Journal of food protection最新文献

In the U.S., Salmonella Kentucky is frequently isolated from food animals, but human cases are often linked to international travel. The objectives of this study were to utilize machine learning models to predict the animal hosts (bovine or poultry) and geographic origin (North America or not North America) of S. Kentucky isolates, and to identify the genomic features associated with host specificity. Core-genome single nucleotide polymorphisms (SNPs), gene presence, and intergenic regions were used to train multiple machine learning algorithms, and the highest performing models were XGBoost trained on core-genome SNPs. The top models accurately predicted animal host (F1 scores: 0.943 poultry, 0.891 bovine) and geographic origin (F1 scores: 0.981 North America, 0.982 not North America). Analyses of feature importance identified SNPs and genes that likely contribute to host specificity. In bovine-associated lineages, top features included SNPs or gene variants linked to drug efflux and pathogenesis in ST152, and the virulence factor rhuM in ST198. In poultry-associated lineages, many of the top features were plasmids or other mobile genetic elements, some of which carried resistance genes, as well as proteins with unknown function. When applied to U.S. human clinical isolates, the models predicted that the most prevalent sequence type, ST198, was primarily acquired from poultry outside North America (76.6%), whereas ST152 was mainly acquired from domestic poultry (92.4%). A notable number of U.S. human clinical cases, as well as some produce isolates and surface water isolates, were predicted to originate from bovine sources. These findings demonstrate that machine learning models using core-genome SNPs are highly effective for differentiating animal hosts of S. Kentucky isolates. These tools facilitate the study of foodborne pathogen ecology and help identify host-associated genomic features, which serve as potential targets for mitigation strategies in food animals.

Building on an earlier pilot classification study, the present study uses a fully independent statewide sample to psychometrically validate the Willing-Ready-Able (WRA) framework and to test behavioral separation across readiness stages. Many small-scale produce farms in West Virginia are exempt from the Food Safety Modernization Act (FSMA) Produce Safety Rule (PSR), yet voluntary adoption of Good Agricultural Practices (GAP) remains inconsistent due to the absence of a standardized engagement measure. The WRA framework is a behavioral readiness model that classifies FSMA-exempt growers by engagement in postharvest food safety practices. A statewide, cross-sectional survey was administered between July and August 2025 to FSMA-exempt produce growers recruited at farmers' markets across all six West Virginia Department of Agriculture regions. Of 130 growers approached, five declined to participate, yielding 125 completed responses (96% response rate). The questionnaire assessed three behavioral constructs-confidence, perceived importance, and willingness-toward seven GAP-aligned postharvest practices. Construct validity and internal consistency were evaluated using exploratory factor analysis and Cronbach's α (0.81-0.88). All constructs showed strong reliability and clear single-factor solutions. Group-level differences across WRA classifications were tested using multivariate analysis of variance (MANOVA), confirming significant overall effects (Wilks' Λ, p < 0.01). Univariate tests indicated significant differences for confidence (p < 0.01), willingness (p < 0.001), and importance (p < 0.001), with construct scores increasing stepwise across groups (Willing < Ready < Able). These findings validate WRA as a reliable diagnostic for assessing behavioral readiness among FSMA-exempt growers. The framework provides a practical mechanism for tailoring outreach strategies-building confidence among the Willing, reducing barriers for the Ready, and leveraging the Able as peer mentors-to strengthen voluntary food safety adoption and guide efficient resource allocation.

Hand hygiene is fundamental in reducing foodborne illness risk, yet current guidance in the U.S. Food and Drug Administration (FDA) Food Code may not fully reflect evidence on product performance or realistic foodservice practices. This study evaluated the in vivo effectiveness of a commercially available nonantibacterial foaming hand wash and an alcohol-based hand sanitizer (ABHS) in reducing bacterial load on hands and transfer to food. Twelve subjects were tested using ASTM E2784 with Escherichia coli (ATCC #10536) as the challenge organism. All subjects used a consistent, standardized technique for each condition. Hand wash was assessed at 5-, 15-, and 20-s lather durations, while ABHS was tested at one dose and with two doses and paper towel use ("SaniOnce"). Mean log10 reductions on hands and bacterial transfer to melon balls were measured. Hand wash always produced substantial reductions, with no significant differences (p > 0.05): mean log reductions were 2.95, 2.86, and 3.00, with corresponding mean transfers of 4.85, 4.76, and 4.64 for 5-, 15-, and 20-s, respectively. ABHS interventions demonstrated superior efficacy (p ≤ 0.05). A single ABHS dose achieved a mean log reduction of 4.06 with transfer of 2.58, while SaniOnce yielded the greatest effect (4.99 reduction, 1.88 transfer). Results suggest that well-formulated hand washes can be effective even at shorter lathering durations, and ABHS provides greater antibacterial efficacy on lightly soiled hands. These findings support modernizing the FDA Food Code guidance to allow situational use of ABHS independently in foodservice settings and to allow shorter wash times when good handwashing technique is practiced.