3.4. Analysis of response surface
Based on the above data (Eq. (4)) using RSM, tri-dimensional response
surface plots were constructed to visualise the relationship
between responses and the levels of the processing variables and
the interactions between two variables. Fig. 2(A) shows the effect
of raw material (DSDPB) concentration and enzyme dose on AX
extraction. Whilst the yield of AX reached the maximum over the
central condition of enzyme dose at a fixed raw material concentration,
increase in raw material concentration at a fixed enzyme
dose led to a slight increase in the yield of AX. The effects of raw
material (DSDPWB) concentration and extraction temperature,
and raw material (DSDPWB) concentration and extraction time
were similar to that of raw material concentration and enzyme
dose (Figure not shown). Fig. 2(B) shows the response surface plot
at various raw material (DSDPWB) concentration and ultrasonic
power. A maximal yield of AX was observed near the central condition
of ultrasonic power at a fixed raw material concentration,
increase in raw material concentration at a fixed ultrasonic power
led to a marked increase in the yield of AX (4.0–12.77% at 150Wof
ultrasonic power, and 2.29–11.1% at 250W of ultrasonic power).
The effects of enzyme dose and ultrasonic power, extraction temperature
and ultrasonic power, and extraction time and ultrasonic
power were similar to that of raw material and ultrasonic power
(Figure not shown). The effect of enzyme dose and extraction temperature
shown in Fig. 2(C) for AX extraction demonstrated that
the yield of AX could reach the maximum value near their zero levels.
Fig. 2(D) shows the effect of enzyme and extraction time on AX
extraction. Whilst the yield of AX reached the maximum over the
central condition of enzyme dose at a fixed extraction time, the
maximal yield of AX was observed over the central condition of
extraction time at a fixed enzyme dose. The effect of extraction
temperature and time was similar to that of enzyme and extraction
time (Figure not shown).
3.5. Validation of the model and control experiment