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Prediction of Pseudomonas spp. Population in Food Products and Culture Media Using Machine Learning-Based Regression
1Department of Nutrition and Dietetics, Istanbul Gedik University, Kartal, Istanbul 34876, Turkey.
Life (Basel, Switzerland)
|July 29, 2023
Summary
Machine learning models accurately predict bacterial populations in foods. Random Forest Regression showed the best predictive performance, offering an alternative to traditional methods for microbial growth and survival analysis.
Area of Science:
- Predictive microbiology
- Food safety
- Computational biology
Background:
- Traditional modeling equations are used in predictive food microbiology.
- Machine learning (ML) offers alternative data-driven approaches.
- ML algorithms analyze large datasets for microbial growth and survival insights.
Purpose of the Study:
- To apply ML-based regression methods for estimating bacterial populations.
- To evaluate Support Vector Regression (SVR), Gaussian Process Regression (GPR), Decision Tree Regression (DTR), and Random Forest Regression (RFR).
- To predict the growth or survival of *Pseudomonas* spp. in food products and culture media.
Main Methods:
- Gathered 5618 data points for *Pseudomonas* spp. from the ComBase database.
- Utilized predictor variables: temperature, salt concentration, water activity, and acidity.
- Assessed algorithm suitability using R², RMSE, Bf, and Af statistical measures.
Main Results:
- All tested regression algorithms demonstrated appropriate estimation capabilities.
- R² values ranged from 0.886 to 0.913; RMSE from 0.724 to 0.899.
- Random Forest Regression (RFR) exhibited the best predictive performance, validated with external data.
Conclusions:
- Machine learning approaches are viable alternatives to conventional modeling in predictive microbiology.
- RFR is effective for predicting microbial survival and growth in food products.
- ML model prediction power is dependent on large dataset availability for specific food matrices.
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