The classical method of studying one variable at a time may be
effective in some processes, but fails to consider the combined effects
of several factors involved. In extraction processes, where
there are multiple independent variables affecting the responding
factors, it is likely that the operational variables interact and influence
each other’s effects on the response. Therefore, it is necessary
to use an optimisation method that can determine many factors
and possible interactions between these independent variables, so
that a set of optimal experimental conditions can be determined
Optimisation through factorial
design and response surface analysis, particularly, fulfils this
requirement. Response surface methodology (RSM) is a collection
of mathematical and statistical techniques widely applied in the
food industry to determine the effects of several variables and to
optimise conditions
The classical method of studying one variable at a time may be
effective in some processes, but fails to consider the combined effects
of several factors involved. In extraction processes, where
there are multiple independent variables affecting the responding
factors, it is likely that the operational variables interact and influence
each other’s effects on the response. Therefore, it is necessary
to use an optimisation method that can determine many factors
and possible interactions between these independent variables, so
that a set of optimal experimental conditions can be determined
Optimisation through factorial
design and response surface analysis, particularly, fulfils this
requirement. Response surface methodology (RSM) is a collection
of mathematical and statistical techniques widely applied in the
food industry to determine the effects of several variables and to
optimise conditions
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