BACKGROUND: Agricultural water is known to be one of the major routes in bacterial contamination of fresh vegetable. However, there is a lack of fundamental data on the microbial safety of agricultural water in Korea. METHODS AND RESULTS:We investigated the density of indicator bacteria in the surface water samples from 31 sites collected in April, July, and October 2018, while the groundwater samples were collected from 20 sites within Jeollabuk-do in April and July 2018. In surface water, the mean density of coliform, fecal coliform, and Escherichia coli was 2.7±0.55, 1.9±0.71, and 1.4±0.58 log CFU/100 mL, respectively, showing the highest bacterial density in July. For groundwater, the mean density of coliform, fecal coliform, and E. coli was 1.9±0.58, 1.4±0.37, and 1.0±0.33 log CFU/ 100mL, respectively, showing no significant difference between sampling time. The survival of E. coli O157:H7 were prolonged in water with higher organic matter contents such as total nitrogen (TN), and nitrate-nitrogen (NO 3 -N). The reduction rates of E. coli O157:H7 in the water showed greater in order of 25, 35, 5, and 15℃. CONCLUSION: These results can be utilized as fundamental data for prediction the microbiological contamination of agricultural water and the development of microbial prevention technology.

No abstract

BACKGROUND: Irrigation water is known to be one of the major sources of bacterial contamination in agricultural products. In addition, anti-microbial resistance (AMR) bacteria in food products possess serious threat to humans. This study was aimed at investigating the prevalence of foodborne bacteria in irrigation water and evaluating their anti-microbial susceptibility. METHODS AND RESULTS: Surface water (n = 66 sites) and groundwater (n = 40 sites) samples were collected from the Gyeongi and Gangwon provinces of South Korea during April, July, and October 2019. To evaluate the safety of water, fecal indicators (Escherichia coli) and foodborne pathogens (E. coli O157:H7, Salmonella spp., and Listeria monocytogenes) were examined. E. coli isolates from water were further tested for antimicrobial susceptibility using VITEK2 system. Overall, detection rate of foodborne pathogens in July was highest among three months. The prevalence of pathogenic E. coli (24%), Salmonella (3%), and L. monocytogenes (3%) was higher in surface water, while only one ground water site was contained with pathogenic E. coli (2.5%). Of the 343 E. coli isolates, 22.7% isolates were resistant to one or more antimicrobials (ampicillin (18.7%), trimethoprim-sulfamethoxazole (7.0%), and ciprofloxacin (6.7%)). CONCLUSION: To enhance the safety of agricultural products, it is necessary to frequently monitor the microbial quality of water.

BACKGROUND: Contaminated water was a major source of food-borne pathogens in various recent fresh producerelated outbreaks. This study was conducted to investigate the microbial contamination level and correlations between the level of sanitary indicator bacteria and the detection ratio of pathogens in agricultural water by logistic regression analysis. METHODS AND RESULTS: Agricultural water was collected from 457 sites including surface water (n=300 sites) and groundwater (n=157 sites) in South Korea from 2018 to 2020. Sanitary indicator bacteria (total coliform, fecal coliform, and Escherichia coli) and food-borne pathogens (pathogenic E. coli, E. coli O157:H7, Salmonella spp., and Listeria monocytogenes) were analyzed. In surface water, the coliform, fecal coliform, and E. coli were 3.27±0.89 log CFU/100 mL, 1.90±1.19 log CFU/100 mL, and 1.39±1.26 log CFU/100 mL, respectively. For groundwater, three kinds of sanitary indicators ranged in the level from 0.09 -0.57 log CFU/100 mL. Pathogenic E. coli, Salmonella and Listeria monocytogenes were detected from 3%-site, 1.5%site, and 0.6%-site water samples, respectively. According to the results of correlations between the level of sanitary indicator bacteria and the detection ratio of pathogens by logistic regression analysis, the probability of pathogen detection increased individually by 1.45 and 1.34 times as each total coliform and E. coli concentration increased by 1 log CFU/100mL. The accuracy of the model was 70.4%, and sensitivity and specificity were 81.5% and 51.7%, respectively. CONCLUSION(S):The results indicate the need to manage the microbial risk of agricultural water to enhance the safety of fresh produce. In addition, logistic regression analysis is useful to analyze the correlation between the level of sanitary indicator bacteria and the detection ratio of pathogens in agricultural water.