2. Background of the study
Problem statement
Aim and objectives
Significance of the study
Literature review
Materials and methods
3. BACKGROUND
OF THE STUDY
Definition of drying
Drying is a complicated process which involves the
removal of moisture due to simultaneous heat
and mass transfer.
Prediction of drying parameters
There are various methods for prediction of
drying parameters of agricultural products.
One simple way is to use available empirical
correlations.
However, Artificial Neural Network (ANN) can
more accurately predict drying parameters.
4. Problem
statement
It is important to be able to
accurately predict with ease the
moisture content of processed
foods since the moisture content
of foods determine their shelf
life. Artificial Neural Networks
(ANN) models provide an
accurate method for the
prediction of drying parameters
of Cassava . Hence, the
development of an ANN model
for the accurate prediction of
drying parameters for Cassava.
5. SIGNIFICANCE OF STUDY
With the aid of this work, accurate prediction of the moisture content of cassava can be
carried out with ease. When successfully completed, this work will serve as a solution to
extending the shelf life of dried cassava products.
6. LITERATURE REVIEW
History of cassava
Wild populations of M. esculenta subspecies flabellifolia, shown
to be the progenitor of domesticated cassava, are centered in
west-central Brazil, where it was likely first domesticated no
more than 10,000 years. The oldest direct evidence of cassava
cultivation comes from a 1,400-year-old Maya site, Joya de
Cerén, in El Salvador .
Types of cassava
Sweet Cassava: Contains large quantities of cyanide compounds.
Bitter Cassava: Contains much higher quantities of cyanide
compounds.
7. Previous works on food drying
Author Work Area of study Findings
Wojdyło et al Studied the effect of different
drying methods – convective
drying, vacuum-microwave
drying, vacuum drying and
freeze drying - on bioactive
compounds of strawberry
fruits
Food drying The researchers found that
drying destroyed anthocyanins
and flavanols. Also, all dried
products showed a
significantly lower antioxidant
capacity.
Sauvageot et al. Studied the effect of water
activity on crispness of
different breakfast cereals.
Drying
parameters
All the products analyzed
showed very similar results. In
the sensory evaluation, high
crispness intensity was
observed until 0.53 aw.
9. METHODS
Sample Preparation
The freshly obtained cassava will
be washed, peeled by hand and
sliced into four different sizes of
10mm, 14mm, 18mm and 22mm.
The slices will be blanched in hot
water at 50oC for 7 minutes.
10. Drying procedure
The freshly obtained cassava slices will be blanched in
hot water at 50oC for 7 minutes. The slices will be
dried using Gallenkamp oven (Model OV 160) at
60oC for 24 hours. Processing time and microwave
output power will be adjusted with the digital control
on the microwave oven while intermittently weighing
the sample and taking record of the weight loss.
11. ANNMODELLING
MATLAB software is to be
used for the design and
testing of various ANN
models. The ANN
configuration to be used in
this work is a multilayer
“feed-forward,” consisting
of one input layer, one
hidden layer, and one
output layer
12. Input-output data sets in the
experiment will be collected
for MR. For ANN model
building, the available data
will be randomly divided into
3 subsets: training (70%),
validation (15%), and testing
(15%) subsets. The networks
performance will be evaluated
by correlation coefficient (𝑅2)
and mean square error
(RMSE).
