Kathryn Owen
Research Subjects
Kathryn Owen has a total of 17 co-authors. Here are the top co-authors by number of shared publications:
Amit Morey
2 shared publicationsRanju Kafle
2 shared publicationsZhen Jia
2 shared publicationsArabella Jones
1 shared publication- Boce Zhang
Department of Food Science and Human Nutrition, University of Florida, Gainesville, FL 32611, USA
1 shared publication Brenda Jovel Gonzalez
1 shared publication
Latest Publications
- Open Access
- 15 August 2026
- Published by Elsevier in Poultry Science
- Vol. 105 (11)
Growth and survival of heat-, cold-, and acid-stressed Salmonella Typhimurium and native bacteria on chicken wings during refrigerated storage and temperature abuse
- 15 August 2026
- Published by Elsevier in Poultry Science
- Vol. 105 (11)
Open AccessGrowth and survival of heat-, cold-, and acid-stressed Salmonella Typhimurium and native bacteria on chicken wings during refrigerated storage and temperature abuse
- Open Access
- 29 July 2026
- Published by Elsevier in SSRN Electronic Journal
Detection and differentiation of Salmonella serotypes in poultry meat using a machine learning-enabled paper-based chromogenic array sensor approach
Rapid and accurate detection of Salmonella serotypes associated with foodborne illness is critical for maintaining food safety. This study developed and evaluated the capability of a machine learning-enabled paper chromogenic array sensor (ML-PCA) approach for non-destructive, continuous, and simultaneous detection and differentiation of Salmonella enterica serotypes Salmonella Typhimurium (ST) and Salmonella Enteritidis (SE) in chicken breast in the presence of background microflora during storage at room temperature and refrigerated temperature (4°C). At room temperature, the ML-PCA approach achieved accuracies of 80–88% for detecting ST and SE in mono-serotype, dual-serotype, and serotype cocktail as early as 6 h. At 4°C, Salmonella serotypes were detected as early as 1 d, with testing accuracy exceeding 80%. These results demonstrated that the ML-PCA approach enables non-destructive, continuous, and simultaneous detection and differentiation of ST and SE on poultry meat. This methodology offers a potential tool for continuous Salmonella monitoring across the food system.
- 29 July 2026
- Published by Elsevier in SSRN Electronic Journal
Open AccessDetection and differentiation of Salmonella serotypes in poultry meat using a machine learning-enabled paper-based chromogenic array sensor approach
Rapid and accurate detection of Salmonella serotypes associated with foodborne illness is critical for maintaining food safety. This study developed and evaluated the capability of a machine learning-enabled paper chromogenic array sensor (ML-PCA) approach for non-destructive, continuous, and simultaneous detection and differentiation of Salmonella enterica serotypes Salmonella Typhimurium (ST) and Salmonella Enteritidis (SE) in chicken breast in the presence of background microflora during storage at room temperature and refrigerated temperature (4°C). At room temperature, the ML-PCA approach achieved accuracies of 80–88% for detecting ST and SE in mono-serotype, dual-serotype, and serotype cocktail as early as 6 h. At 4°C, Salmonella serotypes were detected as early as 1 d, with testing accuracy exceeding 80%. These results demonstrated that the ML-PCA approach enables non-destructive, continuous, and simultaneous detection and differentiation of ST and SE on poultry meat. This methodology offers a potential tool for continuous Salmonella monitoring across the food system.