scholarly journals Effect of water activity on inactivation of Listeria monocytogenes using gaseous chlorine dioxide – A kinetic analysis

2021 ◽  
Vol 95 ◽  
pp. 103707
Author(s):  
Hyeon Woo Park ◽  
Guoying Chen ◽  
Cheng-An Hwang ◽  
Lihan Huang
2005 ◽  
Vol 68 (6) ◽  
pp. 1176-1187 ◽  
Author(s):  
KAYE V. SY ◽  
MELINDA B. MURRAY ◽  
M. DAVID HARRISON ◽  
LARRY R. BEUCHAT

Gaseous chlorine dioxide (ClO2) was evaluated for effectiveness in killing Salmonella, Escherichia coli O157:H7, and Listeria monocytogenes on fresh-cut lettuce, cabbage, and carrot and Salmonella, yeasts, and molds on apples, peaches, tomatoes, and onions. Inoculum (100 μl, ca. 6.8 log CFU) containing five serotypes of Salmonella enterica, five strains of E. coli O157:H7, or five strains of L. monocytogenes was deposited on the skin and cut surfaces of fresh-cut vegetables, dried for 30 min at 22°C, held for 20 h at 4°C, and then incubated for 30 min at 22°C before treatment. The skin surfaces of apples, peaches, tomatoes, and onions were inoculated with 100 μl of a cell suspension (ca. 8.0 log CFU) containing five serotypes of Salmonella, and inoculated produce was allowed to dry for 20 to 22 h at 22°C before treatment. Treatment with ClO2 at 4.1 mg/liter significantly (α = 0.05) reduced the population of foodborne pathogens on all produce. Reductions resulting from this treatment were 3.13 to 4.42 log CFU/g for fresh-cut cabbage, 5.15 to 5.88 log CFU/g for fresh-cut carrots, 1.53 to 1.58 log CFU/g for fresh-cut lettuce, 4.21 log CFU per apple, 4.33 log CFU per tomato, 1.94 log CFU per onion, and 3.23 log CFU per peach. The highest reductions in yeast and mold populations resulting from the same treatment were 1.68 log CFU per apple and 2.65 log CFU per peach. Populations of yeasts and molds on tomatoes and onions were not significantly reduced by treatment with 4.1 mg/liter ClO2. Substantial reductions in populations of pathogens on apples, tomatoes, and onions but not peaches or fresh-cut cabbage, carrot, and lettuce were achieved by treatment with gaseous ClO2 without markedly adverse effects on sensory qualities.


LWT ◽  
2021 ◽  
Vol 141 ◽  
pp. 110906
Author(s):  
Phillip Luu ◽  
Vijay Singh Chhetri ◽  
Marlene E. Janes ◽  
Joan M. King ◽  
Achyut Adhikari

2008 ◽  
Vol 124 (1) ◽  
pp. 21-26 ◽  
Author(s):  
Melinda M. Hayman ◽  
Gilles K. Kouassi ◽  
Ramaswamy C. Anantheswaran ◽  
John D. Floros ◽  
Stephen J. Knabel

2021 ◽  
Vol 11 (1) ◽  
Author(s):  
Satoko Hiura ◽  
Shige Koseki ◽  
Kento Koyama

AbstractIn predictive microbiology, statistical models are employed to predict bacterial population behavior in food using environmental factors such as temperature, pH, and water activity. As the amount and complexity of data increase, handling all data with high-dimensional variables becomes a difficult task. We propose a data mining approach to predict bacterial behavior using a database of microbial responses to food environments. Listeria monocytogenes, which is one of pathogens, population growth and inactivation data under 1,007 environmental conditions, including five food categories (beef, culture medium, pork, seafood, and vegetables) and temperatures ranging from 0 to 25 °C, were obtained from the ComBase database (www.combase.cc). We used eXtreme gradient boosting tree, a machine learning algorithm, to predict bacterial population behavior from eight explanatory variables: ‘time’, ‘temperature’, ‘pH’, ‘water activity’, ‘initial cell counts’, ‘whether the viable count is initial cell number’, and two types of categories regarding food. The root mean square error of the observed and predicted values was approximately 1.0 log CFU regardless of food category, and this suggests the possibility of predicting viable bacterial counts in various foods. The data mining approach examined here will enable the prediction of bacterial population behavior in food by identifying hidden patterns within a large amount of data.


2014 ◽  
Vol 42 (3) ◽  
pp. 322-331 ◽  
Author(s):  
Y.-A. Jeon ◽  
S. Lee ◽  
Y. Lee ◽  
H.-S. Lee ◽  
J.S. Sung ◽  
...  

2006 ◽  
Vol 108 (2) ◽  
pp. 188-195 ◽  
Author(s):  
A. Esteban ◽  
M.L. Abarca ◽  
M.R. Bragulat ◽  
F.J. Cabañes

Author(s):  
Xinyao Wei ◽  
Tushar Verma ◽  
Mary-Grace C. Danao ◽  
Monica A. Ponder ◽  
Jeyamkondan Subbiah

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