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DOE OSTI2024

Susceptibility of pESI positive Salmonella to treatment with biocide chemicals approved for use in poultry meat processing as compared to Salmonella without the pESI plasmid

Abstract Salmonella is a common cause of human foodborne illness, which is frequently associated with consumption of contaminated or undercooked poultry meat. Serotype Infantis is among the most common serotypes isolated from poultry meat products globally. Isolates of serotype Infantis carrying the pESI plasmid, the most dominant strain of Infantis, have been shown to exhibit oxidizer tolerance. Therefore, 16 strains of Salmonella with and without pESI carriage were investigated for susceptibility to biocide chemical processing aids approved for use in US poultry meat processing: peracetic acid (PAA), cetylpyridinium chloride (CPC), calcium hypochlorite, and sodium hypochlorite. Strains were exposed for 15 s to simulate spray application and 90 min to simulate application in an immersion chiller. All strains tested were susceptible to all concentrations of PAA, CPC, and sodium hypochlorite when applied for 90 min. When CPC, calcium hypochlorite, and sodium hypochlorite were applied for 15 s to simulate spray time, strains responded similarly to each other. However, strains responded variably to exposure to PAA. The variation was not statistically significant and appears unrelated to pESI carriage. Results highlight the necessity of testing biocide susceptibility in the presence of organic material and in relevant in situ applications.

Biotechnology & Applied Microbiology↗

DOE OSTI2024

Simulating water dynamics related to pedogenesis across space and time: Implications for four-dimensional digital soil mapping

Digital soil mapping (DSM) relies on machine-learning and geostatistics to represent soil property observations across space. DSM techniques are powerful but often empirical, being limited to the quality and density of point samples. Water dynamics are closely related to soil variability, and the physics that govern water movement are well known. Hydrological properties can hence be simulated by physical models through space and time, unveiling key characteristics about soils. We propose the use of hydrologic models to map soils across the surface (2D), depth (1D), and time (1D)–which provides a 4D approach to digital soil mapping (4DSM). The Distributed Hydrology Soil Vegetation Model (DHSVM) was applied to a watershed currently under pasture. Moisture sensors and wells were installed at different depths in the watershed on summit, sideslope and toeslope positions to validate the model. DHSVM simulations of soil moisture distribution and depth to saturation were performed during the hydrological year (October 2008-September 2009). Clusters of similar pixels based on soil moisture values were determined using Dynamic Time Warping (DTW) to align temporal data and K-means. Clustering was performed both seasonally and for the entire year. Temporal patterns simulated by DHSVM matched measurements given by moisture sensors and wells. Seasonal clusters differed from the annual cluster. Distinct clusters were observed for each season and with depth, showing that spatiotemporal soil variability is lost when statically assessing soils. Spatiotemporal clusters corroborated field observations of fragipan occurrence not explicitly spatially mapped by Soil Survey Geographic Database (SSURGO). If a connection can be made between water and soils, static and dynamic soil variability can be predicted using physically based hydrologic models. Hydrologic models can benefit soil mapping by enabling reliable 4D simulation of water dynamics, which are fundamental to soil variability and soil classification and directly relate to biological, physical and chemical soil processes not captured by typical soil sampling protocols.

54 ENVIRONMENTAL SCIENCES↗