Functional properties of foods. Database and model prediction

More documents

Recommendations

Info

content, feed rate, screw speed, screw configuration. Other affecting factors may include raw material formulation, pre-processing treatments, initial particle size of milled materials, and the milling procedure. A preliminary statistical investigation shows that we have a strong relationship between WAI or WSI and 4 independent variables such as die temperature, feed moisture content of food product in wet base %, screw speed of extruder and blend level% mainly for starchy and proteinaceous extruded food products. It is found from the modelling exercise that using a model, which considers power law dependency of all independent variables, provides the best performance in the model: WAI or WSI a T T o where WAI: water absorption index (dimensionless), WSI: water solubility index (%), T: die temperature of extruder ( o C), X: feed moisture content of food product in wet base %, S: screw speed of extruder (rpm), M: mixture of blend %, variables with subscripts: reference steady average values of independent variables, a,b,c,d,e: dimensionless adjustment parameters. Similarly, concerning the property of PDI the intended model is: b PDI a T T o t t where PDI: protein dispersibility index (%), T: is the temperature of the product which was exposed during preparation ( o C), t: is the corresponding residence time (10 3 *s), variables with subscripts: reference steady average values of independent variables, a,b,c: dimensionless adjustment parameters. The parameter estimation was performed by the Levenberg–Marquardt (LM) algorithm. The LM algorithm is an iterative technique that locates the minimum of a multivariate function that is expressed as the sum of squares of nonlinear real-valued functions. The software package Stargraphics Centurion v. XV (Manugistics Inc. Rockville, MD, USA) was used for the nonlinear regression analysis. RESULTS & DISCUSSION b c X X o c o Table 1 presents, in a compact form, the main information concerning the examinee properties. The minimum, maximum, average values and the standard deviation for each of six properties are presented in the first four columns. The main food systems and the factors which altered the value of properties are presented in the fifth and sixth columns respectively. Finally, the total number of experimental data which was retrieved is presented in the final column. Table 1. Data compiled from literature about examinee functional properties used in statistical analysis Property MIN MAX AVG SD a Food System Main Factor N b WAI 0.3 14.4 4.8 1.9 Corn, Wheat, Rice Flours WSI 0.2 94.8 22.4 15.4 Corn, Wheat, Rice Flours PDI 1.3 100.0 42.0 42.0 Soybean Products NSI 2.5 94.0 40.7 19.4 Soybean Products Extrusion parameters, blends Extrusion parameters, blends pH, Heat, Enzymatic Hydrolysis pH, Heat, Enzymatic Hydrolysis GI 1.0 100.0 71.0 25.1 Wheat Products Genotype, Fertilization, Location, Subunits WG 0.2 60.9 29.6 9.2 Wheat Products Genotype, Fertilization, Location, Subunits a Standard Deviation; b Number of retrieved data S S o d e M M o Due to the large number of replicates, a fundamental prerequisite for the least squares fitting of the models to the corresponding data sets is equal variance of the different observations. The results of these tests show that the variance was almost steady. The results of the nonlinear regression analysis of fitting the equation 1 to the experimental points are shown in Table 2. In this table the standard experimental error and the standard deviation between experimental and calculated values are also presented. In most cases the values of the 1268 1083 1067 780 3400 2354 (1) (2)
standard experimental error are slightly smaller (or slightly bigger in the cases of corn starch:WPC and rice starch:SPI) than the values of the standard deviation between experimental and calculated values indicating that the model (Eqn. 1) is satisfactory in predicting the WAI of products. In the cases of wheat, wheat:DDG and potato:DDG the difference in the values was slightly bigger than that in the above cases showing that there is an adequate fit to the equation 1. Table 2. Standard experimental error (Se), standard deviation between experimental and calculated values (Sr) and the parameters of the model for the WAI and WSI prediction respectively Property Food System Se Sr a b c d e WAI Barley cv. 0.60 1.29 4.62 -0.69 0.44 0.86 0 Corn 0.42 0.69 5.23 0.07 0.16 0.35 0 Oat 0.24 0.55 2.26 -0.68 -0.14 -0.06 0 Rice cv. 1.12 1.21 5.97 0.09 0.60 0.01 0 Wheat 0.26 1.88 4.63 -1.07 -0.46 0.46 0 Beans & Chickpeas cv. 0.42 1.51 4.07 0.11 0.49 0.05 0 Starch 1.08 3.06 7.54 1.23 0.91 -0.06 0 Blends Corn : DDG a 0.40 0.74 0.04 0.11 -0.06 -7.46 -0.17 Corn : Lentil 0.28 0.53 4.39 0.28 0.15 1.00 -0.00 Rice : DDG 0.41 1.21 7.54 0.17 0.32 1.00 -0.08 Wheat : DDG 0.40 1.78 4.07 -0.60 -0.13 0.34 -0.12 Corn starch : WPC b 1.55 1.21 5.37 0.30 0.54 0.14 -0.08 Rice starch : SPI c 1.02 0.51 19.40 0.15 -0.33 1.00 -0.29 Potato : DDG 0.53 2.07 9.40 0.03 0.19 1.00 -0.12 WSI Corn 5.96 11.99 19.76 -0.16 -0.34 -0.20 0 Oat 0.18 1.20 6.24 -0.08 -0.51 -0.07 0 Rice cv. 2.53 18.70 23.17 1.36 0.10 -0.04 0 Wheat 3.34 9.96 10.42 2.57 0.02 1.45 0 Beans & Chickpeas cv. 2.96 16.58 33.98 -1.48 1.12 0.26 0 Starch 7.91 27.30 27.80 0.70 -0.41 -1.09 0 Blends Corn : Lentil 6.23 7.57 12.76 0.52 -0.72 1.00 -0.11 Corn starch : WPC d 10.00 15.48 27.18 -0.20 0.02 0.60 -0.11 a Dried Distillers Grains from corn & wheat; b Whey protein concentrate; c Soy protein isolate; d Whey protein concentrate; cv: cultivar The plot, which relate the WAI (left figure) with die temperature of extruded products at X=15 % and S=180 rpm are presented in figure 1. The various dots shapes represent the experimental values of materials, while the lines are calculated model values. Increase in die temperature does not have a consistent effect on experimental and predicted WAI for all raw materials examined. In the case of WSI (right figure) at X=15 % and S=200 rpm we conclude that there is not a general tendency for the various extruded materials with respect to the die temperature. Figure 2 presents typical chart of the standard experimental error (Se) and lack of fit (Sr) as a function of the number of parameters for WAI model in the case of rice. The confidence intervals (=0.05) for parameters of food products (data not shown), for WAI and WSI show that all the calculated parameters are required in the equation 1. The products such as wheat, beans, wheat:DDG with two or more parameters having 0 within the range of their confidence intervals are also those products where the differences in the values of Se and Sr was great. In these cases the number of parameters for accurate prediction of the model may be smaller. The values of Se, Sr and parameters for PDI (Eqn. 2), obtained by curve fitting using lack of fit regression diagnostic with Levenberg–Marquardt algorithm are presented in Table 3, showing that the performance of the proposed regression model, was satisfactory for the case of beans and canola meal. The results presented in Table 3 suggest that, for soy flour and soybean components, a power low nonlinear relationship between PDI and the two predictor variables (exposing temperature, corresponding residence time) is moderate; a
Page 1: Functional Properties of Foods. Dat
Page 5 and 6: 100 110 120 130 140 PDI % PDI % PDI

Functional properties of foods. Database and model prediction

You also want an ePaper? Increase the reach of your titles

Delete template?

Save as template?