Methods for food shelf life determination and prediction - Oxidation in Foods and Beverages and Antioxidant Applications - page 207

· an accurate kinetic/descriptive model of oxidation during food storage is
available;
· the relationship between the accelerating factor and oxidation rate is known.

In the case of oxidative reactions, temperature is certainly the most used
accelerating factor. However, other factors, such as oxygen concentration,
moisture, pro-oxidants and light can also find application (Frankel, 2005).
Although the study of food oxidative stability as a function of different
environmental factors is extensive, results are difficult to interpret in terms of
shelf life data because of variations in test conditions and acceptability limit
definitions. It should be pointed out that in most cases the lipid stability tests
conducted under extreme environmental conditions (i.e., high temperatures, high
oxygen concentration) have a different finality with respect to a shelf life study.
The former are generally set up to evaluate the susceptibility of a sample to
oxidation and the aim is to predict its stability as a function of different
variables. However, a shelf life predictive study has the objective to correctly
estimate the product shelf life under actual storage conditions. Thus, the
mathematical model adopted should have the power to predict the shelf life with
defined estimation errors.

9.5.3 Temperature as accelerating factor
Among the potential factors accelerating oxidation reactions, temperature is the
only one commonly exploited in ASLT. This is due not only to the fact that
temperature is one of the most critical factors for many products but also to the
availability of a theoretical basis for the development of a mathematical
description of the temperature sensitivity of chemical reaction rates. In fact, the
Arrhenius equation 9.6 (Arrhenius, 1901), developed theoretically on the
molecular basis for reversible chemical reactions, has been shown to hold
empirically for a wide range of complex chemical and physical phenomena
occurring in foods (Labuza and Riboh, 1982):
k k 0 e ÿE a =RT 9:6

where k is the reaction rate constant; R is the molar gas constant (8.31 J/K/mol),
T is the absolute temperature (K); E a is the activation energy (J/mol) and k 0 is
the frequency factor. The possibility to apply the Arrhenius equation to describe
the temperature dependence of oxidation rates can be easily and quickly
assessed by plotting ln k as a function of the reciprocal of temperature. A linear
relation between these variables can be observed when Arrhenius behaviour is
fulfilled. Hence, by measuring the rate of oxidation at least at three different
temperatures, the oxidation rate at a desired temperature can be extrapolated. In
this context, the application of a rigorous statistical approach is essential to
evaluate the accuracy of the extrapolated data. The methodology for the
statistical application of Arrhenius equation to model the temperature
dependence of food quality is well described by van Boekel (2008).