期刊
INTERNATIONAL JOURNAL OF FOOD SCIENCE AND TECHNOLOGY
卷 56, 期 2, 页码 563-572出版社
WILEY
DOI: 10.1111/ijfs.14733
关键词
Artificial neural networks; Escherichia coli; meat; models; non-thermal inactivation; pulsed UV light; regression
资金
- USDA National Institute of Food and Agriculture [1001168, 1015282, 1890]
This study investigated the efficacy of pulsed UV light (PUVL) in inactivating Escherichia coli K12 on goat meat and beef surfaces, with PUVL showing better effectiveness on beef than goat meat. Reduction of Escherichia coli K12 increased significantly with longer treatment time and closer distance from the light source, and predictive models using artificial neural networks and regression effectively described the inactivation process. These predictive models can be valuable for process optimization in the meat industry.
The objective of this study was to investigate the efficacy of pulsed UV light (PUVL) in inactivatingEscherichia coliK12 on goat meat and beef surfaces. Inactivation studies were conducted for 5 to 60 s at three distances from the light source (4.47, 8.28 and 12.09 cm) in the PUVL chamber. Predictive models using regression and artificial neural networks (ANN) were developed to quantify log reductions. Pulsed UV light was more effective on beef than goat meat. Maximum log reductions of 1.66 and 1.74 CFU mL(-1)rinse solution were achieved on goat meat and beef, respectively, at 4.47 cm distance for 60 s.Escherichia coliK12 reduction increased significantly with increasing treatment time and closer distance from the light source. In general, both ANN and regression models effectively described inactivation ofE. coliK12. Predictive models describing PUVL inactivation kinetics ofE. coliK12 can be used for process optimisation in meat industry.
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