OPTIMIZATION OF EXTRUSION PARAMETERS FOR DEVELOPMENT OF READY-TO-EAT BREAKFAST CEREAL USING RSM

Transcription

1 Asian J. Dairy & Food Res.., 33 (2) : 77-86, 2014 doi: / AGRICULTURAL RESEARCH COMMUNICATION CENTRE OPTIMIZATION OF EXTRUSION PARAMETERS FOR DEVELOPMENT OF READY-TO-EAT BREAKFAST CEREAL USING RSM G.J. Kaur*, J. Rehal, A.K. Singh, B. Singh and A. Kaur Krishi Vigyan Kendra, Punjab Agriculture University, Moga , India. Received: Accepted: ABSTRACT Designed experiments were conducted to optimize the operating conditions during the preparation of multi-grain extrudates (rice 45.4%, wheat and maize 27.3% each) with moisture content (14, 16, 18 %) at die head temperature (125,150,175 C) and screw speed (400,500,600 rpm). Suitable statistical models were fitted using Response Surface Methodology and functional properties were analyzed for optimization. Moisture content and die head temperature had a significant effect on the SEI, WAI, WSI, color and texture whereas SME is more dependent on the screw speed and die head temperature. Optimum processing conditions of 14 % moisture content, C die temp and 600 rpm screw speed were recorded. The predicted responses for SEI, WAI, WSI, color and texture were 7.72, 4.5, %, 31.21, N. The difference between the predicted and actual values of models for SEI, WAI, WSI, color, Texture and SME were non-significant. Key words: Cereals, Extrusion cooking, Optimization, Response surface methodology, Sectional expansion index, Water absorption index, Water solubility index. INTRODUCTION Extrusion technology is a high temperature short time (H TST) low cost food processing technique. It is energy efficient, provides better quality products and gives no process effluent (Camie et al.,1990). It is widely used for the production of expanded snacks, modified starches, RTE cereals, baby foods, pasta and pet foods (Deshpande, 2011). Cereals have been popular raw materials because of functional properties, low cost and ready availability. Rice (Oryza sativa), wheat (Triticum aestivum) and maize (Zea mays) are the maj or cereals wi th a world production of MMT, 682 MMT and MMT respectively ( nue.okstate.edu). They are responsible for satisfying the hunger quest of more than billion people (U.S Census Bureau, 2011). Rice is a staple cereal crop and an attractive ingredient in extrusion industry due to its bland taste, attractive white color, hypoallergencity and easy to digestion (Kadan et al., 2003). It is high in carbohydrates (76.5%) but low in protein content (7.5%), while wheat is nutritionally rich in protein (12.3%), fiber (2.3 %) and comparatively less in carbohydrate content i.e.69.4% as compared to rice. Wheat is traditionally consumed in the form of bread as roti, chapatti, pita, poori, upmav, flakes and porridge. Another important cereal which leads the world production is maize. Its carbohydrate content is 70%, protein 8.9% and fiber 2.0 % (FAO, 1993). All the cereals are high in starch content which is majorly responsible for the expansion while the protein, fiber, fat and sugar present in them acts as diluents (Moraru and Kokini,2003). The present study is in continuation of a multigrain cereal (complex carbohydrates) developed by Kaur et al., (2012). The objective of the study is to optimize the operating conditions and study its affect on the physical, functional and textural properties of the extrudates. Optimisation of operating conditions is important for up scaling the technology to industrial production. * Corresponding author s kaur_jagbir@pau.edu

2 78 ASIAN JOURNAL OF DAIRY & FOOD RESEARCH The predicted models were validated with the actual responses. MATERIALS AND METHODS Ingredients selection: Whole grain rice (PR 116), wheat (PBW 343) and maize (PMH 1) were procured from the Department of Plant Breeding and Genetics, Punjab Agricultural University, Ludhiana in the year The grains were milled with lab mill model 3303(Perten, Sweden) and passed through a 200 ìm sieve. The moisture content of the mixture was brought to 12 % by the formulae (Sacilik, Ozturk and Keskin, 2003): Q = Wi (Mf - Mi)/100 - Mi Where Wi, is the initial mass of the sample in kg; Mi, is the initial moisture content of the sample in % w.b.; and Mf, is the final moisture content of sample in % w.b. Ground sugar was incorporated at the rate of 15 % to the grounded multigrain mix (Rice: wheat: maize: 45.4, 27.3, 27.3) and mixed for 5 minutes in a mixer. After cooling the mixture was packed in airtight PET containers and stored at 7 0 C for 2 hr. Extrusion cooking: A laboratory scale co-rotating twin- screw extruder with intermeshing screws (Model BC21; Clextral, Firminy Cedex, France) was used for the extrusion study. The barrel diameter and L/D ratio were 25mm and16:1respectively. Material was fed into the extruder inlet port by a screw feeder, (DS and M, Modena, Italy). The conditioning of the sample was done in the mixing chamber of the extruder connected to the water source through a water pump. The quantity of water for conditioning was regulated with a regulator attached to the pump. The mixture was conditioned at three moisture levels (14, 16 and 18 %) in the first zone of the barrel. The screw of the extruder was operated at 400, 500,600 rpm and extruded at die temperature of 125, 150, C. The extruded samples were cut into small spherical balls with a cutting blade mounted at the outlet, rotating at 60rpm. The extrudates were dried in the tray 70 o C for 1 hour, sealed in a LDPE bag and stored at room temperature for further analysis. Experimental design and data analysis: Central composite rotatable design (CCRD) was used to design the experiments without any blocking comprising three independent processing variables (temperature, screw speed, moisture) at three levels. The statistical software package design expert 8.08(Stat ease Inc., Minneapolis, USA) was used to plan the experimental design and to analyze the data. The experimental design, with coded and actual values along with its range is shown in Table 1. Response surface methodology (RSM) involves design of experiments, selection of operating conditions in the experimental runs, fitting mathematical models and finally selecting variables levels by optimizing the response employed in the study (Khuri and Cornwell, 1987). The coding of the values was carried, as it is the pre requisite for RSM analysis using the following equation. A= (Moisture Content-16)/2. (1) B= (Temperature-150)/25.. (2) C= (Screw speed-500)/100 (3) The design values outside the range were selected for rotatability of the design. In all, 20 experiments were conducted (Table 2) with six experiments at centre point to calculate the repeatability of the method (Montgomery, 2001). The experiments were conducted in randomized order to minimize the effect of unexplained variability in the observed responses due to extraneous factors (Nath and Chattopadhyay, 2007). In the present study an attempt was made to understand the effect of variations in the processing variables on the properties of the extrudates produced from the multi-grain cereal blend. Product responses Sectional expansion index (SEI): Sectional Expansion was evaluated by measuring the diameter TABLE 1: Experimental ranges and levels of independent variables using RSM in terms of actual and coded factors of multi grain cereal breakfast. Parameter Actual Coded Actual Coded Actual Coded Moisture Content Temperature Screw Speed

3 Vol. 33, No. 2, TABLE 2: Experimental design for extrusion experiment with coded and actual variable levels Moisture Temp SS(rpm) SEI WAI WSI Color Textuure SME of five extrudates taken at random, with a digital caliper. SEI was determined by the method given by Alvarez-Martinez et al., (1988). The die had a diameter of 3.5 mm. SEI= S e /S d. (4) Where, S e and S d are the cross sectional areas of the extrudates and the die. Water absorption and water solubility indices: Water Absorption Index (WAI) was determined according to the method of Kaur and Singh (2006) and calculated as: WAI= weight of wet sediment (g)/weight of dried sediment (g).(5) Water solubility index (WSI) was determined by a method given by Nyombaire et al., WSI= [(weight of dried supernatant (g)/ (weight of dry sample (g)] x 100 (6) Texture: The hardness of samples was measured using Texture Analyzer; model TA-XT2i (Stable Micro-Systems, Surrey, England) with a compressi on plate p75. The tests were conducted at pre test speed of 1.0 mm/sec, test speed of 1mm/sec, post test speed of 5 mm/s, distance of 2.5mm, trigger force of 25 g and load cell of 250kg. The value of the first peak obtained during the compression cycle was recorded as hardness (N). An average of five replicates was recorded. Color: Samples were ground to pass through 200 m sieve. Color of extruded samples was measured using a Hunter Laboratory Instrument Model CIE 1996 (Hunter Associates Laboratory, Inc., Reston, Virginia, U.S.A.) and expressed in terms of the L a and b. A white calibration plate (L = 91.08, a = 1.25 and b = 1.43) was used as a standard (Altan et al., 2008). ÄE, which signifies the total color difference, was calculated as (Matthey and Hanna, 1997). E = ( L 2 + a 2 + b 2 ) 1/2.. (7) Where L = L sample - L standard ; a = a sample - a standard ; b = b sample - b standard Specific mechanical energy: Specific mechanical energy, the mechanical energy input per unit mass of the extrudate, was calculated by dividing the net power input to the screw by the extrudate flow rate. SME input was calculated by the following equation (Chang et al., 1999, Fan et al., 1996 and Sokhey and Chinnaswamy, 1992 ) SME( Whkg -1 ) = Screw speed ( S -1 )X torque (Nm)..(8) Mass flow rate (kgh -1 )

4 80 ASIAN JOURNAL OF DAIRY & FOOD RESEARCH Torque was recorded every 6 s for at least 12minandSMEwascalculatedandaveragedfor each processing condition. Optimization: Optimum values of the processing variables were obtained with the help of numerical optimization technique of the Design Expert software.wai, WSI, texture; SEI and color are considered to be dependent on the specific operating conditions of the twin screw extruder. The regression analysis of the responses was conducted by fitting suitable models represented by Y in equation (9). Where was the value of the fitted response at the center point of the design, i.e. point (0, 0, 0, 0). ii and ij were linear and cross product ( interaction effect ) regression terms and n denoted the number of independent variables (n= 3). The numerical optimization finds a point that maximizes the desirability function. The characteristics of the goal may be altered by adjusting the weight or importance of specific variables. Y k = ko+ ki 3 i= 1 Xi+ 3 i= 1 3 j= 1( kij XiXj)...(9) For the present study X 1 is replaced by A representing the moisture content, X 2 by B representing the temperature and X 3 by C representing the screw speed. In order to search a solution satisfying the imposed constraints the goals are combined into an overall composite function, D(x) called desirability factor (Myers and Montgomery, 2002) represented in equation (10) D(x) = (d1 d2 d3 dn) 1/n. (10) RESULTS AND DISCUSSION Sectional expansion index (SEI): Moisture content during extrusion provides the driving force for expansi on and also contributes to the rheological properties of the melt, which in turn affects the expansion. Moisture is the main plasticizer of the cereal flours, which enables them to undergo a glass transition during the extrusion process and thus facilitates the deformation of the matrix and its expansion. The predicted model for SEI is shown in Table 4 in terms of coded values. The significance of coefficients of fitted linear model was evaluated by using F-test and P-value. Moisture content (A) has a significant linear negative effect (p< 0.05) while a positive quadratic effect of moisture (A 2 ) and die temperature, (B 2 ) with screw speed were recorded at p< 0.05 (Table5). The SEI ranged from 3.31to 8.05(Table 3). The interaction of moisture content and screw speed with die temperature as shown in Fig 1(a) has a significant negative effect. With the decrease in moisture content the SEI increased. The analysis of variance showed the R 2 value of with a precision of The F-value was significant with (p< 0.05) and lack of fit non significant p< SEI decreased with the increase in moisture content as reported by Badrie and Mellowes (1991). An increase in feed moisture causes a lower vapor pressure in the melt, resulting in less flashing of moisture and a reduced SEI. Temperature has a TABLE 3: Summary of the Central Composite Rotatable Design(CCRD) experiment for optimisation Response Name Obsv Analy Min Max Mean SD Model Y1 SEI 20 Polynomial Quadratic Y2 WAI 20 Polynomial Linear Y3 WSI 20 Polynomial Linear Y4 Color 20 Polynomial Linera Y5 Texture 20 Polynomial Linear Y6 SME 20 Polynomial Linear Response TABLE 4: Summary of the best fitted curves for various properties. Equation SEI A+ 0.19B+ 0.17C AB-0.58AC-0.36BC+ 0.42A B C 2 WAI A-0.19B-0.047C+ AB AC+ 0.19BC WSI A-0.62B-0.031C-2.51AB+ 0.22AC+ 0.44BC Color A-2.30B C Texture A-2.36B+ 1.80C SME A-14.59B+ 0.89C

5 linear positive effect on the expansion as the viscosity decreases (Mercier and Feillet, 1975) and the reduced viscosity effects the bubble growth during extrusion. The degree of superheating of water in the extruder increases at higher temperatures, leading to greater expansion. A significant positive quadratic effect was recorded for temperature (B 2 ) as shown in fig 1(a). Bhattacharya et. al (1994) also reported a significant quadratic effect of barrel temperature in the range 75 to 185 C on the expansion of rice flour extrudates. Similar effect of temperature on expansion of cereal flour was reported by Ali, et al., (1996), Cha et al., (2001), red bean extrudates by Avin et al., (1992), and for fish rice flour mixtures by Giri and Bandyopadhyay (2000). Combination of speed moisture content with screw speed and die temperature had a negative effect on the SEI. With the increase in the screw speed and die temperature the melt viscosity decreases which is detrimental to expansion and has a negative effect on the volumetric expansion. It allows the cellular matrix to collapse under the high vapor pressure (Della Valle et al., 1997). Water Absorption I ndex ( WAI): The WAI measures the volume occupied by the granule or starch polymer after swelling in excess water (Sriburi and Hill, 2000). The first order polynomial was predicted for WAI and its significance of coefficients was evaluated by using F-test and P-value. Moisture content (A) and die temperature (B) had a significant linear negative effect (p< 0.05) while a positive interaction of moisture content and die temperature (AB), die temperature and screw speed (BC) was recorded (fig 1b). The WAI varied from 3.33 to 4.96 with adeq precision and R 2 = (Table 5). Maximum WAI was recorded with moisture content 14 %, temperature 125 C and screw speed 400 rpm (Table 2). It was observed that WAI increased with the decrease in moisture content which could be due to increased percentage of available sugars as reported by Sopode and Leguys (1991). It was noticed that with the increase in temperature, the WAI decreased significantly (Fig 2b) which could be due to decomposition or degradation of starch (Pelembe, et al., 2002). Similar trend was recorded by Ding et al., (2006), which could be due to dextrinization or melting of starch. Vol. 33, No. 2, The decrease in WAI with the increase in screw speed is also reported by Yagci and Gogus, (2008). On the contrary Njoki and Faller (2001) reported non-significant effect on WAI, Badrie and Mellows (1991) observed increase in WAI with increase in moisture content. The difference in the results could be due to difference in the nature of raw material and operating conditions. Water solubility index: The WSI determines the amount of free polysaccharide or polysaccharide released from the granule after addition of excess water (Sriburi and Hill, 2000). The first order polynomial was predicted as the best fit (Table 4). The significance of coefficients of fitted polynomial was evaluated by using F-test and P-value. The WSI ranged from to 26.83(table 3) with the adeq precision and R 2 of (Table 5). Maximum WSI was recorded at moisture content 18%, temp 125 C and screw speed 400 rpm (Table 2). Moisture content (A) had a significant linear positive effect and die temperature (B) had a significant negative effect (p< 0.05) as shown in (Fig 1c). Increase in moisture content increased the solubilisation of starch resulting in high WSI (Calvin et al., 2003). Similar results are reported by Altan et al., (2008) in extruded snacks from barley and tomato pomace blends and by Gutkoski and El-Dash (1999) in extruded oats. Interactions of temperature and moisture had a significant negative effect on the WSI. Color: Color is an important quality factor directly related to the acceptability of food products and is an important physical property to report for extrudate products (Fig1d). The predicted linear polynomial was found to be the best fit (Table 4), the significance of coefficients were evaluated by using F-test and P- value (p< 0.05). Moisture content (A) had a significant linear positive effect and die temperature (B) had a significant linear negative effect, effect of screw speed was found to be non-significant. DE ranged from (Table 5). With the increase in temperature the expansion increased, incorporating air cells leading to brighter color of extrudates. The change in yellowness (b value) decreased with the increase in temperature, as the thermal treatment might have destroyed the carotenoids resulting in the color loss. The results

6 82 ASIAN JOURNAL OF DAIRY & FOOD RESEARCH SEI X2 = B: Die head temp C: Screw Speed = SEI FIG 1: Response surface plot Warning! Surface truncated by selected response (Y) range - A: Moisture content - B: Die head temp (a) SEI as a function of of moisture content and die head temp WAi X2 = C: Screw Speed B: Die head temp = WAi C: Screw Speed A: Moisture content (b) WAI as a function of moisture content and screw speed WSI X2 = B: Die head temp 24 C: Screw Speed = WSI Color X2 = B: Die head temp 36 C: Screw Speed = Color Texture X2 = B: Die head temp C: Screw Speed = B: Die head temp - - A: Moisture content (c) WSI as a function of die head temp and moisture content SME X1 = B: Die head temp X2 = C: Screw Speed A: Moisture content = B: Die head temp - - A: Moisture content (d) Color as a function of die head temp and moisture content 60 B: Die head temp - - A: Moisture content (e) Texture as a function of die head temp and moisture content C: Screw Speed - - B: Die head temp (f) SME as a function of screw speed and die head temp

7 were in agreement with the results reported by Ilo (1999) and Altan (2008). Texture: Maximum hardness was recorded at 18% moisture content, C temperature and 600 rpm (Fig 1e).The linear polynomial for Hardness (H) in terms of coded variable is shown in Table 4. It ranged from N (Table 2).Presence of high moisture content resulted in harder extrudates after cooling in compari son with low moisture extrudates. The textural difference could be due to partially expanded structure of the extrudate processed at high moisture content which collapses and solidifies more rapidly during cooling and drying, thus forming a hard gritty texture (Badrie and Mellowes, 1991). Specific mechanical energy (SME): The factor is a good quantitative descriptor in extrusion process as it allows the direct comparison of different combinations of extrusion conditions such as screw speed, feeding rate, and torque. The amount of mechanical energy delivered to the extruded material determines the extent of macromolecular transformations and interactions that take place; that is, starch conversion and, consequently, the rheological properties of the Vol. 33, No. 2, melt. It ranged from kj/kg (Table 2). The linear polynomial (Table 4) was found to be the best fit for SME showing significant negative effect of temperature (fig 1f). The minimum SME of kj/kg was recorded at the highest temperature resulting in lower viscosity, promoting mobility which finally lead to increase in bubble growth (Mitchell and Areas, 1992). Similar trends are reported by Liu et al., (2000) and Onwulatta et al., (2001). Optimization: The response optimization was achieved by considering the five response parameters, selected during the design of RSM (SEI, WAI, WSI, Color and Texture). The most desirable characteristics of the extrudates were maximization of SEI and WAI, minimization of WSI, with color and texture values within the range. Maximization of SEI and WAI was considered as the most important parameter in the development of extruded breakfast cereals. The optimized variable levels and importance of responses were also taken into account. The cuboid represented in Fig 2 shows the variation of desirability with respect to process parameters. Maximum desirability of was recorded at moisture content 14 % die head temperature 125 C and screw speed 600 rpm. Desirability X2 = B: Die head temp X3 = C: Screw Speed Cube Desirability B+: B: Die head temp Prediction C+: C: Screw Speed B-: C-: A-: A+: A: Moisture content FIG 2: Cuboid representation of effect of variation of moisture content,screw speed and die head temperature on the desirability

8 84 ASIAN JOURNAL OF DAIRY & FOOD RESEARCH TABLE 5: ANOVA and model statistics for the extruded multi grain breakfast cereal. Parameters SEI WAI WSI Color Texture SME Std. Dev R-Squared Mean Adj R-Squared C.V. % PRESS Adeq Precision TABLE 6: Comparison of the Actual and Predicted values of recorded parameters Parameters SEI WAI WSI Color Texture SME Actual Predicted CONCLUSION To upscale the production of a designed product its operating conditions are of prime importance. Good expansion is an integral part of any extruded product (snack/breakfast cereal). In case of breakfast cereal SEI and WAI are the most desired characteristics. It was observed that moisture content and die temperature had a significant effect on the SEI, WSI, color and texture. The WAI was mainly affected by screw speed and die temperature. The secti onal expansion index ranged from , water solubility index %, color , texture N, SME KW hr/ Kg i n a multigrain (ri ce, wheat and maize extrudates) prepared under different processing conditions. The study suggested that 14 % moisture content, screw speed 600 rpm and die head temperature C gave the best product. There was a non-significant difference between the predicted and the experimental values (Table 6). ACKNOWLEDGEMENTS The authors are thankful to Punjab State Agricultural Marketing Board, Chandigarh, India for funding the project. REFERENCES Ali Y, Hanna M A and Chinnaswamy R (1996). Expansion characteristics of extruded corn grits. Lebensmittel- Wissenschafi und Technology. 29: Altan A, McCarthy L K and Maskan M (2008). Extrusion cooking of barley flour and process parameter optimization by using response surface methodology. Journal of Food Science & Technology. 88: Alvarez-Martinez L, Kondury K P and Harper J M (1988). A general model for expansion of extruded products. Journal of Food Science. 53: Avin D, Kim CH and Maga J A (1992). Effect of extrusion variables on the physical characteristics of red bean (Phaseolis vulgaris) flour extrudates. Journal of Food Process and Preservation. 16(5): Badrie N and Mellowes WA (1991). Effect of extrusion variables on cassava extrudates. Journal of Food Science. 56: Bhattacharya S and Prakash M (1994). Extrusion of blends of rice and chick pea flours: A response surface analysis. Journal of Food Engineering. 21: Cha J Y, Chung D S, Seib P A, Flores R A and Hanna M A (2001). Physical properties of starch-based foams as affected by extrusion temperature and moisture content. Industrial Crops Product. 14(1): Chang Y K, Martinez-Bustos F, Park T S and Kokini J L (1999). The influence of specific mechanical energy on cornmeal viscosity measured by an on-line system during twin-screw extrusion. Brazilian Journal of Chemical Engineering. 16: Della Valle G, Vergnes B, Colonna P and Patria A (1997). Relations between rheological properties of molten starches and their expansion behavior in extrusion. Journal of Food Engineering. 31(3):

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