The objective of this study was to determine the in vitro antimicrobial activity of several organic acids and their derivatives against Gram-positive (G+) and Gram-negative (G−) bacteria. Butyric acid, valeric acid, monopropionin, monobutyrin, valerate glycerides, monolaurin, sodium formate, and ProPhorce—a mixture of sodium formate and formic acid (40:60 w/v)—were tested at 8 to 16 concentrations from 10 to 50,000 mg/L. The tested bacteria included G− bacteria (Escherichia coli, Salmonella enterica Typhimurium, and Campylobacter jejuni) and G+ bacteria (Enterococcus faecalis, Clostridium perfringens, Streptococcus pneumoniae, and Streptococcus suis). Antimicrobial activity was expressed as minimum inhibitory concentration (MIC) of tested compounds that prevented growth of tested bacteria in treated culture broth. The MICs of butyric acid, valeric acid, and ProPhorce varied among bacterial strains with the lowest MIC of 500–1000 mg/L on two strains of Campylobacter. Sodium formate at highest tested concentrations (20,000 mg/L) did not inhibit the growth of Escherichia coli, Salmonella Typhimurium, and Enterococcus faecalis, but sodium formate inhibited the growth of other tested bacteria with MIC values from 2000 to 18,800 mg/L. The MIC values of valerate glycerides, monolaurin, and monobutyrin ranged from 2500 to 15,000 mg/L in the majority of bacterial strains. Monopropionin did not inhibit the growth of all tested bacteria, with the exception that the MIC of monopropionin was 11,300 mg/L on Clostridia perfringens. Monolaurin strongly inhibited G+ bacteria, with the MIC value of 10 mg/L against Streptococcus pneumoniae. The MIC tests indicated that organic acids and their derivatives exhibit promising antimicrobial effects in vitro against G− and G+ bacteria that are resistant to antimicrobial drugs. The acid forms had stronger in vitro antimicrobial activities than ester forms, except that the medium chain fatty acid ester monolaurin exhibited strong inhibitory effects on G+ bacteria.

This study quantified the association of rodent fruit damage and the microbiological quality of irrigation water on the risk of microbiological contamination of strawberries collected from 18 U-pick farms across five different districts in the Guangzhou metropolitan region of southern China. Fifty-four composite strawberries samples, with or without evidence of rodent or avian foraging damage (i.e., bitten), along with 16 irrigation water samples, were collected during the spring of 2014 and winter of 2015 from our cohort of 18 farms. Composite strawberry samples and irrigation water were analyzed for total coliforms, E. coli, Salmonella, E. coli O157, Giardia, and Cryptosporidium. Total coliforms and E. coli were detected in 100% and ~90% of irrigation water samples, respectively. In contrast, Cryptosporidium was detected in only two water samples, while Salmonella, E. coli O157, and Giardia were not detected in any water samples. Strawberries with signs of being bitten by wildlife had significantly higher concentrations of total coliforms and E. coli, compared to strawberries with no physical evidence of rodent damage (p < 0.001). Similarly, Cryptosporidium was detected in 7/18 (39%) of bitten, 4/18 (22%) of edge, and 5/18 (28%) of central strawberry samples, respectively. Concentration of E. coli on strawberries (p < 0.001), air temperature (p = 0.025), and presence of Cryptosporidium in irrigation water (p < 0.001) were all associated with the risk of Cryptosporidium contamination on strawberries. Salmonella and Giardia were detected in <4% strawberry samples and E. coli O157 was not detected in any samples. These results indicate the potential food safety and public health risks of consuming unwashed strawberries from U-pick farms, and the need for improved rodent biosecurity of U-pick strawberry fields and enhanced microbiological quality of irrigation water used at these facilities.

This study characterized the effect of distance from beef cattle feedlots, environmental factors, and climate on the occurrence of airborne bacterial indicators and pathogens. Three hundred air samples were collected over 6 months from five feedlots, with each air sample comprising 6000 L of air. Air samples were processed onto TSB-enriched air filters, qPCR-screened, and then qPCR-confirmed for suspect positive colonies of E. coli O157, non-O157-Shiga-toxin-producing E. coli (STEC), Salmonella, and E. coli. Direct enumeration of E. coli was also collected. Although no bacterial pathogens were qPCR-confirmed for the 300 samples, E. coli was detected in 16.7% (50/300) of samples, with an overall mean concentration of 0.17 CFU/6000 L air. Logistic regression analyses revealed a higher odds of E. coli for samples in close proximity compared to >610 m (2000 ft) distance from feedlots, along with significant associations with meteorological factors, sampling hour of day, and the presence of a dust-generating activity such as plowing a field or nearby vehicular traffic. The lack of bacterial pathogen detection suggests airborne deposition from nearby feedlots may not be a significant mechanism of leafy green bacterial pathogen contamination; the result of our study provides data to inform future revisions of produce-safety guidance.

California Leafy Green Products Handler Marketing Agreement (LGMA) established food safety metrics with guidance recommendations of 366 m (1,200 feet) and 1,609 m (1 mile) distances between production fields of leafy greens and a concentrated animal feeding operation (CAFO) containing >1,000 and >80,000 head of cattle, respectively. This study evaluated the effect of these distance metrics and environmental factors on the occurrence of airborneEscherichia coliin proximity to seven commercial beef cattle feedlots located in Imperial Valley, California. A total of 168 air samples were collected from seven beef cattle feedlots during March and April 2020, which were the months implicated in the 2018 Yuma Arizona lettuce outbreak of E. coli O157:H7. The distance between air sampling sites and the edge of the feedlot ranged from ∼0 to ∼2,200 m (∼1.3 mile), with each sample comprised of 1,000 L of processed air taken at a 1.2 m elevation over a 10-minute duration. E. colicolonies were enumerated on CHROMagar ECC selective agar and confirmed with conventional PCR. Meteorological data (air temperature, wind speed, wind direction, relative humidity) were collectedin situ. The prevalence and mean concentration ofE. coliwere 6.55% (11/168) and 0.09 CFU per 1,000 L of air, with positive samples limited to within 37 m (120 ft) of the feedlot.Based on logistic regression, the odds of airborne E. coli detection were associated with little to no wind and close proximity to a feedlot. This pilot study found limited dispersal of airborne E. coli in proximity to commercial feedlots in Imperial Valley, with light-to-no wind and proximity within 37 m of a feedlot significant factor-associated airborne E. coli in this produce-growing region of California.

The authors wish to make the following corrections to this paper published in Molecules

Accurate water quality monitoring of microbial indicators hinges on understanding the impact of holding times and treatments on water sample results. This study examined interacting factors influencing holding times for fecal indicator bacteria (FIB) in water samples held for a range of times (0–216 h) under three storage treatments (i.e., Controlled Cold [∼4°C], Controlled Ambient [∼21°C], and Changing Conditions [∼4°C–21°C]). Four separate trials were conducted during 2016 using two distinct water supplies: river water ( n = 2) and post‐irrigation canal water ( n = 2). Large carboys of water ( n = 3/trial, ∼53L) were evenly divided into 500 mL aliquots for storage under different treatments ( n = 105/treatment). Samples were processed in triplicate using standard membrane filtration for Escherichia coli and fecal coliforms. Linear regression with autocorrelation and Dunnett's test were used to analyze the concentration of FIB across holding times from baseline ( T = 0). Canal water samples maintained FIB concentrations similar to T = 0 for at least 72 h under Controlled Cold; at least 48 h under Changing Conditions, and at least 30 h under Controlled Ambient treatments. By contrast, river water samples maintained concentrations similar to T = 0 at least 180 h without statistically significant changes under Controlled Cold, at least 72 h under Changing Conditions, and at least 36 h under Controlled Ambient treatments. Regardless of the treatments, river water samples were more stable than canal water samples, likely due to starting conditions. Holding times for water quality monitoring continues to be a multifaceted challenge and may warrant further exploration or more flexibility around regulatory requirements.