“Does the heating process sufficiently reduce pathogens in my products?”

1. Perform a microbiological risk assessment.

Which bacteria could potentially be present in the raw materials? And which pathogens could proliferate in the product if they survive the thermal process? Under certain temperatures, pH, or a_w levels, specific pathogens cannot grow. This step clarifies precisely which microbiological risks must be controlled.

2. Identify the indicator organism with the highest heat resistance capable of growing in your product.

If you achieve an adequate reduction of this target indicator organism, the heating process will also sufficiently reduce other, less resistant vegetative bacteria. Your thermal process should be calibrated against the most heat-resistant relevant microflora.

3. Record and log the heating profile.

Track temperatures at regular intervals (for example, minute by minute) using a data logger placed in the thermal core of the product. This accurately maps the thermal profile and its direct impact on core temperatures throughout the cycle.

4. Apply these data points to a predictive model.

The model determines the time versus temperature based on three variables: the D-, T-, and Z-values for each pathogen. This allows you to determine the decimal reduction achieved. For ready-to-eat products, the general aim is to achieve a 6D reduction of the indicator organism. This reduces the probability of a pathogenic cell surviving to 1 in 1 million.

You can calculate the decimal reduction using the formula:

D = Dref × 10^((Tref − T) / z)

Check whether you have achieved the target of a 6-decimal reduction of the indicator organism in your product.

Please note: look up the appropriate theoretical D-, T-, and Z-values for the relevant product group in scientific publications. These values can vary significantly between product groups.

5. Confirm the theoretical decimal reduction calculations with a single-strain/focused challenge test.

A model is only a mathematical simulation, and microbiology can involve natural variability. For instance, matrices with high fat content provide a protective matrix effect for certain pathogens. Because every formulation is unique, verification via a laboratory challenge test remains essential to validate theoretical modeling under real-world processing conditions.

Once your complete thermal process has been validated, you may be able to optimize routine monitoring by lowering testing frequencies or reducing the number of required analytical parameters. Modeling calculations may also reveal that a shorter heating step is sufficient to guarantee food safety. Over-processing degrades product quality and results in unnecessary energy costs. It remains vital, however, to validate any shortened heating profiles with an empirical challenge test—verification is key.