The resulting model descriptions and parameter estimates are
shown in Fig. 3 and Table 3. As with the CTMI model, Fig. 3 shows
that equidistant aw levels can lead to unrealistic model simulations:
the growth rate seems to increase for higher aw values. This rela-
tion, however, does not correspond to the real mmax(aw)-relation.
Accurate simulation of the behavior as a function of aw around the
optimal conditions can only be obtained when experimental levels
are included slightly below and slightly above the optimum
condition. In combination with the two remaining experiments,
this enables accurate and realistic description of the real mmax(aw)-
relation. As for the CTMI case, these examples show that well-
founded selection of the experimental levels can yield accurate
model identification, even when only a very low number of
experiments is included in the design.
These three studies prove that OED/PE-inspired designs, which
are based on the model’s sensitivity functions, generally yield much
better results than typical equidistant designs. By selecting the four
experimental levels based on the sensitivity functions, a more
realistic description of the behavior around optimal conditions is
obtained.
The resulting model descriptions and parameter estimates are
shown in Fig. 3 and Table 3. As with the CTMI model, Fig. 3 shows
that equidistant aw levels can lead to unrealistic model simulations:
the growth rate seems to increase for higher aw values. This rela-
tion, however, does not correspond to the real mmax(aw)-relation.
Accurate simulation of the behavior as a function of aw around the
optimal conditions can only be obtained when experimental levels
are included slightly below and slightly above the optimum
condition. In combination with the two remaining experiments,
this enables accurate and realistic description of the real mmax(aw)-
relation. As for the CTMI case, these examples show that well-
founded selection of the experimental levels can yield accurate
model identification, even when only a very low number of
experiments is included in the design.
These three studies prove that OED/PE-inspired designs, which
are based on the model’s sensitivity functions, generally yield much
better results than typical equidistant designs. By selecting the four
experimental levels based on the sensitivity functions, a more
realistic description of the behavior around optimal conditions is
obtained.
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