Functional Properties of Foods. Database and Model Prediction

Functional Properties of Foods. Database and Model Prediction Nikolaos A. Oikonomou a, Magda Krokida b a Department of Chemical Engineering, National Technical University of Athens, Athens, Greece (nikosoik@central.ntua.gr) b Department of Chemical Engineering, National Technical University of Athens, Athens, Greece (mkrok@chemeng.ntua.gr) ABSTRACT A food system is characterized by several physicochemical properties. Some of those physicochemical properties affecting the food system during preparation, processing, storage and consumption are defined functional properties. A wide range of functional properties are delivered mainly by proteins, due to their structural characteristics and amphiphilic nature. Additionally, protein functionality is affected by extrinsic factors such as temperature, ph, ionic strength, and interaction with other components. The novelty of the present study was the collection of experiment data in a database from the available scientific journals, regarding the values of water absorption index, water solubility index, protein dispersibility index, nitrogen solubility index, gluten index and wet gluten and secondly the investigation of statistical models for optimum correlation of the retrieved data. The published data was then organized based on the experimental factors. For the water absorption index and water solubility the main experimental factors were the leading extrusion parameters such as die temperature, screw speed, feed moisture content. The extrinsic factors concerning the protein dispersibility index and nitrogen solubility index was the ph, the amount of heat treatment, the total time of treatment and the level of enzymatic hydrolysis. Finally, for gluten index and wet gluten the correlated factors were the wheat genotype, fertilization level, crop location, and the exist subunits of genotype. Furthermore, mathematical models were developed to describe the relationship between functional variables and the main experimental factors. The statistical models fit the experimental data as closely as the experimental variance (literature noise) would allow. The estimation of parameters of power low regression models was conducted by the Levenberg Marquardt algorithm and the regression diagnostic shows a good fit of datasets to functional properties. The investigation of food functional properties is of great importance for food industry as they affect the quality and acceptance of the final product. Keywords: Extrusion; Cereals; Soybean products; Database INTRODUCTION Functionality is a term used to describe those characteristics of a food that have been correlated to quality attributes identified by the human senses. Proteins play important roles in the functional properties of many foods, and thus contribute to the quality and sensory attributes of many food products. Protein functional properties have traditionally been defined as physical or chemical properties of proteins that affect their behaviour in food systems during preparation, processing, storage, and consumption [1,2,3,4,5]. The novelty of the present study was firstly the investigation and proper classification of the effect of various parameters on six typical functional properties such as water absorption index (WAI), water solubility index (WSI), protein dispersibility index (PDI), nitrogen solubility index (NSI), gluten index (GI) and wet gluten (WG) of several food products and secondly the investigation of statistical models for optimum correlation based on the retrieved data. MATERIALS & METHODS For model purposes an extensive literature search was contacted through the most popular food engineering and food science journals of recent years. The compiled data, approximately,000 values, of the previous functional properties were organized into a database developed in Microsoft Excel 03. Extrusion is a technological process, commonly used in the field of the human and animal feed industry, applied successively on a broad spectrum of commercially manufactured products. It is observed that WAI and WSI are related to the extrusion process variables such as extruder type, die temperature, feed moisture

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 ( 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 0.0 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 0.0 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 83 67 780 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. 0 Oat 0.18 1. 6.24-0.08-0.51-0.07 0 Rice cv. 2.53 18.70 23.17 1.36 0. -0.04 0 Wheat 3.34 9.96.42 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.00 15.48 27.18-0. 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=0 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

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. Χ = % (wb) S = 180 (rpm) 9 Starch 8 7 Rice 6 5 Corn Beans 4 3 Barley Wheat 2 1 Oat 0 80 1 140 170 0 230 260 Die Temperature ( o C) WSI %. 50 Χ = 15 % (wb) S = 0 (rpm) 45 40 35 Beans Rice 30 Starch 25 15 5 0 Wheat Corn Oat 80 1 140 170 0 230 260 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. 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

0 1 1 130 140 PDI % PDI % PDI % meal is presented in figure 3 (left). Incremental increase in residence time does not have a consistent effect on experimental and predicted values of PDI for all examined products. Figure 3 (right) presents the scatter plot of PDI versus temperature at three different residence times for beans meal. In general, an increase in temperature leads to a decrease of PDI value, for all products. A possible explanation for this situation is that when the protein is heated rapidly, denatures. The quaternary or tertiary structure is destroyed and the protein molecules break up into several subunits, of which some slowly form a soluble and later on insoluble aggregate, whereas the rest remains in solution. Simultaneously other factors such as ph, salt content and additives concentration can affect the protein solubility; however we don't take them into consideration for practical reasons. 25 25 23 T=2 23 t=0.09 18 T=113 18 t=3.65 15 13 T=136 15 13 t=7.2 0 1 2 3 4 5 6 7 t x^3 (s) 0 5 1 115 1 125 130 135 140 T ( o C) Figure 3. The graphical correlation between experimental (dots) and calculated (lines) values of PDI versus residence time (left) and temperature (right) for beans meal The effect of temperature and residence time on predicted PDI values for the beans meal is presented in figure 4. The minimum and maximum values of the independent variables are based on the corresponding experimental values. At high temperatures, the PDI of all products decreased considerably with increasing residence time, while at high residence times, the decrease in temperature resulted in only a slight decrease in PDI (Fig. 3). 24 22 18 16 14 12 0.09 2 46 8 t x^3 (s) T ( o C) Figure 4. Contour plots showing the effect of temperature and residence time on calculated PDI values for beans meal CONCLUSION The present study proposes a mathematical model that investigates the effects of the main extrusion variables on the WAI and WSI properties for some food products. The model is fitted to literature data and the model parameters were estimated for every category of food products. The method used for curve fitting was a nonlinear regression that was based on the Levenberg Marquardt algorithm. In most cases, the modelling showed that the power law equation is fitted satisfactory to the available experimental values. Furthermore, modelling of a solubility index (PDI) for food products which contain protein or starch showed that power

low equations is fitted adequately to the available experimental values, which allows theoretical prediction so that important information can be withdrawn without experimental procedures. REFERENCES [1] Maroulis, Z.B.; Saravacos, G.D. 07. Food Plant Economics. CRC Press: Boca Raton, pp.70-99. [2] Maroulis, Z.B.; Saravacos, G.D. 03. Food Process Design. Marcel Dekker: USA, pp. 2-13. [3] Oikonomou N.A. & Krokida M. 11. Literature Data Compilation of WAI and WSI of Extrudate Food Products. International Journal of Food Properties, 14(1), 199-240. [4] Oikonomou N.A. & Krokida M. 11. Water Absorption Index and Water Solubility Index Prediction for Extruded Food Products. International Journal of Food Properties, In Press. [5] Oikonomou N.A. & Krokida M. 11. Protein Dispersibility Index: Data Compilation from Published Research for 65 Foods Classified into Seven Product Categories. International Journal of Food Science and Technology, Submitted.