Escherichia coli and Salmonella enterica are significant causes of gastrointestinal disease globally. The Contamination Sanitization Inspection and Disinfection (CSI-D) device is a handheld fluorescence-based imaging system that disinfects food contact surfaces using ultraviolet-C (UVC) illumination. The goal of this study was to determine the optimal parameters for disinfection of E. coli and S. enterica using the CSI-D system. E. coli and S. enterica Enteritidis, Newport, Typhimurium, and Javiana were grown on selective media, followed by transfer to Luria Bertani broth. After overnight incubation, the cultures were diluted and spread-plated on L-agar. The plates were exposed to high-intensity (10 mW/cm2) or low-intensity (5 mW/cm2) UVC for 1 s, 3 s, or 5 s. Exposed and control plates were incubated at room temperature for 2-3 h, then overnight at 37°C. The resulting colonies were counted and compared to control plates. Three trials were conducted on separate days. The average of the trials showed that exposure times of 3-5 s at either intensity resulted in effective and consistent destruction of E. coli and S. enterica. The minimum reduction at 3 s exposure for both intensities was 96%, with a maximum of 100%. The 1 s exposure time showed inconsistent results, with a 0-61.5% survival rate. The results of this study show that exposure to UVC for at least 3 s is required to achieve consistent disinfection of 96-100% for generic E. coli and S. enterica.
A multimodal sensing system was developed for automated and intelligent food safety inspection. The system uses two pairs of lasers and spectrometers at 785 and 1064 nm to realize dual-band Raman measurement. Automated sampling can be conducted using a XY moving stage for solid, powder, and liquid samples in customized well plates or randomly scattered in standard Petri dishes (e.g., bacterial colonies). Three LED lights (white backlight, UV ring light, and white ring light) and two color cameras are used for machine vision measurements of samples in the Petri dishes (e.g., transmission, fluorescence, and color). Real-time image processing and motion control techniques are used to fulfill automated sample counting, positioning, sampling, and synchronization functions. System software was developed with integrated AI functions able to identify and label interesting targets instantly. The system capability was demonstrated by an example application for rapid identification of five common foodborne bacteria. Using a machine learning model based on a linear support vector machine, a classification accuracy of 98.6% was achieved using Raman spectra collected from bacterial colonies grown on nutrient nonselective agar in Petri dishes. The system is compact and portable (30×45×35 cm3) that can be used for biological and chemical food safety inspection in regulatory and industrial applications.
Outbreaks of foodborne illness due to pathogenic bacteria have been identified worldwide and have been associated with the consumption of contaminated agricultural products. The main objective of this research is to develop a rapid method for pathogen detection using Raman spectroscopy (RS). Direct detection in culture media and surface-enhanced Raman scattering (SERS) were used to identify Escherichia coli, Escherichia coli O157:H7, Salmonella spp., Listeria monocytogenes, Staphylococcus aureus, Bacillus cereus, and Bacillus thuringiensis. Bacterial isolates were cultured on selective media for 24 h at 37°C or 30°C and then tested with RS. A portable 785 nm point-scan Raman system was developed at ARS USDA for this purpose and multiple laser current and exposure times were tested to establish optimal conditions. Seven nanoparticles and three substrates were evaluated for optimal bacterial detection using label-free SERS. Raman peaks were very weak in direct detection and the bacteria were not identified using direct or SERS approaches. However, two gold nanoparticles consistently showed SERS peaks at 878.9, 1086, and 1455 cm-1 and relative differences in Raman intensity were observed among each of the tested bacteria. This method can be used to lay a foundation for future research such as SERS combined with chemometric analysis and label-based SERS approaches.
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