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

Summary

Food companies need to know how long products stay fresh and safe to eat. This review explains different scientific methods for predicting when food will spoil based on microbial growth. It compares traditional mathematical models with newer computer-based machine learning approaches, showing that newer methods can be more accurate and efficient for determining how long foods can be stored safely.

Background

Microbial shelf life refers to the duration a food product remains safe for consumption based on microbiological quality. Predictive microbiology uses mathematical models and computational techniques to predict microbial growth, survival, and behavior in food environments. This approach enables assessment of contamination risks and informs decisions regarding food safety and shelf life.

Objective

This review examines various modelling techniques used in predictive food microbiology for estimating shelf life of food products. The study compares two-step modelling, one-step modelling, and machine learning approaches, evaluating their strengths, limitations, and applications in predicting microbial spoilage.

Results

One-step modelling approaches demonstrated superior predictive accuracy compared to two-step approaches across multiple primary models, avoiding error accumulation. Machine learning models showed enhanced precision with R² values ranging from 0.931 to 0.960 and RMSE values from 0.154 to 0.692. The Baranyi model consistently performed well in traditional approaches while random forest regression excelled in machine learning applications.

Conclusion

Predictive food microbiology is essential for ensuring food safety, reducing waste, and maintaining economic viability. The one-step modelling approach is preferable to two-step methods for detailed analysis and precise results. Machine learning models offer promising alternatives that bypass secondary model steps, providing efficient simulation methodologies for shelf life prediction.
  • Published in:Foods,
  • Study Type:Review,
  • Source: PMID: 38137265
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