Machine Learning-Based Software for Predicting <i>Pseudomonas</i> spp. Growth Dynamics in Culture Media
In predictive microbiology, both primary and secondary models are widely used to estimate microbial growth, often applied through two-step or one-step modelling approaches. This study focused on developing a tool to predict the growth of <i>Pseudomonas</i> spp., a prominent bacterial gen...
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2024-11-01
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| author | Fatih Tarlak |
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| description | In predictive microbiology, both primary and secondary models are widely used to estimate microbial growth, often applied through two-step or one-step modelling approaches. This study focused on developing a tool to predict the growth of <i>Pseudomonas</i> spp., a prominent bacterial genus in food spoilage, by applying machine learning regression models, including Support Vector Regression (SVR), Random Forest Regression (RFR) and Gaussian Process Regression (GPR). The key environmental factors—temperature, water activity, and pH—served as predictor variables to model the growth of <i>Pseudomonas</i> spp. in culture media. To assess model performance, these machine learning approaches were compared with traditional models, namely the Gompertz, Logistic, Baranyi, and Huang models, using statistical indicators such as the adjusted coefficient of determination (R<sup>2</sup><sub>adj</sub>) and root mean square error (RMSE). Machine learning models provided superior accuracy over traditional approaches, with R<sup>2</sup><sub>adj</sub> values from 0.834 to 0.959 and RMSE values between 0.005 and 0.010, showcasing their ability to handle complex growth patterns more effectively. GPR emerged as the most accurate model for both training and testing datasets. In external validation, additional statistical indices (bias factor, <i>B</i><sub>f</sub>: 0.998 to 1.047; accuracy factor, <i>A</i><sub>f</sub>: 1.100 to 1.167) further supported GPR as a reliable alternative for microbial growth prediction. This machine learning-driven approach bypasses the need for the secondary modelling step required in traditional methods, highlighting its potential as a robust tool in predictive microbiology. |
| format | Article |
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| institution | OA Journals |
| issn | 2075-1729 |
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| spelling | doaj-art-3418a75baf164a4aa35c521659973d652025-08-20T02:04:58ZengMDPI AGLife2075-17292024-11-011411149010.3390/life14111490Machine Learning-Based Software for Predicting <i>Pseudomonas</i> spp. Growth Dynamics in Culture MediaFatih Tarlak0Department of Bioengineering, Gebze Technical University, Gebze 41400, Kocaeli, TurkeyIn predictive microbiology, both primary and secondary models are widely used to estimate microbial growth, often applied through two-step or one-step modelling approaches. This study focused on developing a tool to predict the growth of <i>Pseudomonas</i> spp., a prominent bacterial genus in food spoilage, by applying machine learning regression models, including Support Vector Regression (SVR), Random Forest Regression (RFR) and Gaussian Process Regression (GPR). The key environmental factors—temperature, water activity, and pH—served as predictor variables to model the growth of <i>Pseudomonas</i> spp. in culture media. To assess model performance, these machine learning approaches were compared with traditional models, namely the Gompertz, Logistic, Baranyi, and Huang models, using statistical indicators such as the adjusted coefficient of determination (R<sup>2</sup><sub>adj</sub>) and root mean square error (RMSE). Machine learning models provided superior accuracy over traditional approaches, with R<sup>2</sup><sub>adj</sub> values from 0.834 to 0.959 and RMSE values between 0.005 and 0.010, showcasing their ability to handle complex growth patterns more effectively. GPR emerged as the most accurate model for both training and testing datasets. In external validation, additional statistical indices (bias factor, <i>B</i><sub>f</sub>: 0.998 to 1.047; accuracy factor, <i>A</i><sub>f</sub>: 1.100 to 1.167) further supported GPR as a reliable alternative for microbial growth prediction. This machine learning-driven approach bypasses the need for the secondary modelling step required in traditional methods, highlighting its potential as a robust tool in predictive microbiology.https://www.mdpi.com/2075-1729/14/11/1490software development<i>Pseudomonas</i> spp.machine learningtraditional modelling |
| spellingShingle | Fatih Tarlak Machine Learning-Based Software for Predicting <i>Pseudomonas</i> spp. Growth Dynamics in Culture Media Life software development <i>Pseudomonas</i> spp. machine learning traditional modelling |
| title | Machine Learning-Based Software for Predicting <i>Pseudomonas</i> spp. Growth Dynamics in Culture Media |
| title_full | Machine Learning-Based Software for Predicting <i>Pseudomonas</i> spp. Growth Dynamics in Culture Media |
| title_fullStr | Machine Learning-Based Software for Predicting <i>Pseudomonas</i> spp. Growth Dynamics in Culture Media |
| title_full_unstemmed | Machine Learning-Based Software for Predicting <i>Pseudomonas</i> spp. Growth Dynamics in Culture Media |
| title_short | Machine Learning-Based Software for Predicting <i>Pseudomonas</i> spp. Growth Dynamics in Culture Media |
| title_sort | machine learning based software for predicting i pseudomonas i spp growth dynamics in culture media |
| topic | software development <i>Pseudomonas</i> spp. machine learning traditional modelling |
| url | https://www.mdpi.com/2075-1729/14/11/1490 |
| work_keys_str_mv | AT fatihtarlak machinelearningbasedsoftwareforpredictingipseudomonasisppgrowthdynamicsinculturemedia |