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.

Cocktails of lawn-collected cells were used to determine the survival of Salmonella, Escherichia coli O157:H7, and Listeria monocytogenes on the surface of raw peanut and pecan kernels. Kernels were inoculated with mixtures of four to five strains at 3 or 6 log CFU/g, dried at room temperature, and then stored at -24 ± 1, 4 ± 2, and 22 ± 1°C for 28 or 365 days. In most cases, rates of decline of the pathogens did not differ significantly between the two inoculum concentrations in the 28-day study. At 6 log CFU/g, populations of all pathogens were reduced by 0.5 to 1.6 log CFU/g during an initial 3-day drying period on both peanuts and pecans. The moisture content of peanuts and pecans remained stable at -24 ± 1 and 22 ± 1°C; at 4 ± 2°C, the moisture content increased from 3.8 to 5.6% on peanuts and from 2.6 to 3% on pecans over 365 days. Pathogen populations were stable on pecans stored under frozen and refrigerated conditions, except for L. monocytogenes, which declined at a rate of 0.03 log CFU/g/30 days at 4 ± 2°C. Salmonella populations were stable on peanuts stored at -24 ± 1 and 4 ± 2°C, but E. coli O157:H7 and L. monocytogenes declined at rates of 0.03 to 0.12 log CFU/g/30 days. At 22 ± 1°C, Salmonella, E. coli O157:H7, and L. monocytogenes declined at a rate of 0.22, 0.37, and 0.59 log CFU/g/30 days, respectively, on peanuts, and at 0.15, 0.34, and 1.17 log CFU/g/30 days, respectively, on pecans. Salmonella counts were above the limit of detection (0.30 log CFU/g) throughout the study. In most cases during storage, counts obtained from pecans were higher than from peanuts.

The microbial quality of water that comes into the edible portion of produce is believed to directly relate to the safety of produce, and metrics describing indicator organisms are commonly used to ensure safety. The US FDA Produce Safety Rule (PSR) sets very specific microbiological water quality metrics for agricultural water that contacts the harvestable portion of produce. Validation of these metrics for agricultural water is essential for produce safety. Water samples (500 mL) from six agricultural ponds were collected during the 2012/2013 and 2013/2014 growing seasons (46 and 44 samples respectively, 540 from all ponds). Microbial indicator populations (total coliforms, generic Escherichia coli, and enterococci) were enumerated, environmental variables (temperature, pH, conductivity, redox potential, and turbidity) measured, and pathogen presence evaluated by PCR. Salmonella isolates were serotyped and analyzed by pulsed-field gel electrophoresis. Following rain events, coliforms increased up to 4.2 log MPN/100 mL. Populations of coliforms and enterococci ranged from 2 to 8 and 1 to 5 log MPN/100 mL, respectively. Microbial indicators did not correlate with environmental variables, except pH (P<0.0001). The invA gene (Salmonella) was detected in 26/540 (4.8%) samples, in all ponds and growing seasons, and 14 serotypes detected. Six STEC genes were detected in samples: hly (83.3%), fliC (51.8%), eaeA (17.4%), rfbE (17.4%), stx-I (32.6%), stx-II (9.4%). While all ponds met the PSR requirements, at least one virulence gene from Salmonella (invA-4.8%) or STEC (stx-I-32.6%, stx-II-9.4%) was detected in each pond. Water quality for tested agricultural ponds, below recommended standards, did not guarantee the absence of pathogens. Investigating the relationships among physicochemical attributes, environmental factors, indicator microorganisms, and pathogen presence allows researchers to have a greater understanding of contamination risks from agricultural surface waters in the field.