Predictive Model for <i>Listeria monocytogenes</i> in RTE Meats Using Exclusive Food Matrix Data
Post-processing contamination of <i>Listeria monocytogenes</i> has remained a major concern for the safety of ready-to-eat (RTE) meat products that are not reheated before consumption. Mathematical models are rapid and cost-effective tools to predict pathogen behavior, product shelf life...
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MDPI AG
2024-12-01
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| Series: | Foods |
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| author | N. A. Nanje Gowda Manjari Singh Gijs Lommerse Saurabh Kumar Eelco Heintz Jeyamkondan Subbiah |
| author_facet | N. A. Nanje Gowda Manjari Singh Gijs Lommerse Saurabh Kumar Eelco Heintz Jeyamkondan Subbiah |
| author_sort | N. A. Nanje Gowda |
| collection | DOAJ |
| description | Post-processing contamination of <i>Listeria monocytogenes</i> has remained a major concern for the safety of ready-to-eat (RTE) meat products that are not reheated before consumption. Mathematical models are rapid and cost-effective tools to predict pathogen behavior, product shelf life, and safety. The objective of this study was to develop and validate a comprehensive model to predict the <i>Listeria</i> growth rate in RTE meat products as a function of temperature, pH, water activity, nitrite, acetic, lactic, and propionic acids. The <i>Listeria</i> growth data in RTE food matrices, including RTE beef, pork, and poultry products (731 data sets), were collected from the literature and databases like ComBase. The growth parameters were estimated using the logistic-with-delay primary model. The good-quality growth rate data (<i>n</i> = 596, R<sup>2</sup> > 0.9) were randomly divided into 80% training (<i>n</i> = 480) and 20% testing (<i>n</i> = 116) datasets. The training growth rates were used to develop a secondary gamma model, followed by validation in testing data. The growth model’s performance was evaluated by comparing the predicted and observed growth rates. The goodness-of-fit parameter of the secondary model includes R<sup>2</sup> of 0.86 and RMSE of 0.06 (μ<sub>max</sub>) during the development stage. During validation, the gamma model with interaction included an RMSE of 0.074 (μ<sub>max</sub>), bias, and accuracy factor of 0.95 and 1.50, respectively. Overall, about 81.03% of the relative errors (RE) of the model’s predictions were within the acceptable simulation zone (RE ± 0.5 log CFU/h). In lag time model validation, predictions were 7% fail-dangerously biased, and the accuracy factor of 2.23 indicated that the lag time prediction is challenging. The model may be used to quantify the <i>Listeria</i> growth in naturally contaminated RTE meats. This model may be helpful in formulations, shelf-life assessment, and decision-making for the safety of RTE meat products. |
| format | Article |
| id | doaj-art-fed6b4bda7d944bf93ef728ee1d9d1b5 |
| institution | OA Journals |
| issn | 2304-8158 |
| language | English |
| publishDate | 2024-12-01 |
| publisher | MDPI AG |
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| series | Foods |
| spelling | doaj-art-fed6b4bda7d944bf93ef728ee1d9d1b52025-08-20T02:38:49ZengMDPI AGFoods2304-81582024-12-011323394810.3390/foods13233948Predictive Model for <i>Listeria monocytogenes</i> in RTE Meats Using Exclusive Food Matrix DataN. A. Nanje Gowda0Manjari Singh1Gijs Lommerse2Saurabh Kumar3Eelco Heintz4Jeyamkondan Subbiah5Department of Food Science, University of Arkansas Division of Agriculture, Fayetteville, AR 72204, USADepartment of Food Science, University of Arkansas Division of Agriculture, Fayetteville, AR 72204, USAFood Preservation and Protection, Kerry Taste & Nutrition, 6708 Wageningen, The NetherlandsFood Preservation and Protection, Kerry Taste & Nutrition, Beloit, WI 53511, USAFood Preservation and Protection, Kerry Taste & Nutrition, 6708 Wageningen, The NetherlandsDepartment of Food Science, University of Arkansas Division of Agriculture, Fayetteville, AR 72204, USAPost-processing contamination of <i>Listeria monocytogenes</i> has remained a major concern for the safety of ready-to-eat (RTE) meat products that are not reheated before consumption. Mathematical models are rapid and cost-effective tools to predict pathogen behavior, product shelf life, and safety. The objective of this study was to develop and validate a comprehensive model to predict the <i>Listeria</i> growth rate in RTE meat products as a function of temperature, pH, water activity, nitrite, acetic, lactic, and propionic acids. The <i>Listeria</i> growth data in RTE food matrices, including RTE beef, pork, and poultry products (731 data sets), were collected from the literature and databases like ComBase. The growth parameters were estimated using the logistic-with-delay primary model. The good-quality growth rate data (<i>n</i> = 596, R<sup>2</sup> > 0.9) were randomly divided into 80% training (<i>n</i> = 480) and 20% testing (<i>n</i> = 116) datasets. The training growth rates were used to develop a secondary gamma model, followed by validation in testing data. The growth model’s performance was evaluated by comparing the predicted and observed growth rates. The goodness-of-fit parameter of the secondary model includes R<sup>2</sup> of 0.86 and RMSE of 0.06 (μ<sub>max</sub>) during the development stage. During validation, the gamma model with interaction included an RMSE of 0.074 (μ<sub>max</sub>), bias, and accuracy factor of 0.95 and 1.50, respectively. Overall, about 81.03% of the relative errors (RE) of the model’s predictions were within the acceptable simulation zone (RE ± 0.5 log CFU/h). In lag time model validation, predictions were 7% fail-dangerously biased, and the accuracy factor of 2.23 indicated that the lag time prediction is challenging. The model may be used to quantify the <i>Listeria</i> growth in naturally contaminated RTE meats. This model may be helpful in formulations, shelf-life assessment, and decision-making for the safety of RTE meat products.https://www.mdpi.com/2304-8158/13/23/3948gamma modelRTE meatlag phase durationclean labelorganic acids |
| spellingShingle | N. A. Nanje Gowda Manjari Singh Gijs Lommerse Saurabh Kumar Eelco Heintz Jeyamkondan Subbiah Predictive Model for <i>Listeria monocytogenes</i> in RTE Meats Using Exclusive Food Matrix Data Foods gamma model RTE meat lag phase duration clean label organic acids |
| title | Predictive Model for <i>Listeria monocytogenes</i> in RTE Meats Using Exclusive Food Matrix Data |
| title_full | Predictive Model for <i>Listeria monocytogenes</i> in RTE Meats Using Exclusive Food Matrix Data |
| title_fullStr | Predictive Model for <i>Listeria monocytogenes</i> in RTE Meats Using Exclusive Food Matrix Data |
| title_full_unstemmed | Predictive Model for <i>Listeria monocytogenes</i> in RTE Meats Using Exclusive Food Matrix Data |
| title_short | Predictive Model for <i>Listeria monocytogenes</i> in RTE Meats Using Exclusive Food Matrix Data |
| title_sort | predictive model for i listeria monocytogenes i in rte meats using exclusive food matrix data |
| topic | gamma model RTE meat lag phase duration clean label organic acids |
| url | https://www.mdpi.com/2304-8158/13/23/3948 |
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