The Use of Predictive Microbiology for the Prediction of the Shelf Life of Food Products
Microbial shelf life refers to the duration of time during which a food product remains safe for consumption in terms of its microbiological quality. Predictive microbiology is a field of science that focuses on using mathematical models and computational techniques to predict the growth, survival,...
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| Format: | Article |
| Language: | English |
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MDPI AG
2023-12-01
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| Series: | Foods |
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| Online Access: | https://www.mdpi.com/2304-8158/12/24/4461 |
| _version_ | 1797381025826865152 |
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| author | Fatih Tarlak |
| author_facet | Fatih Tarlak |
| author_sort | Fatih Tarlak |
| collection | DOAJ |
| description | Microbial shelf life refers to the duration of time during which a food product remains safe for consumption in terms of its microbiological quality. Predictive microbiology is a field of science that focuses on using mathematical models and computational techniques to predict the growth, survival, and behaviour of microorganisms in food and other environments. This approach allows researchers, food producers, and regulatory bodies to assess the potential risks associated with microbial contamination and spoilage, enabling informed decisions to be made regarding food safety, quality, and shelf life. Two-step and one-step modelling approaches are modelling techniques with primary and secondary models being used, while the machine learning approach does not require using primary and secondary models for describing the quantitative behaviour of microorganisms, leading to the spoilage of food products. This comprehensive review delves into the various modelling techniques that have found applications in predictive food microbiology for estimating the shelf life of food products. By examining the strengths, limitations, and implications of the different approaches, this review provides an invaluable resource for researchers and practitioners seeking to enhance the accuracy and reliability of microbial shelf life predictions. Ultimately, a deeper understanding of these techniques promises to advance the domain of predictive food microbiology, fostering improved food safety practices, reduced waste, and heightened consumer confidence. |
| first_indexed | 2024-03-08T20:46:19Z |
| format | Article |
| id | doaj.art-05ee3ba0b0b8476c8664850483fa2f8a |
| institution | Directory Open Access Journal |
| issn | 2304-8158 |
| language | English |
| last_indexed | 2024-03-08T20:46:19Z |
| publishDate | 2023-12-01 |
| publisher | MDPI AG |
| record_format | Article |
| series | Foods |
| spelling | doaj.art-05ee3ba0b0b8476c8664850483fa2f8a2023-12-22T14:08:51ZengMDPI AGFoods2304-81582023-12-011224446110.3390/foods12244461The Use of Predictive Microbiology for the Prediction of the Shelf Life of Food ProductsFatih Tarlak0Department of Nutrition and Dietetics, Faculty of Health Sciences, Istanbul Gedik University, Kartal, Istanbul 34876, TurkeyMicrobial shelf life refers to the duration of time during which a food product remains safe for consumption in terms of its microbiological quality. Predictive microbiology is a field of science that focuses on using mathematical models and computational techniques to predict the growth, survival, and behaviour of microorganisms in food and other environments. This approach allows researchers, food producers, and regulatory bodies to assess the potential risks associated with microbial contamination and spoilage, enabling informed decisions to be made regarding food safety, quality, and shelf life. Two-step and one-step modelling approaches are modelling techniques with primary and secondary models being used, while the machine learning approach does not require using primary and secondary models for describing the quantitative behaviour of microorganisms, leading to the spoilage of food products. This comprehensive review delves into the various modelling techniques that have found applications in predictive food microbiology for estimating the shelf life of food products. By examining the strengths, limitations, and implications of the different approaches, this review provides an invaluable resource for researchers and practitioners seeking to enhance the accuracy and reliability of microbial shelf life predictions. Ultimately, a deeper understanding of these techniques promises to advance the domain of predictive food microbiology, fostering improved food safety practices, reduced waste, and heightened consumer confidence.https://www.mdpi.com/2304-8158/12/24/4461modellingmicrobial growthspoilagemachine learning approach |
| spellingShingle | Fatih Tarlak The Use of Predictive Microbiology for the Prediction of the Shelf Life of Food Products Foods modelling microbial growth spoilage machine learning approach |
| title | The Use of Predictive Microbiology for the Prediction of the Shelf Life of Food Products |
| title_full | The Use of Predictive Microbiology for the Prediction of the Shelf Life of Food Products |
| title_fullStr | The Use of Predictive Microbiology for the Prediction of the Shelf Life of Food Products |
| title_full_unstemmed | The Use of Predictive Microbiology for the Prediction of the Shelf Life of Food Products |
| title_short | The Use of Predictive Microbiology for the Prediction of the Shelf Life of Food Products |
| title_sort | use of predictive microbiology for the prediction of the shelf life of food products |
| topic | modelling microbial growth spoilage machine learning approach |
| url | https://www.mdpi.com/2304-8158/12/24/4461 |
| work_keys_str_mv | AT fatihtarlak theuseofpredictivemicrobiologyforthepredictionoftheshelflifeoffoodproducts AT fatihtarlak useofpredictivemicrobiologyforthepredictionoftheshelflifeoffoodproducts |