An intelligent based prediction of microbial behaviour in beef

Yücel, Özgün; Tarlak, Fatih

doi:10.1016/j.foodcont.2023.109665

Cited by 4 publications

(4 citation statements)

References 22 publications

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“…The primary objective of the work performed by Yucel and Tarlak, [79] was to formulate distinct machine-learning-based regression methodologies, specifically decision tree regression (DTR), generalised additive model regression (GAMR), and random forest regression (RFR), for the anticipation of bacterial populations in beef. To achieve this goal, a dataset comprising 2654 bacterial data points pertaining to Listeria monocytogenes, Escherichia coli, and Pseudomonas spp., the most extensively investigated bacterial genera in beef, was procured from the ComBase database (www.combase.cc, accessed on 3 October 2023).…”

Section: Shelf Life Prediction With Machine Learning Modelling Approachmentioning

confidence: 99%

The Use of Predictive Microbiology for the Prediction of the Shelf Life of Food Products

Tarlak

2023

Foods

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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.

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Section: Shelf Life Prediction With Machine Learning Modelling Approachmentioning

confidence: 99%

The Use of Predictive Microbiology for the Prediction of the Shelf Life of Food Products

Tarlak

2023

Foods

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“…The radial basis function kernel is commonly used for support vector regression. However, its effectiveness decreases with noise in the dataset [12,14].…”

Section: Modellingmentioning

confidence: 99%

“…However, DTR is inadequate for regression and is better suited for classification [17,18]. Random forest regression (RFR) fits a large number of classification trees to a dataset and combines their predictions to produce a final predictive model [12,19]. RFR is effective in finding nonlinear relationships in the training data and generalises well to new data.…”

Section: Modellingmentioning

confidence: 99%

“…The authors employed both traditional models, such as the re-parametrised Gompertz, Baranyi, and Huang models, as well as a machine learning-based regression model. Yücel and Tarlak [12] developed a prediction tool to describe the behaviours of Listeria monocytogenes, Escherichia coli, and Pseudomonas spp., specifically in beef. Collectively, all these studies highlighted the potential of machine learning models in predicting the behaviour of bacterial populations.…”

Section: Introductionmentioning

confidence: 99%

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Prediction of Pseudomonas spp. Population in Food Products and Culture Media Using Machine Learning-Based Regression Methods

Tarlak

Yücel

2023

Life

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Machine learning approaches are alternative modelling techniques to traditional modelling equations used in predictive food microbiology and utilise algorithms to analyse large datasets that contain information about microbial growth or survival in various food matrices. These approaches leverage the power of algorithms to extract insights from the data and make predictions regarding the behaviour of microorganisms in different food environments. The objective of this study was to apply various machine learning-based regression methods, including support vector regression (SVR), Gaussian process regression (GPR), decision tree regression (DTR), and random forest regression (RFR), to estimate bacterial populations. In order to achieve this, a total of 5618 data points for Pseudomonas spp. present in food products (beef, pork, and poultry) and culture media were gathered from the ComBase database. The machine learning algorithms were applied to predict the growth or survival behaviour of Pseudomonas spp. in food products and culture media by considering predictor variables such as temperature, salt concentration, water activity, and acidity. The suitability of the algorithms was assessed using statistical measures such as coefficient of determination (R2), root mean square error (RMSE), bias factor (Bf), and accuracy (Af). Each of the regression algorithms showed appropriate estimation capabilities with R2 ranging from 0.886 to 0.913, RMSE from 0.724 to 0.899, Bf from 1.012 to 1.020, and Af from 1.086 to 1.101 for each food product and culture medium. Since the predictive capability of RFR was the best among the algorithms, externally collected data from the literature were used for RFR. The external validation process showed statistical indices of Bf ranging from 0.951 to 1.040 and Af ranging from 1.091 to 1.130, indicating that RFR can be used for predicting the survival and growth of microorganisms in food products. Therefore, machine learning approaches can be considered as an alternative to conventional modelling methods in predictive microbiology. However, it is important to highlight that the prediction power of the machine learning regression method directly depends on the dataset size, and it requires a large dataset to be employed for modelling. Therefore, the modelling work of this study can only be used for the prediction of Pseudomonas spp. in specific food products (beef, pork, and poultry) and culture medium with certain conditions where a large dataset is available.

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Quantification and modeling of disinfectant inactivation of Listeria monocytogenes biofilms on stainless steel surfaces

Zhen,

Hu,

Xiong

et al. 2024

Food Bioscience

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An intelligent based prediction of microbial behaviour in beef

Cited by 4 publications

References 22 publications

The Use of Predictive Microbiology for the Prediction of the Shelf Life of Food Products

The Use of Predictive Microbiology for the Prediction of the Shelf Life of Food Products

Prediction of Pseudomonas spp. Population in Food Products and Culture Media Using Machine Learning-Based Regression Methods

Quantification and modeling of disinfectant inactivation of Listeria monocytogenes biofilms on stainless steel surfaces

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