Prevalence and related factors of Salmonella spp. and Salmonella Typhimurium contamination among broiler farms in Kerman province, Iran

Journal Pre-proof Prevalence and related factors of Salmonella spp. and Salmonella Typhimurium contamination among broiler farms in Kerman province, Iran A. Firouzabadi, D. Saadati, M. Najimi, M. Jajarmi

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S0167-5877(19)30289-2

DOI:

https://doi.org/10.1016/j.prevetmed.2019.104838

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PREVET 104838

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Preventive Veterinary Medicine

Received Date:

29 April 2019

Revised Date:

10 November 2019

Accepted Date:

11 November 2019

Please cite this article as: Firouzabadi A, Saadati D, Najimi M, Jajarmi M, Prevalence and related factors of Salmonella spp. and Salmonella Typhimurium contamination among broiler farms in Kerman province, Iran, Preventive Veterinary Medicine (2019), doi: https://doi.org/10.1016/j.prevetmed.2019.104838

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Prevalence and related factors of Salmonella spp. and Salmonella Typhimurium contamination among broiler farms in Kerman province, Iran Firouzabadi A.a, Saadati D. b*, Najimi M.c, Jajarmi M.d

DVM graduated, Faculty of veterinary medicine, University of Zabol, P O Box: 538-98615

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a

Zabol, Sistan and Baluchestan, Iran.

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Department of Nutrition and Animal Breeding, Faculty of Veterinary Medicine, University

Department of Pathobiology, Faculty of Veterinary Medicine, University of Zabol, P O Box:

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c

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of Zabol, P O Box: 538-98615 Zabol, Sistan and Baluchestan, Iran.

d

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538-98615 Zabol, Sistan and Baluchestan, Iran.

Department of Pathobiology, Faculty of Veterinary Medicine, Shahid Bahonar University of

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Kerman, P O Box: 761-69133 Kerman, Iran.

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*Corresponding author: Tel: +985431232268

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Email: [email protected]

Fax: +985431232250

HIGHLIGHTS 

The apparent flock-level prevalence of Salmonella spp. (all serovars) and Salmonella Typhimurium in poultry in the province of Kerman, Iran, was 48% (95% CI = 39-58%) and 24% (95% CI = 16-33%) respectively.

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The result showed that a time interval of less than one month between the two breeding periods (OR = 6.530), the number of fans less than 5 in each poultry house (OR = 4.094) and the number of houses less than 4 in each farm significantly increased the probability of

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infection with Salmonella spp. (ORs were respectively 9.650, 29.427 and 7.140 for one, two and three houses). 

The results of multivariable logistic regression showed that the use of a bell drinking system

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(OR = 4.379) and the presence of fewer than 5 fans in each poultry house (OR = 2.512) had

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increased significantly the risk of infection with S. Typhimurium.

Abstract

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Salmonella is one of the most important pathogens in the poultry industry that not only causes financial and economic damage, but also, some serovars of this bacterium, including the S.

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Typhimurium, can infect humans through poultry-to-human transmission. The purpose of this

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study was to investigate the prevalence of this pathogen among broiler poultry houses in the Kerman region, southeast of Iran and to identify factors which could increase the risk for Salmonella contamination in the chickens. In a cross-sectional study, 110 poultry houses were surveyed from June to October 2018. Twenty-eight variables related to the prevalence of Salmonella contamination were considered by a questionnaire template with farmers' and laborers' help. Also, the prevalence of Salmonella in poultry manure was determined based on

fecal sampling, microbiological tests and polymerase chain reaction (PCR) technique. A multivariable logistic regression model was developed to measure the influence of independent variables on Salmonella contamination. Results showed that a time interval less than one month between the two breeding periods (OR = 6.530), the number of fans less than 5 in each poultry house (OR = 4.094) and the number of houses less than 4 in each farm significantly increased the probability of infection with Salmonella spp. (ORs were respectively 9.650, 29.427 and 7.140 for one, two and three

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houses). Also, the results of multivariable logistic regression showed that the use of a bell drinking system (OR = 4.379) and the presence of fewer than 5 fans in each poultry house (OR

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= 2.512) increased significantly the risk of infection with Salmonella Typhimurium.

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Keywords: Salmonella, Broiler chicken, Related factors, PCR, Kerman

1. Introduction

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Non-thyphoid Salmonella (NTS) is one of the main causes of human intestinal infections. Each year 93.8 million people develop NTS and 155,000 deaths occur globally (Majowicz et al.,

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2010; Feasey et al., 2012). non-typhoidal Salmonellae may be transmitted to humans through contact with animals, contaminated water, or the environment. But most cases of this infection

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in humans are foodborne (Hoelzer et al., 2011). Poultry is considered the most important source of Salmonella for humans (Kwon et al., 2000). Salmonella enterica subspecies enterica serovars Typhimurium and Enteritidis (usually abbreviated as S. Typhimurium and S. Enteritidis) are the most common causes of NTS. S. Typhimurium is the most frequently isolated serovar worldwide (Amini et al., 2015) that causes severe enterocolitis in caudal ileum, cecum and proximal colon (Coburn et al., 2007).

Prevention of Salmonella introduction into the chicken farm and prevention of in-farm transmission are recommended measures. Control of Salmonella infection in poultry farms requires data on the related factors of the infection (Skov et al., 1999). Although a descriptive study had already determined the prevalence of Salmonella in broiler in Kerman (Ashrafganjooyi et al., 2014), no epidemiological research had been carried out to identify the risk factors in this region. This study was conducted to investigate the associated factors of Salmonella spp. and S. Typhimurium contamination in broiler chickens in Kerman province.

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We used Adjusted OR (odds ratio) on multiple logistic regression models as a tool to assess the role of independent factors on the salmonellosis prevalence. Using multivariable logistic regression can control the effect of confounding variables, and thus, the adjusted OR obtained

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from multivariable logistic regression is less biased and more accurate than the unadjusted OR.

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of infection transmission to humans.

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The results of this study can be used for the control of poultry salmonellosis and the prevention

2. Materials and Methods

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2.1. Study farms and samples

The cross-sectional study was conducted from June to October 2018 on broiler chickens in

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Kerman province. The sampling unit in this study was the poultry house and the sample size

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of this study was one hundred and ten. This sample size is suitable for the analysis with the logistic regression method, if the confidence level and test power be considered as 0.95 and, 0.80, respectively (Agresti, 2018). A list of 412 active poultry farms was taken from the veterinary administration office of Kerman and the 103 poultry farms were selected randomly and contacted with their owners that 90 of them agreed to participate in the investigation.

In selected poultry houses, information for the associated factors with Salmonella contamination was collected by a pre-designed questionnaire. The quality of questionnaire was assessed in 10 poultry houses, and then a final questionnaire was compiled with 28 items. Approximately 25-30 g of poultry manure was collected from five different points in each of the poultry houses. Samples were transferred in special sterilized containers at 4°C to the veterinary microbiology laboratory of Shahid Bahonar University of Kerman for next steps. 2.2. Salmonella isolation

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Ten grams of each sample were added to ×10 volume of Selenite-F broth (Merck, Germany) and incubated at 37°C (OIE, 2008). After 16–20 hours, 50 μl of the incubated selective broth were cultured by streak plate method in Salmonella-Shigella (SS) agar (Merck Germany). The

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plates were incubated at 37°C for 24 hours and checked for up to five suspect colonies (pale

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with black center because of the H2S production). For more purification, these colonies were sub-cultured in SS agar at 37°C for 24 hours again. Urease production of the suspected colonies

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was tested using urea agar (Merck, Germany). Finally, the isolates which were urease-negative were saved for the next steps; the isolates were glycerolated by adding glycerol (for final

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concentration of 20%) to an overnight culture of the isolates in LB broth. (Jajarmi et al., 2015). 2.3. DNA extraction and molecular detection

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DNA extraction was performed on the cultured selenite F enrichment media and on each saved isolate

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from the previous step using a kit (Favorgen Biotech, Taiwan). Finally, the extracted DNA from each sample was stored in separate microtubes at -18 ° C. Molecular detection of Salmonella spp. and Salmonella Typhimurium was done by using a conventional PCR (polymerase chain reaction). The volume of reaction was 25μl containing 12.5μl Taq DNA polymerase 2x master mix red (Ampliqon, Denmark), 0.3μM of each primer (Pishgam, Iran; Table 1), 3μl of DNA template, and distilled water (up to volume of reaction).

Table 1 Also, the thermal programs generally were: 95°C (5 minutes) for initial denaturation, 95°C (40 s) for denaturation, 60°C (45 s) for annealing, 72°C (60 s) for extension and 72°C (10 minutes) for final extension. The steps denaturation, annealing and extension were repeated 35 times. Then, 10 μl of the PCR product were electrophoresed with 1.5% agarose gel which had been made by TAE buffer (Tris base, acetic acid and EDTA; 1X) for 1 hour in 80V. The agarose gel was stained with ethidium

(Cambridge, England) (Moghadam et al., 2017) (Figures 1).

2.4. Statistical analysis method

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Figure 1

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bromide (Sinaclon, Iran) and subsequently visualized and photographed by a Gel doc 1000 system

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The response variables (Salmonella spp. and S. Typhimurium contamination using PCR) in this study were dichotomous nominal ones. Therefore, the logistic regression model was used

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for statistical analysis. In this model, all independent variables were considered categorically. In order to avoid the multicollinearity problem, the relationships between very similar

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independent variables were measured by a Chi-square test. In cases where high

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multicollinearity was observed (p <0.05), one of those variables (the variable that was thought to be more related to the response variable) was selected for multivariable regression model. SPSS version 23 was used to analyze the data. In the first stage, the relationship between dependent variable, Salmonella contamination, and the independent variables or predictors was determined using Chi-square test and Fisher’s exact test. Next, variables related to Salmonella contamination (P <0.25) were used in the multivariable logistic regression model. Due to the

large number of independent variables, these variables were entered in the models in a forwardstepwise fashion. Variables were included or excluded from the model automated by software on the basis of the adjusted Wald test statistic, and only variables with P<0.05 were retained. The confidence interval for the prevalence of contamination was determined using binomial distribution.

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3. Results In this study, Salmonella spp. strains were isolated from 13 out of 110 samples (12%-95% CI: 6% to 19%) during microbiological tests. However, by PCR method, 53 and 26 samples were

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positive respectively for Salmonella spp. and S. Typhimurium. The molecular prevalence of contamination of poultry houses with Salmonella spp. was 48% (95% CI: 39% to 58%) and

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with S. Typhimurium was 24% (95% CI: 16% to 33%). All positive samples by culture were

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also positive by PCR method.

Of the 28 questions (independent variables), the answers of all chicken breeders to these two

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questions: "Do workers wear overalls and boots when working in poultry houses?" and "Are the feeders and drinkers disinfected at the beginning of the breeding period?" were positive.

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Therefore, these two variables were excluded from statistical analysis. Five variables were related to neither Salmonella spp. nor S. Typhimurium at level of P<0.25 (number of birds in

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the house, presence of livestock farms near the poultry, presence of dog and cats around the poultry, type of the bedding and location of fans). Six variables were related only to Salmonella spp. (variables 12 to 17 in table 2) while four cases were related only to S. Typhimurium (variables 12 to 15 in table 3). The other eleven were associated with both Salmonella spp. and S. Typhimurium at level of P<0.25 (Variables 1 to 11 in both Tables 2 & 3).

The molecular prevalence of contaminated poultry houses with Salmonella spp. and S. Typhimurium in terms of the related factors is shown in Tables 2 and 3. Due to the large number of variables, only those with P<0.25 are shown in these tables

Table 2

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Table 3

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The results of multivariable logistic regression analysis showed that the 3 variables were significantly related to Salmonella spp. contamination in poultry houses (Table 4). A time

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interval of less than one month between the two breeding periods (OR = 6.530), the number of fans less than 5 in each poultry house (OR = 4.094) and the number of houses less than 4 in

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each farm significantly increased the probability of contamination with Salmonella spp. (ORs

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Table 4

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Square = 33%).

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were respectively 9.650, 29.427 and 7.140 for one, two and three houses). (Nagelkerke R

Also, the multivariable analysis showed that the relationship of two variables with S. Typhimurium contamination was statistically significant (Table 5). The use of bell drinking system (OR = 4.379) and the presence of fewer than 5 fans in each poultry house (OR = 2.512)

had increased significantly the risk of infection with S. Typhimurium (Nagelkerke R Square = 19%).

Table 5

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4. Discussion We used 5 naturally pooled feces from the floor for detection of Salmonella spp. In an infected flock, Salmonella spp. is naturally excreted at any time by a small proportion of chickens

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(Desmidt et al., 1997). Therefore, the collection of pooled feces samples increases the chance of detection of an infected flock. In the present study, the prevalence of Salmonella spp.

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positive poultry houses based on culture was 12%. This is slightly less than the results of a study conducted in Shiraz, southern Iran, in which 22.5% of chicken flocks were positive to

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Salmonella according to cultures of pooled cloacal contents (Ansari-Lari et al., 2014). In a previous study in the province of Kerman only 5.6% of broiler chickens were found infected

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with Salmonella spp. (Ashrafganjooyi et al., 2014). But this prevalence might have been underestimated due to use of individual chicken droppings instead of pooled ones for the

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bacterial culture. Various prevalence rates of Salmonella spp. amongst broiler flocks have been

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reported in other countries, with 41% (Donado-Godoy et al., 2012) in Colombia, 50% (Arsenault et al., 2007) in Quebec province of Canada and 8.6% (Le Bouquin et al., 2010) in France. These three prevalence estimates (like the present study) were obtained using a culture of pooled samples. In the present study, all poultry breeders stated that between the two breeding periods, poultry houses were emptied, the old litter was removed, the houses were cleaned and the feeders and

drinkers were cleaned and disinfected. Also, all workers were wearing overalls and boots at the poultry house. Application of these biosecurity measures might be the reason for the lower prevalence in the present study compared to most other studies. In this study the molecular prevalence of positive samples for Salmonella spp. was 48%. This is much more than that found by bacterial culture which agrees with the work of Oliveira et al (2002). We used naturally pooled (fresh and old) droppings samples. It is well known that PCR assay is able to detect DNA from live and dead bacteria (Langkabel

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et al., 2014), while only live bacteria can be cultivated. PCR is a reliable and fast technique for detection of Salmonella in chicken fecal samples (Jinu et al., 2014). We used PCR results as dependent variables in the logistic regression model.

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In this study, three factors were related to contamination with Salmonella spp. in broiler

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chickens. The result shows that less interval between two breeding periods increases the Salmonella spp. prevalence. Decreasing the time interval increases the probability of

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Salmonella spp. survival from the previous breeding period. Another related factor was the number of poultry houses showing that the lower number of houses caused a higher

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contamination rate. Large poultry producers can afford to finance and provide better health facilities. On these farms, biosecurity measures are applied to prevent the

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introduction and spread of infectious diseases. As in a study conducted in 2013 in the country, the high number of poultry houses was reported as one of the factors

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contributing to the increase in Salmonella spp. infection in broiler breeding farms. (Bokaie et al., 2016). This is not consistent with the results of the recent study. The next factor related to Salmonella infection was the number of ventilation fans in the poultry house. When the number of installed fans in the poultry house was lower, the likelihood of the occurrence of contamination was higher. Airflow directly over the litter surface

contributes to drier litter and, consequently, to decreased viable Salmonella spp. population (Eriksson de Rezende et al., 2001). In some studies, other factors such as hand washing (Namata et al., 2009), rinsing and disinfecting poultry houses, distance more than 1 km from other farms, the absence of dogs and cats in the field (Snow et al., 2010), the use of antibiotics and the use of detergents (Cardinale et al., 2004) suggested a reduction in the risk of contamination. The presence of infected rodents with Salmonella Enteritidis (Snow et al., 2010), the

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replacement of birds in each poultry house separately (not all in – all out) (Mollenhorst et al., 2005), the presence of rodents after the disinfection of the poultry house (Rose et al., 2000) and the one-week increase in the flock’s age as with the increase in flock size

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(Namata et al., 2008), were the factors that have been mentioned as risk of contamination

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with Salmonella bacterium. However, these factors in the present study were not significantly related to Salmonella contamination in multivariable logistic regression. A

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European food safety authority reported that some related factors for occurrence of Salmonella in turkey flocks vary considerably between countries, and recommended that

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detailed risk factor analysis be carried out at national level in order to identify the specific factors (European food safety authority, 2008). It is also apparent that the prevalence and

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attributed effects of influencing factors for occurrence of Salmonella in broiler flocks varies across different regions.

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Also, in present study, two factors were related to contamination with S. Typhimurium

including the drinking water system and the number of ventilation fans in the poultry house. Bell drinkers increased the prevalence of contamination over the nipple ones. The probability of contamination is likely to increase due to the greater surface area of water in the bell drinkers than the nipple system. Another study on Danish broiler flocks shows

that medication, visual appearance of the bedding and the age of the chickens at sampling were ineffective in contamination with S. Typhimurium (Skov et al., 1999) and in the present study, the relationship between these mentioned factors and infection with S. Typhimurium were non-significant in multivariable analysis.

5. Conclusion

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In this research, flock-level prevalence of Salmonella spp. (all serovars) and S. Typhimurium was determined in poultry of Kerman province. The result showed that a time interval of less than one month between the two breeding periods, the number of fans less than 5 in each

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poultry house and the number of poultry houses less than 4 in each farm significantly increased the probability of infection with Salmonella spp. Also, the usage of a bell drinking system and

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the presence of fewer than 5 fans in each poultry house increased significantly the risk of

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infection with S. Typhimurium in the poultry house.

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Funding source

This article is extracted from the thesis written by A. Firouzabadi that was supported by

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dissertation grants from the Vice Chancellor for Academic Affairs and Graduate Studies, University of Zabol. Also this research is financially supported by vice chancellor of research

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and technology, University of Zabol under grant No. UOZ-GR-9517-120.

Declarations of interest None

Acknowledgments The authors would like to thank Mr. Shahriari, head of the genetic laboratory in the Veterinary faculty of Zabol University for providing sophisticated laboratory facilities. We also are thankful to Dr. Rashidi, general manager of the Kerman Provincial Veterinary Service for assistance in selecting the poultry houses.

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2005. Vet. Rec. 166, 579-586. https://doi.org/10.1136/vr.b4801.

Table 1: Characterization of primers used in this study Gene inva

Sequence (5'-3')

Length

Forward GTGAAATTATCGCCACGTTCGGGCAA Reverse TCATCGCACCGTCAAAGGAACC Forward CGGTGTTGCCCAGGTTGGTAAT Reverse ACTCTTGCTGGCGGTGCGACTT

285bp 559bp

Specific pathogen

Source

(Rahn et al., 1992) (Cohen et al., S. Typhimurium 1996) Salmonella spp.

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Primer

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Table 2: results of univariable analysis of variables related to detection of Salmonella spp. by PCR on 110 poultry houses in Kerman province. No. of No. of Percentage of P (overall Salmonella sampled Salmonella spp. p-value in Unadjusted Variable and level spp. poultry positive houses univariable OR positive a houses analysis) a houses 1. Use of hydrated lime for disinfection of poultry houses Yes 74 39 53% 1.751 No 36 14 39% 0.174 1.000 2. The removal of equipment from the poultry house for washing and disinfection at the beginning of the period Yes 31 19 61% 2.096 No 79 34 43% 0.085 1.000 3. Neighboring poultry farms Nearest farm ≤500 m 47 26 55% 1.651 Nearest farm >500 m 63 27 43% 0.196 1.000 4. Rodents seen Yes 20 12 60% 1.793 No 90 41 46% 0.242 1.000 5. Type of drinking water for birds b Urban water 57 27 47% 1.687 Qanat water 30 18 60% 2.812 Well water 23 8 35% 0.188 1.000 6. Refined drinking water for birds b Yes (Urban water or refined well and 79 34 43% 0.477 Qanat water) No 31 19 61% 0.085 1.000 7. Type of water used for washing b Urban water 54 24 44% 1.333 Qanat water c 32 20 63% 2.778 Well water 24 9 38% 0.134 1.000 8. Drinking system Bell drinker 90 50 56% 7.083 Nipple drinker 20 3 15% 0.001 1.000 9. Number of poultry houses in the farm d 1 house 42 21 50% 7.000 2 houses 34 22 65% 12.833 3 houses 18 8 44% 5.600 ≥4 houses 16 2 13% 0.007 1.000 10. Number of ventilation fans <5 fans 50 33 66% 3.785 ≥5 fans 59 20 34% 0.001 1.000 11. Age at sampling ≤15 days 19 11 58% 1.228 16 days -30 days 38 14 37% 0.521 ≥31 days 53 28 53% 0.209 1.000 12. Taking antibiotics Yes 45 27 60% 2.250 No 65 26 40% 0.039 1.000 13. Method of destruction of dead birds Burning 30 11 37% 0.651 Throwing in the well 63 34 54% 1.319 Throwing out of poultry house 17 8 47% 0.294 1.000 14. Presence of insects Yes 38 22 58% 1.819 No 72 31 43% 0.139 1.000

No. of No. of Percentage of Salmonella sampled Salmonella spp. spp. poultry positive houses positive a houses a houses

Variable and level

P (overall p-value in univariable analysis)

Unadjusted OR

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15. Time interval between two breeding periods
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Table 3: results of univariable analysis of variables related to detection of S. Typhimurium by PCR on 110 poultry houses in Kerman province. No. of No. of S. Percentage P (overall sampled Typh.a of S. Typh.a p-value in Unadjusted Variable and level poultry Positive positive univariable OR houses houses houses analysis) 1. Use of hydrated lime for disinfection of poultry houses. Yes 74 21 28% 2.46 No 36 5 14% 0.093 1.00 2. The removal of equipment from the poultry house for washing and disinfection at the beginning of the period Yes 31 14 45% 4.60 No 79 12 15% 0.001 1.00 3. Neighbouring poultry farms Nearest farm ≤500 m 47 15 32% 2.22 Nearest farm >500 m 63 11 17% 0.078 1.00 4. Rodents seen Yes 20 8 40% 2.67 No 90 18 20% 0.079 1.00 5. Type of drinking water for birds b Urban water 57 10 18% 1.01 Qanat water 30 12 40% 3.17 Well water 23 4 17% 0.047 1.00 6. Refined drinking water for birds b Yes (Urban water or refined well and Qanat 79 14 18% 0.34 water) No 31 12 39% 0.020 1.00 7. Type of water used for washing b Urban water 54 8 15% 0.87 Qanat water 32 14 44% 3.89 Well water 24 4 17% 0.006 1.00 8. Drinking system Bell drinker 90 25 28% 7.31 Nipple drinker 20 1 5% 0.039 1.00 9. Number of poultry houses in the farm 1 house 42 13 31% 6.72 2 houses 34 9 26% 5.40 3 houses 18 3 17% 3.00 ≥4 houses 16 1 6% 0.037 1.00 10. Number of ventilation fans <5 fans 50 21 42% 7.82 ≥5 fans 59 5 8% <0.001 1.00 11. Age at sampling ≤15 days 19 8 42% 2.48 16 days -30 days 38 6 16% 0.64 ≥31 days 53 12 23% 0.086 1.00 12. Washing & Disinfection of Hands before entering the poultry house Yes 27 4 15% .048 No or only with water 83 22 27% 0.214 1.000 13. "Formaldehyde gas for disinfection of the poultry house Yes 67 19 28% 2.04 No 43 7 16% 0.146 1.00 14. Distance to one-day chicken production center <150 km 38 12 32% 1.91 ≥150 km 72 14 19% 0.154 1.00

No. of No. of S. Percentage sampled Typh.a of S. Typh.a poultry Positive positive houses houses houses

Variable and level

P (overall p-value in Unadjusted univariable OR analysis)

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15. Visual appearance of poultry litter Dry 47 7 15% 0.36 Semi-wet 20 5 25% .069 Wet 43 14 33% 0.050 1.00 a. Salmonella Typhimurium b. due to multicollinearity among these variables, only variable 6 (Refined drinking water for birds) was entered into regression models.

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Table 4: Factors associated with Salmonella spp. contamination in poultry houses based on PCR results for variables significant in final multivariable logistic regression model Wald P Adjusted 95% CI for Variable and level B Wald value OR OR 12.384 0.006 1. Number of poultry houses in the farm 1 house 2.267 5.442 0.020 9.650 1.437-64.810 2 houses 3.382 11.232 0.001 29.427 4.072-212.66 3 houses 1.966 3.704 0.054 7.140 0.965-52.852 ≥4 houses 0 1 2. Number of ventilation fans <5 fans 1.409 7.620 0.006 4.094 1.505-11.137 ≥5 fans 0 1 3. Time interval between two breeding periods
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Table 5: Factors associated with S. Typhimurium contamination in poultry houses based on PCR results for variables significant in final multivariable logistic regression model Wald P Adjusted 95% CI for Variable and level B Wald value OR OR 1. Drinking system Bell drinker 1.477 4.393 0.036 4.379 1.101-17.422 Nipple drinker 0 1 2. Number of ventilation fans <5 fans 0.921 4.387 0.036 2.512 1.061-5.948 ≥5 fans 0 1 -

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Fig 1. PCR reactions for detection of Salmonella spp. and S. Typhimurium. M: The 100 bp marker, +: The positive control, and -: The negative control. Positive samples for Salmonella spp. characterized by identifying a gene fragment of 285 bp and positive samples for S. Typhimurium characterized by identifying a gene fragment of 559 bp.