Functional properties of foods. Database and model prediction

Standard Deviation
WAI .
different relationship concerning more predictor variables is more appropriate. In general, the agreement
between the experimental data and the estimated values is reasonably good.
10
Χ = 20 % (wb) S = 180 (rpm)
9
Starch
8
7
Rice
6
5
Corn
Beans
4
3
Barley
Wheat
2
1
Oat
0
80 110 140 170 200 230 260
Die Temperature ( o C)
WSI % .
50
Χ = 15 % (wb) S = 200 (rpm)
45
40
35
Beans
Rice
30
Starch
25
20
15
10
5
0
Wheat
Corn
Oat
80 110 140 170 200 230 260
Die Temperature ( o C)
Figure 1. Effect of die temperature ( o C) on WAI (left) and WSI (right) for barley (▲), corn (●), oat (■), rice (x), wheat
(◊), beans (○) and starch (□). Lines are calculated model values using parameters given in Table 2
16
12
8
Lack of fit
4
0
Experimental
error
0 1 2 3 4 5
Number of Parameters
Figure 2. The standard experimental error (Se) and lack of fit (Sr) as a
function of the number of parameters for WAI model; case of rice
Table 3. Standard experimental error (Se), standard deviation between experimental and
calculated values (Sr) and the parameters of the model for the PDI prediction
Food System Se Sr a b c
Beans (P. vulgaris L.) meal 2.83 3.23 16.58 -0.98 -0.05
Canola meal 1.55 1.98 21.57 -2.08 0.11
Soy flour 3.34 14.45 16.22 -3.26 -0.10
Soybean 0.62 3.18 27.08 -0.01 -0.06
The variety of experimental PDI values ranges from 6.2 to 96.8 while, PDI predicted vary from 6.6 to 46.8
for all products. The smaller range of predicted values compared to experimental values may be explained by
the low ability of the model to predict accurate values mainly at high values of independent variables. The
scatter plot, which relates the PDI with residence time of products at three different temperatures for beans