Basis of HTST and UHT'S Effectiveness
The effectiveness of HTST and UHT processing can be explained by thermal-process reaction kinetics. The exposure time and
temperature in these processes exploit the differences between the reaction kinetics of the inactivation of microorganisms
and of the reactions that define product quality. Quality can be broadly defined by components ranging from nutrient content
to enzyme activity, but the overall relationship remains.
The Bigelow and Ball method or Arrhenius kinetics can be used to describe the reaction kinetics supporting these processes,
as shown in Table I. Microbial inactivation and the loss of quality both follow first-order kinetics. The decimal reduction time (D value) is
the time needed to reduce a microbial population by one log cycle at one temperature. Both the D value and the reaction rate
(k) change exponentially with temperature. This temperature sensitivity is described by the z-value and the activation energy
Table I: Equations for determining reaction kinetics, t = time, T = temperature.
The important point is that the reactions for inactivating microorganisms accelerate exponentially more than those defining
quality, regardless of the mathematical model. The z values are smaller for microbial inactivation than for the destruction
of most qualitative components. Correspondingly, the Eas are larger for microbial inactivation than for the destruction of most qualitative components.
This gap provides the opportunity to select conditions that optimize product safety and quality. Consider pasteurization or
sterilization where the target organism and the microbial reduction (calcuated by log (N0 /N) in which N0 is the initial microbial count and N is the microbial count), reference temperature (Tr), D-value at Tr (Dr), and z are all known. Using either Bigelow and Ball or Arrhenius kinetics, the resulting equation yields a constant or iso-process
line representing all the time and temperature combinations that provide the microbial reduction for pasteurization. Each
pair of time and temperature conditions on the line delivers the same microbial kill. More than that, they define a boundary,
which is shown in Figure 3. Treatments higher in temperature or longer in time (i.e., those left or above the microbial log reduction line in Figure 3) have higher microbial kill and higher assurance levels. All other treatments do not have sufficient lethality.
Figure 3: Plot of the log of process time (t) vs. 1/T or (Tr-T) showing optimization region of quality and process lethality, T = temperature, Tr = reference temperature.
Similarly, the iso-process line for a qualitative factor (e.g., nutrient retention) can be calculated. As shown in Figure 3, this line has a different slope because the reactions inactivating nutrients do not have the same sensitivity to temperature
change as those inactivating microorganisms. All the points below and right of this line (i.e., lower temperature and time)
retain more nutrients.
Figure 3 demonstrates why HTST and UHT processes retain quality and deliver proper lethality. Tracing the pasteurization/sterilization
line toward higher nutrient retention leads to higher-temperature and shorter-time processing conditions. The higher- temperature
and shorter-time processes retain more nutrients than the longer-time and lower-temperature processes, such as autoclaving.
In addition, a treatment combination between the lines that increases both microbial inactivation and nutrient retention can
The significance of these relationships and this analysis are considerable. Having the reaction-kinetic parameters for the
target organism and the qualitative factor (in this case, nutrient retention) enables the selection of optimal processing
time and temperature conditions. Taken another way, determining the thermal-destruction kinetics of qualitative materials
allows them to be screened for suitability to preselected conditions. Either way, determining the thermal reaction kinetics
for the target organism and the materials that are to be retained in the actual product is valuable because it allows prediction
of their performance under a wide range of conditions using simple math and avoids problems at the production level.