application of expert design in food science

1. EEXXPPEERRTT DDEESSIIGGNN
BByy:: NNoooosshhiinn NNoosshhiirrvvaannii

2. a Analysis of volatile arroommaa ccoommppoouunnddss ooff ffrreesshh
cchhiillllii ((CCaappssiiccuumm aannnnuuuumm)) dduurriinngg ssttaaggeess ooff
mmaattuurriittyy uussiinngg ssoolliidd pphhaassee mmiiccrrooeexxttrraaccttiioonn
((SSPPMMEE))

3. Software for Design of
Experiments
 Design Expert is a piece of software designed to
help with the design and interpretation of
multi-factor experiments

4.  Optimization of SPME extraction of
selected aroma compounds was done
using a central composite design (CCD)
 consisting of a 22 factorial design with
 resulting in 13 treatments

5.  For the optimization method, CCD is one
of the most useful design for estimating a
multifactor response surface which keeps
to a minimum number of experiments
 And allows the simultaneous variation of
all the experimented factors studied and
the distinguishing of interaction among
them

6. Experimental design showing
coded and uncoded variables

7. The evaporation of the volatile compounds can
be adequately represented by the model
equations as shown in Table

8. Results from response surface
methodology (RSM) showed that heating
at 60ºC for 30 min gave the optimum
parameter, as indicated by the high
desirability

12.  This study, therefore, tried developing a
mathematical model capable of predicting the
aw at fixed temperature (25º C ) of some foods,
from easily measured, pertinent
physicochemical food properties, namely pH
and Brix values.
 The study established a predictive model in
simulated food solutions (SFS), which
contained varying levels of water, sucrose and
acid.
 The predictive performance of the established
model was assessed through validations using

14.  The SFS were formulated based on the
data supplied to and processed using the
Design Expert Version 7.0.3 software
package
 A Central Composite Rotatable Design
(CCRD) was applied, to determine the
appropriate combinations of various levels
of pH and Brix

20. Response surface plot showing the
influences of pH and Brix on aw

25. Optimization ooff tthhee bbaassiicc ffoorrmmuullaattiioonn ooff aa
ttrraaddiittiioonnaall bbaakkeedd ccaassssaavvaa ccaakkee uussiinngg rreessppoonnssee
ssuurrffaaccee mmeetthhooddoollooggyy

26.  Effects of amount of ingredients such as
sugar (10–30%) and coconut milk (15–
35%) on the textural characteristics
(hardness and chewiness) and sensory
qualities (colour, firmness, cassava flavour
and overall acceptability) of cakes were
investigated.

27.  The three coded levels of sugar:
 -1 (10%),
 0 (20%),
 1(30%)
 coconut milk:
 -1 (15%),
 0 (25%),
 1(35%)

28. Central composite design arrangement and
experimental result for the response
variables

38.  Optimum formulation of baked cassava cake
was 25% of sugar and 20% of coconut milk.