A comparison of Listeria monocytogenes growth monitoring in ground pork samples by real-time polymerase chain reaction to conventional agar and most probable number methods

Abstract

To construct a predictive growth model of Listeria monocytogenes in ground pork, growth data were collected by real-time PCR and Listeria selective agar according to Ottaviani and Agosti (ALOA). Most probable number (MPN) was used to estimate viable numbers of L. monocytogenes at the beginning, middle, and end of the incubation period. Growth curves obtained from this study were fitted to the Baranyi and Roberts model to obtain growth parameters. Furthermore, the theoretical minimum temperature of growth was estimated by Ratkowsky's model. L. monocytogenes growth rates estimated from ALOA data were lower than those estimated by real-time PCR. Moreover, cell concentrations at all incubation temperatures were underestimated and lag phase duration at refrigeration temperature (4 °C) was overestimated by ALOA. However, the estimation from MPN more closely resembled the real-time PCR quantification results. Thus, the direct plate count tends to result in fail-dangerous prediction for bacterial risk from the model.

Introduction

Ground pork has been reported to have a higher L. monocytogenes prevalence than meat cuts because the large exposed surface area facilitates spoilage (Kanuganti et al., 2002; Okutani et al., 2004; Limbo et al., 2010). Although ground pork is stored at chilled temperatures inside fresh meat displays at retail stores, the growth of L. monocytogenes is difficult to control. This is because L. monocytogenes is a psychrotrophic bacterium that can thrive at low-temperature conditions such as refrigerated storage (Zuliani et al., 2007; Jami et al., 2014; Szczawiński et al., 2017; Sosnowski et al., 2019). Therefore, a study that estimates the effect of storage temperature on the growth of L. monocytogenes in ground pork in the presence of naturally occurring microbial background flora is necessary to estimate the risk of listeriosis.

The concept of predictive microbiology has been proposed using various mathematical models to precisely estimate microbial risk and to determine the shelf-life of foods (Koutsoumanis et al., 2006). To date, numerous bacterial growth prediction studies have been proposed involving data generated using selective agar media (Bovill et al., 2001; Culliney and Schmalenberger, 2020; Gérard et al., 2020; Jia et al., 2020). Selective agar media such as Listeria selective agar according to Ottaviani and Agosti (ALOA) are generally used for the enumeration of L. monocytogenes from food and have been established as a standard according to the revised EN ISO 11290 - Part 2 (Rollier et al., 2019). However, interference by high levels of injured cells remains an obstacle for the direct plating method (Jeyaletchumi et al., 2010; Noriega et al., 2013). If cells fail to develop colonies on selective agar, erroneous predictions could be made from the underestimation of true bacterial numbers, leading to potentially hazardous conditions from the presence of L. monocytogenes (Lavieri et al., 2014).

As for the enumeration of L. monocytogenes in food, a combination of agar plating and the most probable number (MPN) method is recommended in the L. monocytogenes chapter of the Bacteriological Analytical Manual (BAM) by the U.S. Food and Drug Administration (FDA) (Hitchins et al., 2016). The MPN method is one of the primary methods for the enumeration of L. monocytogenes and other foodborne pathogens, and is supported by enrichment schemes that can potentially suppress the level of background flora (Capita and Alonso-Calleja, 2003; Chen et al., 2017). However, the MPN method is laborious and requires the use of a large amount of media (Sutton, 2010; Kashyap, 2011; Erkmen and Bozoglu, 2016). Furthermore, the MPN method cannot be applied for a large number of samples simultaneously, making it difficult to utilize the data for constructing a growth curve under various conditions (Kawasaki et al., 2014).

Real-time PCR has been recognized as a powerful tool for quantitative bacterial detection from food materials that is highly sensitive and specific (Kimura et al., 2001; Rodríguez-Lázaro et al., 2004; Mafu et al., 2009; Liu et al., 2019). Several studies have compared the application of real-time PCR to plate counting, optical density, and other methods (Reichert-Schwillinsky et al., 2009; Macé et al., 2013; Ilha et al., 2016; Ricchi et al., 2017; Lopes and Maciel, 2019). However, no study has utilized real-time PCR quantification to generate bacterial growth data from food materials for the construction of growth prediction models in comparison with other methods. In recent years, we successfully developed a real-time PCR quantification technique as an alternative method for bacterial enumeration with high throughput results to construct bacterial growth prediction models (Hosotani et al., 2018; Noviyanti et al., 2018; Noviyanti et al., 2020). In the present study, we aimed to investigate the use of the real-time PCR quantification method to collect L. monocytogenes growth data from ground pork samples incubated at six different temperatures for the construction of mathematical models. Furthermore, we compared the results to direct plating counts by the ALOA and MPN methods, and an underestimation phenomenon resulting from the selective agar medium was then evaluated and discussed.

Materials and Methods

Culture and inoculum preparation L. monocytogenes    ATCC 49594 (Serotype 4b, Scott A) was used in this study. To prepare the inoculum, the strain was cultured in 10 mL tryptic soy broth (TSB; BBL, Becton Dickinson and Company, USA) at 35 °C overnight. The early stationary phase inoculum was obtained after subculture in fresh TSB, which was incubated at 35 °C inside an automatic optical density (OD, abs 660 nm) measurement instrument (BioPlotter; Toyo Sokki Co. Ltd., Japan) and reached an OD of 0.80. The enriched culture was serially diluted (10-fold) in 9 mL phosphate-buffered saline (PBS; GSI Creos, Huko FS Co. Ltd., Japan) to obtain a suspension containing approximately 10⁷ CFU/mL for sample inoculation.

Confirmation of microbial background flora and detection of L. monocytogenes in ground pork samples    Fresh ground pork samples (Kirkland, 80% lean) purchased from the wholesale market were used for the study. A 25-g sample of ground pork was used to confirm the initial load of microbial background flora in the sample. PBS (225 mL) was added using a gravimetric dilutor (Smart Dilutor; IUL, S.A., Spain) and the mixture was homogenized for 60 s using a stomacher (Pro Media, SH-IIM; Elmex Ltd., Tokyo, Japan). A 50-µL aliquot from the appropriate 10-fold diluted sample in PBS was plated onto tryptic soy agar (TSA; Difco, Becton, Dickinson and Company, USA) using a spiral plater (Eddy Jet 2; IUL, S.A., Spain) and incubated at 35 °C for 24 h prior to enumeration. For the detection of L. monocytogenes, a 25-g sample was mixed with 225 mL sterilized Half-Fraser broth (FRASER, Merck, Germany) and homogenized for 60 s using a stomacher. The mixtures were then incubated for 24–48 ± 2 h at 35 °C. One loopful of the selective enrichment was then streaked onto agar for Listeria according to Ottaviani and Agosti (ALOA) containing Listeria selective supplement (both Merck, Darmstadt, Germany) to examine Listeria colonies after incubation at 35 °C for 24–48 h. Typical colonies were confirmed by real-time PCR targeting the hlyA gene, which is specific to L. monocytogenes.

Sample preparation and bacterial inoculation    Ground pork samples were aseptically weighed and divided into portions (450 g, in triplicate) for each incubation temperature, then transferred into vacuum bags. Each sample portion was immediately inoculated with 2 mL of the diluted L. monocytogenes culture to achieve an initial inoculum level of approximately 10⁵ CFU/g. Each sealed bag was aseptically hand-mixed for approximately 5 min to obtain a homogeneous distribution of L. monocytogenes in the sample. After mixing, 25 g sample portions were weighed and uniformly shaped into small patties, then transferred into stomacher filter bags (190 mm × 280 mm, GSI Creos) and incubated at 4, 8, 12, 16, 20, and 35 °C.

Microbiological sampling procedure    The ground pork was sampled after inoculation and after every 2 h of incubation at 35 °C, 4 h of incubation at 20 °C and 16 °C, 12 h of incubation at 12 °C, 24 h of incubation at 8 °C, and 72 h of incubation at 4 °C in 1, 4, 6, 12, 24, and 63 day cycles, respectively, to allow a sufficient amount of time for L. monocytogenes to grow. A 225-mL portion of PBS was added into the filter bag containing the 25-g sample. The triplicate samples were then thoroughly homogenized for 60 s by a stomacher. Three 1-mL aliquots of samples from each storage temperature were immediately stored at −20 °C until all samples were collected for DNA extraction and analyzed by real-time PCR.

Enumeration of L. monocytogenes and total viable cell counts by direct plating method    A 50-µL aliquot from the appropriate 10-fold samples was surface plated onto ALOA using a spiral plater to determine the viable cell counts of L. monocytogenes at the designated time intervals. Plated samples were incubated at 37 °C for 24–48 h and blue-green colonies with an opaque white halo were counted. At the same intervals, the enumeration of total viable cell count was performed by surface plating 50 µL of the appropriately diluted sample onto TSA using a spiral plater, followed by incubation at 35 °C for 24 h.

Enumeration of L. monocytogenes by most probable number (MPN) method    The MPN method was carried out for each temperature condition at the beginning (t₀), middle (t_m), and end (t_end) of the incubation period for the enumeration of L. monocytogenes in ground pork samples. As suggested by the International Organization for Standardization (ISO) 11290 method, a 225-mL aliquot of Listeria enrichment broth with selective supplement was added to the sample bag containing the 25 g sample. The sample was then homogenized for 60 s, then serially diluted 8 times in 9 mL PBS. The diluted samples (1-mL aliquots) were then transferred into triplicate tubes containing 10 mL Fraser broth. The sample tubes were incubated at 35 °C for 24–48 h. After incubation, aliquots of the enrichment broth that darkened because of esculin hydrolysis (presumptive-positive tubes) were streaked onto ALOA and incubated at 37 °C for 24–48 h. The MPN value was determined from the number of positive tubes obtained in serial dilution, which were confirmed by the appearance of blue colonies with opaque white halo on ALOA.

Real-time PCR standard curve construction    A standard curve was constructed for each real-time PCR amplification and analysis by diluting the L. monocytogenes culture in ground pork to obtain concentrations of 10⁴, 10⁵, 10⁶, 10⁷, and 10⁸ CFU/g. The slope generated from the standard curve was used to estimate real-time PCR efficiency (e), calculated by the formula e = 10^−1/s − 1, where s is the slope (Pfaffl, 2001). This standard curve was used to obtain the quantitative value of L. monocytogenes cell concentration for each DNA template and finally converted to bacterial cell concentration per gram (CFU/g). The robustness of the real-time PCR assay was examined based on the precision and performance of standard curves by generating 3 replicates of the standard curve points at each concentration.

L. monocytogenes DNA extraction and real-time PCR conditions    The DNA template for the real-time PCR assay was obtained from individual 25-µL aliquots of the ground pork samples extracted according to the L. monocytogenes DNA extraction method of Noviyanti et al. (2020). The L. monocytogenes DNA copy number from each sample was determined by real-time PCR using primers targeting the hlyA gene fragment, and the oligonucleotide design was used to amplify a 64-bp fragment from the L. monocytogenes hlyA gene (Rodríguez-Lázaro et al., 2004). The real-time PCR sample (25 µL) consisted of 2.5 µL DNA template and 22.5 µL real-time PCR master mix solution containing TaqMan gene expression master mix (Applied Biosystems, USA), 200 nM of each primer (fwd-primer: 5′-CATGGCACCACCA GCATCT-3′ and rev-primer: 5′-ATCCGCGTGTTTCTTT TCGA-3′), and 100 nM probe (internal probe: 5′-FAM-CGCC TGCAAGTCCTAAGACGCCA-BHQ1-3′). The QuantStudio 5 real-time PCR system (Applied Biosystems) was used according to the following program: 2 min at 50 °C, initial denaturation for 10 min at 95 °C, 50 cycles each of a denaturation step for 15 s at 95 °C, followed by annealing and extension for 1 min at 63 °C.

Primary and secondary models for growth kinetics of L. monocytogenes    L. monocytogenes cell concentrations (log CFU/g) in ground pork samples, including total viable cell counts, obtained from the six tested incubation temperatures were modeled as a function of time using a primary Baranyi and Roberts model (Baranyi and Roberts, 1994). The model was used to estimate the kinetic parameters such as lag phase duration (λ, h), maximum population density (N_max), and maximum specific growth rate (µ_max, 1/h). All obtained data were fitted using DMFit 2.1 for Excel™, kindly provided by J. Baranyi (Institute of Food Research, Norwich, UK). The effect of temperature on the parameters of the primary model, particularly the maximum growth rate, is described by the secondary model (Juneja et al., 2007). Ratkowsky's square root model (Ratkowsky et al., 1982) was used as the secondary model to simulate the relation between µ_max and temperature for the determination of L. monocytogenes theoretical minimum temperature of growth (T_min) in ground pork, as described in the following equation:   

where b, T, and T_min indicate the constant, temperature, and theoretical minimum temperature for growth, respectively.

Statistical analysis    Statistical analyses were performed using Microsoft Excel™ (Microsoft, Redmond, WA, USA). A two-tailed t-test analysis was selected and a P value lower than 0.05 (p < 0.05) was determined as the level of significance.

Results

Examination of natural contamination of L. monocytogenes in ground pork samples    Non-inoculated samples were tested according to the ISO 11290 method to confirm natural contamination of L. monocytogenes in ground pork samples. Several presumptive blue colonies were observed on ALOA during microbial examination of the enriched ground pork samples, which were suspected to be Listeria spp. Those presumptive blue colonies were then confirmed by real-time PCR analysis targeting the hlyA gene, which encodes listeriolysin O and is specific to L. monocytogenes. However, none of the templates contained in the suspected colonies were amplified and detected as L. monocytogenes (data not shown). The results revealed that L. monocytogenes was not present in the ground pork samples before inoculation.

Real-time PCR quantification performance    A standard curve constructed from serially diluted L. monocytogenes in ground pork samples was used to assess the sensitivity of the assay to determine the DNA concentration range. The realtime PCR efficiencies obtained from this study ranged from 91.1 to 104.5%, with R² values ranging from 0.97 to 0.99 for overall experiments. Figure 1 shows one of the obtained standard curves with real-time PCR efficiency of 104.5%, R² value of 0.99, and quantification range of 10⁴ to 10⁸ CFU/g. Demes et al. (2012) previously described that the range of realtime PCR efficiency should be within 90–110%, similar to that obtained in this study. The extraction method performed for ground pork samples was highly efficient in generating the L. monocytogenes DNA concentration for real-time PCR analysis.

Fig. 1.

Amplification profile of serial dilutions and standard curve of hlyA gene fragment generated by real-time PCR from the extracted L. monocytogenes cells in ground pork sample represented as log of genome equivalents/reaction (n = 3).

Growth of L. monocytogenes and microbial background flora in ground pork    The growth of L. monocytogenes in ground pork samples exposed to various storage temperatures is shown in Figure 2. The growth of total viable cell counts in samples was observed to be faster than for L. monocytogenes. The difference can be seen from the doubling time of total viable cell counts measured on TSA versus the doubling time of L. monocytogenes on ALOA and real-time PCR quantification data. Total viable cell counts showed that the stationary phase was achieved after 360 h at 4 °C when the cell concentration reached 9.1 log CFU/g, after 96 h at 8 °C when the cell concentration reached 9.1 log CFU/g, 48 h at 12 °C when the cell concentration reached 9.4 log CFU/g, 20 h at 16 °C when the cell concentration reached 9.4 log CFU/g, 16 h at 20 °C when the cell concentration reached 9.6 log CFU/g, and 10 h at 35 °C when the cell concentration reached 9.5 log CFU/g.

Fig. 2.

Representative growth curves of L. monocytogenes in ground pork incubated at 4, 8, 12, 16, 20, and 35 °C as quantified by real-time PCR (◆), direct plating on selective agar medium (ALOA) (▲), MPN method (○), and total viable cell count including microbial background flora enumerated by non-selective agar medium (TSA) (×) fitted to Baranyi and Roberts' model (n = 3).

Real-time PCR quantification of L. monocytogenes growth showed greater similarity to data obtained from the MPN method than that obtained from ALOA in terms of cell concentrations. The cell concentrations obtained from ALOA were lower than those from the real-time PCR and MPN methods. The observed minimum and maximum log count differences between real-time PCR and ALOA were −0.2 log CFU/g and 1.4 log CFU/g, respectively. The growth rates obtained by ALOA were also slightly lower than those calculated using real-time PCR data (Table 1). Moreover, the growth data generated by ALOA showed a longer lag time duration than real-time PCR at 4 °C. The lag phase duration was determined from ALOA and real-time PCR growth data as 216 h and 113 h, respectively. The overall standard deviation of real-time PCR quantification and ALOA was 0.13 and 0.15, and similar results were obtained from triplicate samples. The average R² value from curve fitting was 0.96 and 0.94 for real-time PCR and ALOA, respectively. The t-test method was performed with the assumption that the groups had equal variance. A significant difference (p < 0.05) in cell concentration was observed between real-time PCR quantification and enumeration using ALOA.

Table 1. Growth of L. monocytogenes in ground pork incubated at different temperatures quantified by real-time PCR and selective agar medium (ALOA).

	PCR			ALOA
Temperature (°C)	λ (h)	µmax (1/h)	Nmax (log CFU/g)	λ (h)	µmax (1/h)	Nmax (log CFU/g)
4	112.878	0.007	6.883a)	216.069	0.006	6.312a)
8	55.957	0.017	7.868	45.738	0.012	7.217
12	35.822	0.030	7.871	37.453	0.048	7.078
16	7.016	0.064	7.955	9.719	0.057	7.054
20	4.412	0.125	7.735	4.712	0.118	7.420
35	2.293	0.227	7.893	2.843	0.196	7.468

^a)  Significantly different to N_max at 8–35 °C

Estimation of L. monocytogenes theoretical minimum temperature of growth (T_min)    The effect of temperature on the µ_max value analyzed using Ratkowsky's square root model for the data obtained by real-time PCR is shown in Figure 3. The calculated value of T_min for L. monocytogenes was −2.7 °C, and the relationship between √µ_max value and incubation temperature for real-time PCR quantification was as follows:   

Fig. 3.

The relationship between µ_max value and incubation temperature of L. monocytogenes by Ratkowsky's square root model derived from real-time PCR data. The dotted line represents the fit, which was calculated by Ratkowsky's square root model as a secondary model.

The effect of temperature on the µ_max value analyzed using Ratkowsky's square root model for the data obtained using the selective agar medium is shown in Figure 4. The calculated value of T_min for L. monocytogenes was −1.6 °C, and the relationship between √µ_max value and incubation temperature for direct plating on selective agar medium (ALOA) was as follows:   

Fig. 4.

The relationship between µ_max value and incubation temperature of L. monocytogenes by Ratkowsky's square root model derived from direct plate on selective agar medium (ALOA). The dotted line represents the fit, which was calculated by Ratkowsky's square root model as the secondary model.

Discussion

L. monocytogenes is frequently isolated from pigs, which are the natural carriers of this pathogen (Gamboa-Marín et al., 2012). Serotype 4b, which is associated with crosscontamination, has been reported to be more virulent than other serotypes found as contaminants of raw meat and its products (Greenwood et al., 1991; Hayes et al., 1991; Kathariou, 2002; Okutani et al., 2004; Shen et al., 2006). This serotype is highly persistent, as it can expand its distribution spatially and temporally in foods and their industrial environments (Okutani et al., 2004). The presence of L. monocytogenes serotype 4b strains in the meat processing industry has the potential to cause listeriosis outbreaks (Thévenot et al., 2006). Therefore, we chose a serotype 4b strain and observed its growth behavior under various storage temperatures.

The hlyA gene, which encodes listeriolysin O (LLO), was chosen as a target gene for quantification by real-time PCR, since it is present in all strains of L. monocytogenes and is highly important for virulence (Cossart et al., 1989; Lukowiak et al., 2004). Many studies have reported that the hlyA gene has an excellent detection limit and can be used to accurately quantify L. monocytogenes (Kumar and Batish, 2015; Faille et al., 2020; Garrido-Maestu et al., 2020). Moreover, the hlyA gene was reported to demonstrate high PCR amplification efficiency in a real-time PCR reaction (Rodríguez-Lázaro et al., 2004; Noviyanti et al., 2020), which was also shown in this study (Fig. 1).

The obtained results demonstrated that the growth of L. monocytogenes was profoundly affected by the storage temperature. Despite a slower generation time compared to incubation at higher temperatures, L. monocytogenes was able to grow well at 4 °C, which is considered to be a severe condition for other bacteria. L. monocytogenes showed up to a 1.8 log-order increase after incubation for 14 days at 4 °C, and the number of L. monocytogenes remained constant up to day 63 of the incubation period (Fig. 2, a). Similar behavior was observed in a Norwegian fermented fish product (rakfisk) made from two different fish species. L. monocytogenes exhibited some initial growth in rakfisk made from char at 4 °C (one log-order increase) during the early incubation process and L. monocytogenes numbers thereafter remained essentially constant (Axelsson et al., 2020). This result indicated that L. monocytogenes can grow and multiply at refrigeration temperatures during the first several days of incubation, then endure prolonged refrigerated storage without additional population increases.

A lower N_max was achieved by L. monocytogenes at refrigeration temperature (4 °C), since this temperature supposedly limits the N_max that can occur. The FDA/FSIS (2001) reviewed the phenomenon where the N_max of L. monocytogenes at low temperatures is limited to 1000-fold lower than at temperatures above 8 °C. Buchanan et al. (2004) described that L. monocytogenes characteristically reaches a lower N_max (10⁴ to 10⁶ log CFU/g) when grown at temperatures close to its lower limit for growth (2 to 5 °C), while higher storage temperatures may promote N_max to between 10⁷ and 10⁹ log CFU/g. Similar results were previously obtained by Werner et al. (1994) in culture broth, where the average N_max of L. monocytogenes at 4 °C was lower (p < 0.05) than that at 10 or 37 °C. Recently, Huang et al. (2019) found that growth of L. monocytogenes in honeydew, watermelon, and cantaloupe at 4 °C showed a 0.7 log-order increase by the end of the storage period, while a higher N_max was reached at higher storage temperatures.

The lower L. monocytogenes N_max under a hostile environment such as a temperature of 4 °C may also be a result of the Jameson effect, where the growth of L. monocytogenes ceased when the indigenous flora reached its N_max, i.e., due to microbial competition (Jameson, 1962; Wang et al., 2015). Additionally, McManamon et al. (2017) suggested that intraspecies competition plays a greater role in limiting growth when L. monocytogenes reaches higher cell densities at low temperatures. However, the observed data at all temperatures showed that the Jameson effect might not always be expected to occur, since the interaction of L. monocytogenes and the microbial flora does not necessarily affect growth under all conditions (Koseki et al., 2011). Muscle degradation by proteolytic bacteria present in the meat product might actually support the growth of L. monocytogenes. Gouet et al. (1978) described that the absence of spoilage microorganisms, such as in irradiated meat products, consequently decreased L. monocytogenes numbers after incubation for an extended period of time. The fact that the N_max values of L. monocytogenes were not higher than 8 log CFU/g in the ground pork study was attributed to the lack of nutrients required for L. monocytogenes growth in the meat (Johnson et al., 1988). Therefore, to mitigate the growth potential of L. monocytogenes (greater than 100 CFU/g) during the shelf-life of products, stricter temperature management is required at the retail level and the storage temperature should be limited to a maximum of 5 °C (Ziegler et al., 2019).

The growth data of L. monocytogenes under various storage temperatures generated by real-time PCR corresponded well with the prediction curve from the Baranyi and Roberts model. However, the ALOA enumeration results were lower than quantification by real-time PCR, especially at storage temperatures below 8 °C. As seen in Table 1, the lag phase duration obtained from ALOA at 4 °C was double that obtained from the real-time PCR data. The harsh environment at 4 °C was assumed to cause high cell damage, and L. monocytogenes lost the ability to recover on ALOA and produce viable colonies. ALOA tends to overestimate the lag phase duration because population increases including nonviable cells cannot be accurately estimated, resulting in underestimation of µ_max values. Since real-time PCR quantifies the amount of DNA from samples, total changes in L. monocytogenes concentrations can be estimated regardless of the presence of damaged cells that are not viable on ALOA. In contrast, the estimated L. monocytogenes viable numbers from the MPN method resembled the real-time PCR quantification results. A study of L. monocytogenes growth in naturally contaminated ice cream products also showed that the direct plating method using ALOA estimated a lower value than the MPN method when the concentration of background flora was higher (Chen et al., 2017). In addition, there was a poor correlation between direct plating on selective medium and MPN from naturally contaminated samples with another bacteria (Berry and Wells, 2008).

Conclusion

The results obtained in this study demonstrated the potential use of the real-time PCR method to quantify L. monocytogenes from complex food matrices containing microbial background flora. Real-time PCR showed higher performance to specifically quantify L. monocytogenes concentrations compared to the ALOA or MPN method. Underestimation of L. monocytogenes concentrations on selective agar medium may lead to fail-dangerous prediction, especially when bacterial cells are subjected to severe processing steps. The fail-dangerous conditions resulting from the prediction results do not match commercial realities. An accurate prediction model will help food industries to enhance the safety of food products that meet consumer demands. Thus, considering its high accuracy, bacterial quantification by realtime PCR has potential to solve growth analysis problems associated with food environmental effects, which are difficult to evaluate by conventional methods.

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