ISO 23691:2026

International Standard   Current Edition · Approved on 09 February 2026

Microbiology of the food chain — Determination and use of cardinal values

ISO 23691:2026 Files

English 47 Pages
Current Edition
OMR 88.05

ISO 23691:2026 Scope

This document establishes basic principles and specifies requirements and methods to determine the cardinal values of bacteria and yeast strains and use them to predict microbial growth.

The four main steps of the approach are:

  1. determination of the cardinal values in culture medium;
  2. determination of the correction factor in the target food;
  3. validation of the model;
  4. simulations.

Four environmental factors are considered: temperature, pH, aw and inhibitors (e.g. organic acids).

NOTE 1        Microbial competition is not considered as an inhibitor in this document and can be addressed by proper modelling approaches.

The determination of cardinal values is performed in a two-step approach:

  • the determination of maximum specific growth rates of the studied strain grown in broth under a defined range of values of the studied environmental factor(s);
  • the use of recognized predictive microbiology secondary models to fit the obtained experimental data to obtain the cardinal values.

The use of cardinal values in microbial growth simulation is based on predictive microbiology primary and secondary models. The cardinal values are combined with challenge test data to consider the matrix effect. Depending on the goal of the growth simulation, it is important to account for variation of cardinal values between strains within a bacterial or yeast species.

Cardinal values are a good indicator of a strain growth ability for the studied environmental factors. They are therefore used as criteria to select strains, in addition to their origin and virulence, when performing growth challenge tests (see ISO 20976-1) or in methods validation (see ISO 16140 series).

NOTE 2        This document focuses on the determination of cardinal values for one strain. The same methodology can be used to characterize multiple strains independently to cover biological strain variability and include these results in the predictions.

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