Predicting Salmonella Populations from Biological, Chemical, and Physical Indicators in Florida Surface Waters

Abstract

c Coliforms, Escherichia coli, and various physicochemical water characteristics have been suggested as indicators of microbial water quality or index organisms for pathogen populations. The relationship between the presence and/or concentration of Salmonella and biological, physical, or chemical indicators in Central Florida surface water samples over 12 consecutive months was explored. Samples were taken monthly for 12 months from 18 locations throughout Central Florida (n ‫؍‬ 202). Air and water temperature, pH, oxidation-reduction potential (ORP), turbidity, and conductivity were measured. Weather data were obtained from nearby weather stations. Aerobic plate counts and most probable numbers (MPN) for Salmonella, E. coli, and coliforms were performed. Weak linear relationships existed between biological indicators (E. coli/coliforms) and Salmonella levels (R 2 < 0.1) and between physicochemical indicators and Salmonella levels (R 2 < 0.1). The average rainfall (previous day, week, and month) before sampling did not correlate well with bacterial levels. Logistic regression analysis showed that E. coli concentration can predict the probability of enumerating selected Salmonella levels. The lack of good correlations between biological indicators and Salmonella levels and between physicochemical indicators and Salmonella levels shows that the relationship between pathogens and indicators is complex. However, Escherichia coli provides a reasonable way to predict Salmonella levels in Central Florida surface water through logistic regression.

Distribution and Characterization of Salmonella enterica Isolates from Irrigation Ponds in the Southeastern United States

“…Conversely, a survey of Canadian rivers reported significantly higher prevalence in spring than in summer, although seasonal precipitation was also correlated with increased Salmonella presence (43). Moreover, results showing small but significant positive correlations with temperatures and rainfall are consistent with prior research for central Florida surface water during an overlapping time period (16). Although regression analysis provided predictive models for effects of rainfall and temperature on Salmonella densities, further study is needed to refine these models for evaluating Salmonella risk.…”

“…Multiple linear regression modeling revealed that generic E. coli levels and temperatures in both water and sediment were significantly related to the Salmonella densities (P Ͻ 0.05). However, the R 2 values of these models were low (Ͻ0.2), indicating that the models did not reveal strong linear relationships (16). Therefore, logistic regression models were also used to examine Salmonella occurrence with respect to these parameters, and a predictive relationship (P Ͻ 0.05) was observed for both generic E. coli densities and water temperatures (not shown) relative to Salmonella densities in water and sediment samples (Fig.…”

“…Differences in Salmonella mean populations for different categorical pond characteristics were determined using generalized linear models (GLM). Moreover, multiple linear regressions were run to determine the relationships among environmental parameters and Salmonella density, and binomial logistic regressions were done to predict the probability of Salmonella occurrence (16). For both regression analyses, Salmonella level/ occurrence was the dependent variable, while all other parameters (fecal indicator bacteria, temperatures, and total rainfall amounts) were independent variables.…”

“…Unfortunately, the densities of indicator bacteria generally show little or no correlation with the presence/densities of pathogens in environmental samples (14,(16)(17)(18)(19). Therefore, we examined whether or not the ponds in this study would meet the FSMA criteria.…”

“…The recommended microbial standards for the quality of agricultural water that comes into direct contact with preharvest produce (other than sprouts) requires the threshold of generic Escherichia coli to be Ͻ410 most probable number (MPN)/100 ml for any single sample, with a geometric mean of Ͻ126 MPN/100 ml for all samples. Although this policy change will provide greater assurance of food safety, it may not be sufficient to eliminate risks from pathogens, such as Salmonella, that have shown little or no correlation between the level of the pathogen and that of generic E. coli (14,(16)(17)(18)(19)(20)(21)(22).…”