Predictive Microbiology & Machine Learning

Predictive microbiology is a field that combines microbiology, mathematics, and statistics with the goal of developing models to characterise and predict microbe growth and inactivation under a variety of situations. Predictive microbiology is based on the idea that the responses of populations of microorganisms to environmental factors are repeatable, and that by thinking about environments in terms of identifiable dominating constraints, it is possible to predict those microorganisms' responses based on previous observations. Predictive microbiology, according to proponents, has numerous advantages in the field of food microbiology, and there is growing international interest.

Microbiologists are living in an era when high-throughput smart technologies are being used to capture biological data in unprecedented volumes. Applying computational tools to find useful information from these data is becoming an increasingly important skill requirement outside laboratory and field experiments. Machine learning (ML), a subfield of artificial intelligence (AI), is a well-established approach that is gaining traction in the field of microbiology. It has so far been applied to computationally intensive problems such as predicting drug targets and vaccine candidates, diagnosing infectious disease-causing microorganisms, classifying drug resistance to antimicrobial medicines, predicting disease outbreaks, and investigating microbial interactions.

• Microbiological Risk Assessment
• Predictive Models
• Qualitative and Quantitative Tools
• Microbiological Quality
• Statistics
• Big Data Analysis
• Machine Learning
• Artificial Intelligence