1
|
McCoy Sanders J, Alarcon V, Marquis G, Tabb A, Van Kessel JA, Sonnier J, Haley BJ, Baek I, Qin J, Kim M, Vasefi F, Sokolov S, Hellberg RS. Inactivation of Escherichia coli, Salmonella enterica, and Listeria monocytogenes using the Contamination Sanitization Inspection and Disinfection (CSI-D) device. Heliyon 2024; 10:e30490. [PMID: 38726110 PMCID: PMC11079081 DOI: 10.1016/j.heliyon.2024.e30490] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/19/2023] [Revised: 04/26/2024] [Accepted: 04/28/2024] [Indexed: 05/12/2024] Open
Abstract
The Contamination Sanitization Inspection and Disinfection (CSI-D) device is a handheld fluorescence-based imaging system designed to disinfect food contact surfaces using ultraviolet-C (UVC) illumination. This study aimed to determine the optimal CSI-D parameters (i.e., UVC exposure time and intensity) for the inactivation of the following foodborne bacteria plated on non-selective media: generic Escherichia coli (indicator organism) and the pathogens enterohemorrhagic E. coli, enterotoxigenic E. coli, Salmonella enterica, and Listeria monocytogenes. Each bacterial strain was spread-plated on non-selective agar and exposed to high-intensity (10 mW/cm2) or low-intensity (5 mW/cm2) UVC for 1-5 s. Control plates were not exposed to UVC. The plates were incubated overnight at 37 °C and then enumerated. Three trials for each bacterial strain were conducted. Statistical analysis was carried out to determine if there were significant differences in bacterial growth between UVC intensities and exposure times. Overall, exposure to low or high intensity for 3-5 s resulted in consistent inhibition of bacterial growth, with reductions of 99.9-100 % for E. coli, 96.8-100 % for S. enterica, and 99.2-100 % for L. monocytogenes. The 1 s exposure time showed inconsistent results, with a 66.0-100 % reduction in growth depending on the intensity and bacterial strain. When the results for all strains within each species were combined, the 3-5 s exposure times showed significantly greater (p < 0.05) growth inhibition than the 1 s exposure time. However, there were no significant differences (p > 0.05) in growth inhibition between the high and low UVC intensities. The results of this study show that, in pure culture conditions, exposure to UVC with the CSI-D device for ≥3 s is required to achieve consistent reduction of E. coli, S. enterica, and L. monocytogenes.
Collapse
Affiliation(s)
| | - Vanessa Alarcon
- Food Science Program, Chapman University, One University Drive, Orange, CA, 92866, USA
| | - Grace Marquis
- Food Science Program, Chapman University, One University Drive, Orange, CA, 92866, USA
| | - Amanda Tabb
- Food Science Program, Chapman University, One University Drive, Orange, CA, 92866, USA
| | - Jo Ann Van Kessel
- Environmental Microbial and Food Safety Laboratory, USDA-Agricultural Research Service, Beltsville, MD, 20705, USA
| | - Jakeitha Sonnier
- Environmental Microbial and Food Safety Laboratory, USDA-Agricultural Research Service, Beltsville, MD, 20705, USA
| | - Bradd J. Haley
- Environmental Microbial and Food Safety Laboratory, USDA-Agricultural Research Service, Beltsville, MD, 20705, USA
| | - Insuck Baek
- Environmental Microbial and Food Safety Laboratory, USDA-Agricultural Research Service, Beltsville, MD, 20705, USA
| | - Jianwei Qin
- Environmental Microbial and Food Safety Laboratory, USDA-Agricultural Research Service, Beltsville, MD, 20705, USA
| | - Moon Kim
- Environmental Microbial and Food Safety Laboratory, USDA-Agricultural Research Service, Beltsville, MD, 20705, USA
| | | | | | - Rosalee S. Hellberg
- Food Science Program, Chapman University, One University Drive, Orange, CA, 92866, USA
| |
Collapse
|
2
|
Nath P, Mahtaba KR, Ray A. Fluorescence-Based Portable Assays for Detection of Biological and Chemical Analytes. SENSORS (BASEL, SWITZERLAND) 2023; 23:s23115053. [PMID: 37299780 DOI: 10.3390/s23115053] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Received: 04/26/2023] [Revised: 05/16/2023] [Accepted: 05/22/2023] [Indexed: 06/12/2023]
Abstract
Fluorescence-based detection techniques are part of an ever-expanding field and are widely used in biomedical and environmental research as a biosensing tool. These techniques have high sensitivity, selectivity, and a short response time, making them a valuable tool for developing bio-chemical assays. The endpoint of these assays is defined by changes in fluorescence signal, in terms of its intensity, lifetime, and/or shift in spectrum, which is monitored using readout devices such as microscopes, fluorometers, and cytometers. However, these devices are often bulky, expensive, and require supervision to operate, which makes them inaccessible in resource-limited settings. To address these issues, significant effort has been directed towards integrating fluorescence-based assays into miniature platforms based on papers, hydrogels, and microfluidic devices, and to couple these assays with portable readout devices like smartphones and wearable optical sensors, thereby enabling point-of-care detection of bio-chemical analytes. This review highlights some of the recently developed portable fluorescence-based assays by discussing the design of fluorescent sensor molecules, their sensing strategy, and the fabrication of point-of-care devices.
Collapse
Affiliation(s)
- Peuli Nath
- Department of Physics and Astronomy, University of Toledo, Toledo, OH 43606, USA
| | - Kazi Ridita Mahtaba
- Department of Physics and Astronomy, University of Toledo, Toledo, OH 43606, USA
| | - Aniruddha Ray
- Department of Physics and Astronomy, University of Toledo, Toledo, OH 43606, USA
| |
Collapse
|
3
|
Shin S, Dowden B, Doh IJ, Rajwa B, Bae E, Robinson JP. Surface Environment and Energy Density Effects on the Detection and Disinfection of Microorganisms Using a Portable Instrument. SENSORS (BASEL, SWITZERLAND) 2023; 23:2135. [PMID: 36850732 PMCID: PMC9968048 DOI: 10.3390/s23042135] [Citation(s) in RCA: 1] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Figures] [Subscribe] [Scholar Register] [Received: 01/19/2023] [Revised: 02/08/2023] [Accepted: 02/13/2023] [Indexed: 06/18/2023]
Abstract
Real-time detection and disinfection of foodborne pathogens are important for preventing foodborne outbreaks and for maintaining a safe environment for consumers. There are numerous methods for the disinfection of hazardous organisms, including heat treatment, chemical reaction, filtration, and irradiation. This report evaluated a portable instrument to validate its simultaneous detection and disinfection capability in typical laboratory situations. In this challenging study, three gram-negative and two gram-positive microorganisms were used. For the detection of contamination, inoculations of various concentrations were dispensed on three different surface types to estimate the performance for minimum-detectable cell concentration. Inoculations higher than 103~104 CFU/mm2 and 0.15 mm of detectable contaminant size were estimated to generate a sufficient level of fluorescence signal. The evaluation of disinfection efficacy was conducted on three distinct types of surfaces, with the energy density of UVC light (275-nm) ranging from 4.5 to 22.5 mJ/cm2 and the exposure time varying from 1 to 5 s. The study determined the optimal energy dose for each of the microorganisms species. In addition, surface characteristics may also be an important factor that results in different inactivation efficacy. These results demonstrate that the proposed portable device could serve as an in-field detection and disinfection unit in various environments, and provide a more efficient and user-friendly way of performing disinfection on large surface areas.
Collapse
Affiliation(s)
- Sungho Shin
- Department of Basic Medical Sciences, Purdue University, West Lafayette, IN 47907, USA
| | - Brianna Dowden
- Department of Basic Medical Sciences, Purdue University, West Lafayette, IN 47907, USA
| | - Iyll-Joon Doh
- Department of Basic Medical Sciences, Purdue University, West Lafayette, IN 47907, USA
| | - Bartek Rajwa
- Bindley Bioscience Center, Purdue University, West Lafayette, IN 47907, USA
| | - Euiwon Bae
- School of Mechanical Engineering, Purdue University, West Lafayette, IN 47907, USA
| | - J. Paul Robinson
- Department of Basic Medical Sciences, Purdue University, West Lafayette, IN 47907, USA
- Weldon School of Biomedical Engineering, Purdue University, West Lafayette, IN 47907, USA
| |
Collapse
|
4
|
Combining deep learning and fluorescence imaging to automatically identify fecal contamination on meat carcasses. Sci Rep 2022; 12:2392. [PMID: 35165330 PMCID: PMC8844077 DOI: 10.1038/s41598-022-06379-1] [Citation(s) in RCA: 2] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/12/2021] [Accepted: 01/25/2022] [Indexed: 12/26/2022] Open
Abstract
Food safety and foodborne diseases are significant global public health concerns. Meat and poultry carcasses can be contaminated by pathogens like E. coli and salmonella, by contact with animal fecal matter and ingesta during slaughter and processing. Since fecal matter and ingesta can host these pathogens, detection, and excision of contaminated regions on meat surfaces is crucial. Fluorescence imaging has proven its potential for the detection of fecal residue but requires expertise to interpret. In order to be used by meat cutters without special training, automated detection is needed. This study used fluorescence imaging and deep learning algorithms to automatically detect and segment areas of fecal matter in carcass images using EfficientNet-B0 to determine which meat surface images showed fecal contamination and then U-Net to precisely segment the areas of contamination. The EfficientNet-B0 model achieved a 97.32% accuracy (precision 97.66%, recall 97.06%, specificity 97.59%, F-score 97.35%) for discriminating clean and contaminated areas on carcasses. U-Net segmented areas with fecal residue with an intersection over union (IoU) score of 89.34% (precision 92.95%, recall 95.84%, specificity 99.79%, F-score 94.37%, and AUC 99.54%). These results demonstrate that the combination of deep learning and fluorescence imaging techniques can improve food safety assurance by allowing the industry to use CSI-D fluorescence imaging to train employees in trimming carcasses as part of their Hazard Analysis Critical Control Point zero-tolerance plan.
Collapse
|