCTTTAAATGCATCAAAAGAACC
2Streptococcus aureusmultiplex PCRSau 327/SAU1GGACGACATTAGACGAATCA[130]
Sau 1645/SAU2CGGGCACCTATTTTCTATCT[131]
STAUR4ACGGAGTTACAAAGGACGAC[129]
STAUR6AGCTCAGCCTTAACGAGTAC
3Streptococcus agalactiaeSag 432/SAGA1CGTTGGTAGGAGTGGAAAAT[130]
Sag 1018/SAGA2CTGCTCCGAAGAGAAAGCCT[131]
SIP3/SIP3TGAAAATGCAGGGCTCCAACCTCA
SIP4/SIP4GATCTGGCATTGCATTCCAAGTAT
4Escherichia coliEco 2083/Ecoli1GCTTGACACTGAACATTGAG[130]
Eco 2745/Ecoli2GCACTTATCTCTTCCGCATT[131]
FOPCCGTTAAAGTGCC
BOPGCTTTACGTGCCGCC
5Cronobacter sakazakiiQLAMPFIPTGCTGCTCAACCGCCGATTTCTCCCCCCCCCCCCACCACCAAAGACA[133]
BIPGATGAACGAGCTGCTGGCCGTCGATAATTTTGCCGA
FLPCACCTCGGAGGAGACC
BLPCTGCTGGAGAACCC
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Nesterova, E.; Morozova, P.; Gladkikh, M.; Kazemzadeh, S.; Syromyatnikov, M. Molecular Methods for Detecting Microorganisms in Beverages. Beverages 2024, 10, 46. https://doi.org/10.3390/beverages10020046

AMA Style

Nesterova E, Morozova P, Gladkikh M, Kazemzadeh S, Syromyatnikov M. Molecular Methods for Detecting Microorganisms in Beverages. Beverages. 2024; 10(2):46. https://doi.org/10.3390/beverages10020046

Chicago/Turabian Style

Nesterova, Ekaterina, Polina Morozova, Mariya Gladkikh, Shima Kazemzadeh, and Mikhail Syromyatnikov. 2024. "Molecular Methods for Detecting Microorganisms in Beverages" Beverages 10, no. 2: 46. https://doi.org/10.3390/beverages10020046

APA Style

Nesterova, E., Morozova, P., Gladkikh, M., Kazemzadeh, S., & Syromyatnikov, M. (2024). Molecular Methods for Detecting Microorganisms in Beverages. Beverages, 10(2), 46. https://doi.org/10.3390/beverages10020046

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