Predictive Microbiology & Modeling

Predictive Microbiology

• Predictive microbiology involves techniques for examining food microbiologically, using rapid methods, and detecting food spoilage microorganisms.

Predictive Modeling

• Modeling microbial behavior in food is essential for assessing and mitigating risks in the food industry.
• Models are based on mathematical algorithms and empirical data, providing insights into microorganism behavior in various food products and processing conditions.
• Models must be validated for accuracy and applicability.
• Integrating predictive modeling into food safety and hazard analysis helps forecast microbial growth, identify control points, and optimize preventive measures.

Microorganisms in Food

• Microorganisms in food include bacteria, fungi, viruses, and protozoa, all relevant to food quality and safety.
• Microbial spoilage leads to food deterioration and health risks.
• Food fermentations by microbial starter cultures improve product characteristics, health benefits, and shelf-life.
• Growth and transmission of pathogenic microbes, toxin production, and cross-contamination compromise food safety.
• Microbial growth is determined by intrinsic and extrinsic factors.
• Diverse ingredients/processes mean almost any bacterium could be present, altering food qualities if they multiply.
• Hygienic measures based on hazard analysis and critical control point (HACCP) principles are often referred to as "microbiological criteria."

Predictive Mathematical Modeling

• The modeling offers a unique lens to anticipate and understand microorganism behavior in food.
• Researchers develop models using mathematical and statistical techniques to encapsulate the interplay between microorganisms and their environments.
• Models consider environmental conditions, nutrient availability, and microbial characteristics, simulating growth patterns over time.
• Predictive modeling enhances understanding of microbial behavior and empowers the ability to forecast potential hazards and adopt preemptive measures.

Integration of Predictive Microbiology in Food Safety

• Food safety ensures food is safe for consumption and free from contaminants or hazards.
• Predictive microbiology is crucial in hazard analysis, Hazard Analysis and Critical Control Points (HACCP), Quantitative Microbial Risk Assessment (QMRA), and addressing emerging risks.
• Mathematical models and statistical tools used forecast the behavior of microorganisms in food and give valuable insights for risk management.
• Hazard analysis uses predictive microbiology for proactive assessment of potential microbial hazards in food products.
• Predictive models allow food safety professionals anticipate pathogen/spoilage microorganism growth and behavior, helping in identifying potential hazards and implement preventive measures to ensure the food supply chain's safety.

Quantitative Microbial Risk Assessment

• (QMRA) is a comprehensive tool evaluating health risks from microbial hazards in food.
• Predictive microbiology contributes to QMRA by providing quantitative data on microbial behavior, survival, and growth.
• This data serves as input for risk assessment models, allowing accurate estimation of the probability and consequences of microbial contamination.
• QMRA aids in establishing science-based food safety standards and regulations.
• Predictive microbiology evaluates the impact of novel technologies on microbial behavior, like new preservation techniques/innovative packaging.
• This allows for the development of targeted safety measures to mitigate risks from emerging technologies.

Alternative Ingredients and their Impact

• Predictive microbiology supports the assessment of alternative ingredients and their impact on microbial safety.
• Predictive models helps understand how these ingredients interact with microorganisms, influencing both safety and shelf life, in the interest of plant-based and alternative protein sources.
• The industry can ensure the production of safe and sustainable food products, by incorporating such insights into risk management strategies.
• Predictive microbiology adapts to changes in distribution and supply chain dynamics and plays a vital role in understanding how these ingredients interact with microorganisms, influencing both safety and shelf life.
• As globalization and e-commerce reshape food distribution, the potential for microbial contamination increases so predictive models help identify vulnerable points and guide the implementation of control measures to maintain food safety during transportation, storage, and distribution.

Predictive Mathematical Modeling

• A growth kinetics model with equations between microbial population changes and influential factors such as temperature, pH, water activity, and nutrient availability.
• Gompertz, Baranyi, and Logistic models are frequently used to predict microbial growth curves.
• Models consider the lag phase, exponential growth phase, and stationary phase for a comprehensive understanding of microbial behavior.
• ComBase Predictor, Food Spoilage and Safety Predictor (FSSP), and Pathogen Modeling Program (PMP) are notable examples of software tools

Modeling Process

• The process comprises primary, secondary, and tertiary models.
• Primary models determine the reaction/response of microorganisms in a system as they evolve.
• Primary models in growth and inactivation/survival modeling include growth models, growth decline models, D-values/thermal inactivation models, inactivation/survival models, and growth rate values models.
• Secondary modeling involves modeling variables in a primary model that are subject to change due to environ-mental factors.
• Models describe ecological variables such as growth rate and lag-phase time, affect certain kinetic parameters.
• Tertiary models integrate the models within a computer program, facilitating forecasting via a practical interface and involve simplifying and converting complex predictive models into accessible versions for non-expert end-users, such as hazard analysts, researchers, and those with limited knowledge of microbiological prediction.

Data Generation

• Models are constructed using preexisting and newly generated data from experiments, Data can be obtained through artificial media or food matrices.
• Artificial media allows enhanced control over experimental conditions, minimizing human involvement and saving resources.
• Consideration of parameters influencing microbial behavior is crucial.
• Food matrix experiments closely approximate reality and provide trustworthy values.
• These experiments are intricate, requiring resources.
• Growth curves are produced to examine the effects of various environmental parameters (temperature, pH, NaCl concentration).

Modeling

• Researchers evaluate the reactivity/reaction of organisms within the system across standardized conditions over time.
• Secondary modeling examines the variability in the reaction of major model parameters to environmental conditions.
• Product validation assesses the accuracy/reliability of models before use via internal and external validation.
• Tertiary modelling integrates above models into software to yield outcomes that facilitate relationships for predictive purposes.

Role of PM in FM

• Predictive microbiology helps in the selection of dangerous microorganisms and better understanding of the ecology of microorganisms.
• It also aids in comparison of information with control criteria and the improvement of systems for microbiology infections monitoring.

Death Kinetics Equations

• N = No - kt is a simple approach to inactivation kinetics, where a negative and linear correlation exists between cell count and lethal treatment/inactivation rate
  • N and No are respectively cell count over time and the initial cell count; t is the independent variable, and k the inactivation rate. N = Noe-kt
• log N = log No - (kmaxt / In 10)

Ball and Olson Equation

• Ball and Olson introduced the decimal reduction value or first reduction time, represented by the symbol D. log N = log No - (t / D)

• N and No are the population at time t and initial cell number respectively and Kmax the inactivation rate.

• This is one of the most used approach for thermal sentivity of bacteria and fungi

• Independent variable is time (thermal treatement duration) but can model chemical and pressure effects

• works for simple systems and fails in fitting data when lethal treatment exerts significant effect

Shoulder/Tail Negative Gompertz Equations

• Inactivation kinetics can be described by a negative sigmoid or a non-linear equation that covers at least three steps: a shoulder (no decrease in cell count), exponential death phase and a tail. shoulder presents in death kinetics:

  • Microorganisms are in clumps.
  • Cells counterbalance the lethal treatment.
  • The medium contains protective components.
  • Cumulative injury must occur.

• Tail presence in inactivation kinetics: sub-resistant population exists. Can be due to:

  • Vitalistic approach.
  • Mechanistic approach: the tail is a “normal” trait and describes a sub-population inaccessible to or adapted to the lethal treatment.
  • Mechanistic approach: tail is an artifact because a residual sub-population is genetically more resistant.

• The shoulder/tail model by Geeraerd et al. comprises shoulder tail steps, is described by two parameters (kmax and Nres) with this equation: dN/dt = -kN, dC/dt = -kmaxCc k=kmax(1/1+Cc)(1-Nres/N) as these equations have four degrees of freedom (two parameters and two initial states) and encompass log-linear inactivation implying the selection of an extremely low value of the parameter Cc(0) and Nres in order to imply non-displaying the shoulder and the tail

• After substituting Cc(0) by ek max SL -1 with SL (time units), a parameter can be reached that is represented by this equation:

Re-parameterized Gompertz Equations

• Gompertz Equations y = k - A exp[-exp (dmaxe/A) (a - time) + 1]) (9)
• k and A are respectively the initial cell count and the decrease of cell count over time, dmax is maximal inactivation rate, a is the shoulder
• only one equation needs to be changed
• drawbacks present

Weibull Model

• The Weibull model can be defined as follows:

• The Weibull model can be defined as follows:f(t) = (β / α) (t / α)^β-1 exp(-(t/α)^β) (10)

• cumulative function:F (t) = exp(-(t/α)^β)(11)-

• the survival kinetics:InS (t) = -

• (t) = -()

• S is the ratio N/N0

• β = the shape of the curve (ẞ = 1, straight line; ẞ < 1, concave curve; ẞ > 1)

• the parameter & modifies the slope The Weibull equation:log N = log No - (t/a)^n

Software Tools Of PM

• Some notable examples of software tools include GInaFit, MLA, AMI, and DMFit.
• One of the most important and valuable tools in predictive microbiology is ComBase