A novel produce wash consisting of pelargonic acid (PEL) emulsions was tested on tomatoes contaminated with a five-serovar Salmonella enterica cocktail. Ability to reduce contamination on the inoculated tomato surface, as well as mitigation of subsequent cross-contamination to uninoculated tomatoes washed in re-used/spent wash water were examined. Sanitizer efficacy was also examined over 1 and 7 d storage time (8 °C, recommended for red ripe tomatoes) and in the presence of 0.5% (w/v) organic load. PEL performed statistically the same (p ≤ 0.05) at both 30 mM and 50 mM concentrations and resulted in greater than 1, 5 and 6 log CFU/g Salmonella reductions at 0 h, 1 d and 7 d, respectively, when compared to a water-only or no rinse (NR) treatment. This was also a significantly greater reduction than was observed due to chlorine (sodium hypochlorite) and peroxyacetic acid (PAA) at all time points (p ≤ 0.01). Organic load had no impact on sanitizer efficacy for all examined treatments. Finally, PEL had a deleterious impact on tomato texture. At 1 d, ca. 5 N and 7 N were required to achieve tomato skin penetration and compression, respectively, compared to >9 N and 15 N required by all other treatments (p ≤ 0.05). While PEL sanitizers effectively reduced inoculated Salmonella and subsequent transfer to uninoculated tomatoes, reformulation may be necessary to prevent deleterious quality impacts on produce.

Cyanotoxins in agricultural waters pose a human and animal health risk. These toxins can be transported to nearby crops and soil during irrigation practices; they can remain in the soil for extended periods and be adsorbed by root systems. Additionally, in livestock watering ponds, cyanotoxins pose a direct ingestion risk. This work evaluated the performance of the random forest algorithm in estimating microcystin concentrations using eight in situ water quality measurements at one active livestock water pond and two working irrigation ponds in Georgia and Maryland, USA. Measurements of microcystin along with eight in situ-sensed water quality parameters were used to train and test the machine learning model. The models performed better at the Georgia ponds compared to the Maryland pond, and interior models performed better than nearshore or whole-pond models. The most important variables for microcystin prediction were water temperature and phytoplankton pigments. Overall, the random forest algorithm(RF), augmented with a ‘trainControl’ function to perform repeated cross validations, was able to explain 40% to 70% of the microcystin concentration variation in the three agricultural ponds. Water quality measurements showed potential to aid water monitoring/sampling design by predicting the microcystin concentrations in the studied ponds by using readily available and easy to collect in situ data.

Quantification of Escherichia coli in water is commonly used to understand a surface source's suitability for produce irrigation. Location, season, and physicochemical water quality impact the levels of E. coli in irrigation ponds. Water samples were collected periodically at three ponds in Southeast Georgia along a sampling grid from July 2021 through September 2023 and quantified for E. coli with simultaneous collection of relevant water physicochemical parameters. Mean relative differences (MRDs) were calculated for each collection point to determine differences in E. coli levels across sampling locations. E. coli levels varied significantly across sampling area (perimeter, surface, and subsurface) at each pond. The log most probable number E. coli 100 mL⁻¹ (EC MRD) values ranged from −0.25 to 0.33 in Pond 1, −1.5 to 0.65 in Pond 2, and −1.25 to 0.65 in Pond 3. In Pond 1, EC MRD correlated positively with chlorophyll and turbidity, and negatively with dissolved organic matter, dissolved oxygen (DO), specific conductance, and pH MRDs. In Pond 2, the MRD of E. coli correlated with the MRDs of chlorophyll, DO, phycocyanin, pH, and temperature. In Pond 3, E. coli MRD correlated positively with nitrate MRD. This work showed MRD analysis may reveal stable patterns of E. coli and the physicochemical factors that impact these levels in ponds, though no universal covariates were identified that could estimate E. coli levels. These findings may provide context for water quality managers wishing to augment measurements of E. coli with other factors, or better represent variable E. coli levels with MRD.

Cyanobacteria and their toxins can have multiple effects on agricultural productivity and water bodies. Cyanotoxins can be transported to nearby crops and fields during irrigation and may pose a risk to animal health through water sources. Spatial and temporal variations in cyanotoxin concentrations have been reported for large freshwater sources such as lakes and reservoirs, but there are fewer studies on smaller agricultural surface water bodies. To determine whether spatiotemporal patterns of the cyanotoxin microcystin occurred in agricultural waters used for crop irrigation and livestock watering, three agricultural ponds on working farms in Georgia, USA, were sampled monthly within a fixed spatial grid over a 17-month period. Microcystin concentrations, which ranged between 0.04 and 743.75 ppb, were determined using microcystin–ADDA ELISA kits. Temporal stability was assessed using mean relative differences between microcystin concentrations at each location and averaged concentrations across ponds on each sampling date. There were locations or zones in all three ponds that were consistently higher or lower than the average daily microcystin concentrations throughout the year, with the highest microcystin concentrations occurring in winter. Additionally, microcystin patterns were strongly correlated with the patterns of chlorophyll, phycocyanin, and turbidity. The results of this work showed that consistent spatiotemporal patterns in cyanotoxins can occur in produce irrigation and livestock watering ponds, and this should be accounted for when developing agricultural water monitoring programs.

Electron-beam (E-beam) irradiation is a non-thermal intervention with the potential to reduce microbial load and extend shelf-life in raw poultry products. The objective of this study was to evaluate the effectiveness of E-beam irradiation doses on microbial load, color stability, and shelf-life parameters of tray packed chicken tenders stored under refrigerator conditions for 27 days. Across all sampling days, E-beam irradiation significantly reduced APC compared to the nonirradiated control (p < 0.05), with suppression of microbial growth persisting throughout refrigerated storage. By day 27, irradiated samples exhibited reductions of 2.90 to 5.90 Log CFU/g compared to the control. Enterobacteriaceae (EB) was largely undetectable in irradiated samples during early storage. At day 27, irradiated samples were significantly lower than control samples, indicating delay in microbial growth. E-beam irradiation consistently increased redness(a*) across storage days independent of dose, while changes in lightness (L*) and yellowness (b*) were minimal, time-dependent and largely remained within acceptable ranges for poultry meat. Ranges 1.5 and 2.5 kGy provided effective microbial suppression with minor impact on color. These findings demonstrate that E-beam irradiation has the ability to alter spoilage of refrigerated chicken tenders by reducing initial microbial load and delaying the onset of microbial growth, while maintaining acceptable color characteristics.

Escherichia coli is one measure of fecal contamination in water, which is expected to correlate with the presence of fecal pathogens, such as pathogenic E. coli or Salmonella . Supplementing E. coli measurements with other water quality measurements, for example, temperature or pH, may better identify situations when foodborne pathogens are likely to be present in water. This work used generalized and multinomial regression and machine learning (K‐nearest neighbors, random forest (RF), and support vector machines) to create explanatory models for levels of E. coli , pathogenic E. coli , and Salmonella presence in irrigation ponds. Model performance was mixed, with RF models performing best for E. coli levels (root mean square error [RMSE] = 0.45) for all ponds ( n = 1266), pond 1 (RMSE = 0.44, n = 563), and pond 2 (RMSE = 0.71, n = 505), while generalized regression performed best at pond 3 (RMSE = 0.41, n = 198). Pathogenic E. coli presence for all ponds was best explained by RF (receiver operating characteristic area under the curve [ROC AUC] = 0.94, n = 717). Salmonella presence for ponds 1 and 2 ( n = 284) was best explained by multinomial regression (ROC AUC = 0.83). These results indicate that explanatory models struggled to identify situations conducive to pathogen occurrence in this system and were not useful in identifying factors associated with pathogen presence.