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Millet–legume blended extrudates characteristics and process optimization using RSM
food and bioproducts processing 8 9 ( 2 0 1 1 ) 492–499
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Food and Bioproducts Processing journal homepage: www.elsevier.com/locate/fbp
Millet–legume blended extrudates characteristics and process optimization using RSM Subir K. Chakraborty a,∗ , Daya S. Singh b , Baburao K. Kumbhar c , Shalini Chakraborty d a
Department of Food Technology, Bundelkhand University, Jhansi, Uttar Pradesh, India Department of Farm Engineering, Institute of Agricultural Sciences, Benaras Hindu University, Varanasi, Uttar Pradesh, India c Department of Post Harvest Process and Food Engineering, G.B.P.U.A.T., Pantnagar, Uttrakhand, India d Directorate of Extension Services, R.V.S. Agricultural University, Gwalior, Madhya Pradesh, India b
a b s t r a c t Designed experiments were conducted to prepare extrudates from different millet–legume blend ratios (BR) of varying moisture content (MC); the extruder was operated at varying die head temperature (DHT), barrel temperature (BT), and screw speed (SS). Second order polynomial models were developed using response surface methodology (RSM) to understand the effect of the variables on density, sectional expansion index (SEI), water absorption index (WAI) and crispness of extrudates. The MC had predominant effect upon SEI, WAI and crispness, while density was most susceptible to the variations in SS. All the models were found to be statistically valid. Optimum processing condition generated from the models was: MC, 23.2%w.b.; BR, 19.9%legume; DHT, 187 ◦ C; BT, 121.1 ◦ C and SS, 123 rpm. The predicted responses in terms of density, SEI, WAI and crispness were 0.52 kg/m3 , 5.1, 9.4 and 45, respectively. The predicted values registered non-significant (p < 0.01) difference from experimental values. © 2010 The Institution of Chemical Engineers. Published by Elsevier B.V. All rights reserved. Keywords: Extrusion; Density; Water absorption index; Sectional expansion index; Crispness; Optimization
1.
Introduction
Recent years have witnessed a spate of consumers in search for food that not only adequately answer the nutritional requirements but also have characteristics that would ensure an improved state of health and well being and/or reduction of risk of diseases (Riccardi et al., 2005). Fibre rich food lowers the glycaemic index (GI) effect and can reduce the risk of postprandial oxidative stress (a factor in the onset of several chronic diseases) (Jenkins et al., 2006). High fibre consumption is associated with lesser chances of cardio vascular diseases, certain forms of cancer, blood pressure, obesity, and a healthy gastrointestinal tract (Jones, 2008). Fibre rich extruded snacks were developed in the present research work. Millets are a rich source of dietary fibre, phytochemicals, micro-nutrients, nutraceuticals, and hence now-a-days they are rightly termed as nutricereals (Desikachar, 1977). Snacks are consumed with impunity by all groups of people around the world and can be
used as a means of administering nutritious components of foods to the consumers. Enhancement of the nutritional characteristics of the extruded snacks was carried out by incorporating it with grain legume. Grain legumes are an important source of protein, minerals and vitamins for millions of people in the world, particularly in the developing countries (Singh and Singh, 1992). They improve the nutritional quality of predominantly cereal based diets of large segments of population, as cereal proteins are deficient in lysine (Deosthale, 1984). The legumes are associated with some anti-nutritional properties because of the presence of phytic acid, condensed tannins, polyphenols, protease inhibitors (trypsin and chymotrypsin) and lectins. Moreover digestibility of legume protein and legume starch is limited by the presence of these anti-nutrients (Yadav and Khetrapaul, 1994). However, extrusion has been reported to cause the biggest effect in reduction of anti-nutritional factors and has appeared
∗ Corresponding author at: Department of Post Harvest Engineering and Technology, Anand Agricultural University, Anand 388110, Gujarat, India. Tel.: +91 9574889605; fax: +91 2692 261302. E-mail address: [email protected] (S.K. Chakraborty). Received 9 March 2010; Received in revised form 24 September 2010; Accepted 13 October 2010 0960-3085/$ – see front matter © 2010 The Institution of Chemical Engineers. Published by Elsevier B.V. All rights reserved. doi:10.1016/j.fbp.2010.10.003
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Nomenclature d1 , d2 ,. . .,dn the respective responses mass of the extrudate (kg) M1 n total number of responses Ve volume of extrudate (m3 ) X1 coded moisture content coded blend ratio X2 X3 coded die head temperature coded barrel temperature X4 X5 coded screw speed coded processing variables Xi , Xj Yk responses regression coefficients ˇk e density of extrudate (kg/m3 )
to be very effective in improving both in vitro protein digestibility and in vitro starch digestibility of legumes (Alonso, 2000). During extrusion cooking the quality of the final product is dependant on the various controls of the extrusion process. Proper manipulation of any, some or all the processing conditions through adoption of well defined experimental design influence ultimate extrudate quality and functionality (Dziezak, 1990). While developing of nutritionally rich food the organoleptic properties cannot be overlooked. Fibre is known to alter viscoelastic properties and water absorption in bakery doughs and final products (Collar et al., 2007; Wang et al., 2002). Therefore, the objective for the present work was aimed at understanding the effect of changes in characteristics of the feed formulation (moisture content and blend ratio) to the extruder and extrusion conditions (die head temperature, barrel temperature and screw speed) on the properties (density, sectional expansion index, water absorption index and crispness) of the extrudates that shall supposedly be affected by the presence of fibre. Further, prediction models were developed and optimum processing conditions were also generated.
2.
Experimental
2.1.
Materials
Locally grown dehusked barnyard millet (Echinochloa frumantacea L.) was procured from the local market and legume of pigeon pea or red gram (Cajanus cajan L.) was obtained from a local legume processing unit, were used as raw materials for the present research work. The dehulled millet and the legume were cleaned manually of all the foreign material and were ground into flour by a hammer mill, separately. The ground flour was then passed through 200 mesh I.S. sieve, the underflow was collected for further research work.
2.2.
Table 1 – Design of experiments in codeda for the manufacture of extrudates of millet–legume blends. No. of experiments
Coded variables X1
16 2 2 2 2 2 6 32 a
X2
X3
X4
X5
±1 ±1 ±1 ±1 ±2 0 0 0 0 ±2 0 0 0 0 ±2 0 0 0 0 ±2 0 0 0 0 0 0 0 0 Total no. of experiments
±1 0 0 0 0 ±2 0
Code ‘0’ is for centre point value of the processing parameter, ‘±1’ for factorial points and ‘±2’ for the axial points; X1 , coded moisture content; X2 , coded blend ratio; X3 , coded die head temperature; X4 , coded barrel temperature; X5 , coded screw speed.
2001). The experiments were conducted in a randomized order to minimize the effects of unexplained variability in the observed responses due to extraneous factors (Nath and Chattopadhyay, 2007). The processing variables for the present research work were; moisture content (MC) and blend ratio (BR) of the feed formulation expressed as per cent wet basis (w.b.) and percent legume, respectively; die head temperature (DHT), ◦ C; barrel temperature (BT), ◦ C and screw speed (SS), rpm of the extruder. In the present study, an attempt was made to understand the effect of variations in the processing variables on some properties of the extrudates produced from the blends of millet and legume flour. Response surface methodology (RSM) which involves design of experiments, selection of levels of variables in experimental runs, fitting mathematical models and finally selecting variables’ levels by optimizing the response was employed in the study (Khuri and Cornell, 1987). The coding of the values of the variables was carried, as it is a prerequisite for response surface methodology analysis, using following equations: X1 =
Moisture content − 18 3
(1)
X2 =
Blend ratio − 20 4
(2)
X3 =
Diehead temperature − 180 10
(3)
X4 =
Barrel temperature − 120 10
(4)
X5 =
Screw speed − 120 10
(5)
The actual values and the coded values of the processing variables are reported in Table 2.
Experimental design 2.3.
The central composite rotatable design (CCRD) is a powerful tool for optimization and drastically reduces the number of experiments for studies involving more than two independent variables. In the present study CCRD was used to design the experiments comprising five independent processing variables at five different levels. In all thirty-two experiments were conducted (Table 1), with six experiments at centre point to calculate the repeatability of the method (Montgomery,
Extrusion cooking
Blends of millet and legume flour were prepared as per the experimental design, i.e. 12, 16, 20, 24, and 28% legume. Simultaneously, the moisture content of the blends was also ascertained. Additional water required for desired moisture content levels in the blends i.e. 12, 15, 18, 21, and 24%w.b. was calculated and added (Pelembe et al., 2002; AACC, 1983). The samples were then fed into a single screw laboratory
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Table 2 – Coded and uncoded levels of processing variables. Codes for the values
−2 −1 0 +1 +2
Processing variables and levels MC, %w.b. (X1 )
BR, %legume (X2 )
DHT, ◦ C (X3 )
BT, ◦ C (X4 )
SS, rpm (X5 )
12 15 18 21 24
12 16 20 24 28
160 170 180 190 200
100 110 120 130 140
100 110 120 130 140
X1 , coded moisture content; X2 , coded blend ratio; X3 , coded die head temperature; X4 , coded barrel temperature; X5 , coded screw speed.
peaks obtained in the product during compression. Mean of five observations was recorded as the result.
extruder (model Brabender D47055 Duisburg, Germany). The extruder had controls available to monitor the system variables DHT, BT, and SS for adhering to the requirements of experimental design. However, there were certain system variables of the extruder which was kept fixed during the course of the research work, feed screw speed rate (rpm), length-todiameter ratio of barrel, and compression ratio, was 20 i.e. (3.48 kg/h), 20:1, and 3:1, respectively.
2.9.
2.4.
Yk = ˇko +
Moisture content
Statistical analysis
The experimental data obtained were analysed after fitting them into a second order polynomial model (7). 5
ˇki Xi +
5 5
i=1
The moisture content was determined using standard hot air oven method (AOAC, 1984).
2.5.
Density
The density (e ) of extrudate was determined by sand displacement method. A measuring cylinder of capacity 250 ml was used. Sand was filled into the measuring cylinder up to volume, V. The container was then emptied and a weighed amount of product, M1 was placed inside the cylinder. The remaining space was filled with the same amount of sand which was earlier emptied from the cylinder. The sand now fills up to the mark, V1 . Thus, the volume of the extrudate is the difference between V and V1 , say Ve . The density of the extrudate was therefore defined as: e =
2.6.
M1 Ve
(6)
Sectional expansion index (SEI)
SEI was determined by dividing the cross sectional area (mean of five measurements) of the extrudates by the cross-sectional area of the die nozzle (Alvarez – Martinez et al., 1988). The die had a diameter of 5 mm.
2.7.
Water absorption index (WAI)
WAI was determined by the method of Anderson et al. (1969).
2.8.
Crispness
The crispness was measured with the help of a texture analyzer (model TA – XT2i, Stable Micro Systems, Surrey, England) and the probe was 45◦ chisel end knife blade (TA-42) along with bell lock. The tests were conducted with texture analyzer at pre-test speed of 5.0 mm/s, test speed of 2.0 mm/s, post-test speed of 10 mm/s, distance 10 mm, trigger force of 25 g, and load cell of 5 kg. Crispness was measured in terms of number of major positive peaks (Nath and Chattopadhyay, 2007). A macro was developed which counts number of major
ˇkij Xi Xj
(7)
i=1 i≥1
The models obtained were checked for their statistical validity with the help of regression analysis and analysis of variance (ANOVA). The adequacy of the models was determined using model analysis, lack-of-fit test and R2 (coefficient of determination) analysis as outlined by Weng et al. (2001). The lack-of-fit is a measure of the failure of a model to represent data in the experimental domain at which points were not included in the regression or variations in the models cannot be accounted for by random error (Montgomery, 2001). A model is said to be adequate in describing the response if the lack-of-fit is insignificant. The R2 is defined as the ratio of the explained variation to the total variation and is a measure of the degree of fit (Haber and Runyon, 1977). If the R2 value for a model is more than 80% then the model can be considered for further analysis (Filmore et al., 1976). The predominant processing variable in relation to each response was determined by understanding contribution of particular response to the model R2 value (Alvarez – Martinez et al., 1988). Coefficient of variation (c.v.) indicates the relative dispersion of the experimental points from the prediction of the model. It is desirable to have a c.v. of less than 10%, suggesting that an experiment is reproducible (Nath and Chattopadhyay, 2007).
2.10.
Optimization
Optimum values of the processing variables were obtained with the help of the numerical optimization technique of the Design-Expert software (Design Expert ver. 7.1.6). The software necessitates assigning goals to the processing variables and the responses. All the processing variables were kept within range while the responses were either maximized (SEI, WAI, crispness) or kept in range (density). In order to search a solution satisfying the imposed constraints, the goals are combined into an overall composite function, D(x), called the desirability function (Myers and Montgomery, 2002), which is defined as: D(x) = (d1 × d2 × . . . × dn )
1/n
(8)
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Table 3 – ANOVA and regression coefficients of the second order polynomial models of the various responses. Predictor
Intercept X1 X2 X3 X4 X5 X1 X2 X1 X3 X1 X4 X1 X5 X2 X3 X2 X4 X2 X5 X3 X4 X3 X5 X4 X5 X12 X22 X12 X42 X52 ANOVA R2 , % Model, F-value c.v., % Lack of fit
Regression coefficients (ˇ) Density
SEI
WAI
Crispness
0.375 0.050*** 0.00 N.S. 0.00 N.S. 0.017*** −0.019*** 0.018** 0.032*** 0.017** 0.026*** −0.014* −0.015** −0.045*** −0.00 N.S. 0.012* −0.014* 0.00 N.S. −0.00 N.S. 0.00 N.S. 0.00 N.S. 0.00 N.S.
4.140 0.580*** 0.184** 0.03 N.S. 0.03 N.S. −0.05 N.S. 0.176* 0.367*** 0.378*** 0.240** −0.280*** −0.166* −0.533*** −0.10 N.S. 0.06 N.S. −0.07 N.S. −0.06 N.S. −0.138* −0.05 N.S. −0.126* 0.03 N.S.
8.280 0.792*** 0.01 N.S. 0.379*** −0.411*** −0.10 N.S. 0.17 N.S. −0.11 N.S. −0.12 N.S. 0.16 N.S. 0.337** 0.279** −0.07 N.S. 0.319** 0.07 N.S. −0.19 N.S. −0.01 N.S. −0.18 N.S. −0.06 N.S. −0.42 N.S. −0.77 N.S.
38.86 4.58*** 1.17** −0.0 N.S. 0.5 N.S. −0.5 N.S. 1.38** 2.38*** 3.38*** 1.75** −2.13*** −1.63** −4.00*** −0.3 N.S. – −0.7 N.S. −0.2 N.S. −1.11** −0.2 N.S. −0.99* 0.6 N.S.
95.4 11.54*** 6.99 N.S.
94.5 9.44*** 8.89 N.S.
95.9 12.92*** 6.21 N.S.
95.3 11.242*** 6.59 N.S.
X1 , coded moisture content; X2 , coded blend ratio; X3 , coded die head temperature; X4 , coded barrel temperature; X5 , coded screw speed. N.S., not significant (p > 0.1). ∗ ∗∗ ∗∗∗
Significant at 10% (p < 0.1). Significant at 5% (p < 0.05) Significant at 1% (p < 0.01).
The numerical optimization finds a point that maximizes the desirability function. The characteristics of a goal may be altered by adjusting the weight or importance of specific variables (Design Expert Version, 2002).
3.
Results and discussion
Experiments were conducted as per CCRD, and RSM was applied to the experimental data using a commercial statistical package, Design Expert Version (2002). The estimated regression coefficients of the second order polynomial models for the various responses and their statistical validity defining values are reported in Table 3. The R2 values for all the models were more than 80%, thus the models developed had the capability of being used to navigate the design space and to predict the responses correctly. Furthermore, the F-values reflected that the all the models were significant and c.v. less than 10% establishes that the experiments were conducted with reasonable accuracy and suggesting that the models can be reproducible (Montgomery, 2001). The lack-of-fit was also insignificant for all the models.
3.1.
Density
The density of the extrudates varied between 0.234 and 0.501 kg/m3 . Density is a measure of how much expansion has occurred as a result of extrusion. The heat developed during extrusion can increase the temperature of the moisture above the boiling point so that when the extrudate exits from the die, a part of the moisture would quickly flash-off as steam
and result in an expanded structure with large alveoli and low density. On the other hand, if not enough heat is generated to flash-off enough of the moisture (either through low process temperature or high feed moisture), less expansion occurs resulting in a high bulk density product with collapsed cells which usually disintegrates on cooling. High density product is an indication of more uniform and continuous protein matrix and therefore, the extrudate is dense with parallel layers, no air pockets and is not spongy upon hydration (Filli, 2009). All the processing variables except DHT and BR had significant (p < 0.01) effect upon density. The density increased with the increase in the levels of all the processing variables except SS. The decrease in density with increase in SS may be attributed to the structural breakdown that might have accompanied the high SS. Guha et al. (1997) made similar observations in their study of extrudates from rice flour. Increasing screw speed tends to increase the shearing effect, this causes protein and starch molecules to be stretched farther apart, weakening bonds and resulting in a puffer product and consequently decreasing density (Filli, 2009). All the interactions of screw speed significantly affected density (Table 3). The interaction of SS with MC and BR were effective at p < 0.01, while with DHT and BT at p < 0.1. The most prominent interaction of SS was with BR (ˇ = −0.045). It can be observed from Fig. 1 that as the levels of SS and BR increased the density would decrease. This can be attributed to dual effect of low residence time because of the high SS and lesser millet starch available for gelatinization because of high BR. However, at low SS because of enhanced residence time the density increased. The predominance of SS over other processing variables in
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Fig. 1 – Contour plot (a) and response surface (b) depicting the behaviour of density at different SS and BR while the other processing variables are at centre point.
affecting density is represented in Table 4. The SS had greater contribution to R2 followed by MC.
3.2.
Fig. 2 – Contour plot (a) and response surface (b) depicting the behaviour of SEI at different SS and BR while the other processing variables are at centre point. Expansion of the extrudates is heavily dependent upon the moisture content of the feed formulation to the extruder (Alvarez – Martinez et al., 1988). Similar observations were made in the present study as well (Table 4).
Sectional expansion index (SEI)
The maximum and minimum values of SEI of the extrudates were 5.46 and 1.83, respectively. The MC (p < 0.01), BR (p < 0.05), DHT and BT had positive effect on SEI. Increase in SEI with MC has also been observed in the extrudates made from whole wheat meal (Singh and Smith, 1997). Positive correlation between BT and SEI has been observed by Pelembe et al. (2002), Singh and Smith (1997), and Owusu – Ansah et al. (1984). The negative effect of SS is perhaps because of the complete expansion associated with low residence time resulting in an increased SEI. All the interactions of MC and BR with other variables were found to be significantly affecting SEI (Table 3). There was a marked significant (p < 0.01) interaction between BR and SS (ˇ = −0.533), Fig. 2. The SEI exhibited highest values when extrusion was carried out at low SS and high BR while other variables were at centre point. The quadratic terms of BR and BT were also observed to be significantly affecting SEI at p < 0.1.
Table 4 – Predominant effect of processing variables upon responses. Processing variables
MC BR DHT BT SS Total R2 , %
Contributiona to R2 , % Density (kg/m3 )
SEI
WAI
Crispness
32.7 7.7 13.9 23.1 36.0 95.4
58.8 33.9 15.6 14.6 24.1 94.5
57.0 27.7 13.7 11.7 33.6 95.9
59.8 32.7 11.6 19.0 23.3 95.3
MC, moisture content; BR, blend ratio; DHT, die head temperature; BT, barrel temperature; SS, screw speed; SEI, sectional expansion index; WAI, water absorption index. a
Contribution to R2 of a given variable should be interpreted as the amount by which the total R2 would be reduced if that variable was removed from the regression equation.
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3.3.
Water absorption index (WAI)
The extrudates exhibited WAI in the range of 3.90–9.60. Positive correlation was demonstrated by the significant effects of MC (p < 0.01), BR, and DHT (p < 0.01) towards WAI. The result shows that an increase in BT was significant at p < 0.01 and the SS led to a decrease in WAI (Table 3). WAI has been reported to increase with increase in MC and BR while extruding corn (Gomez and Aguilera, 1983) and sorghum-cowpea composites (Pelembe et al., 2002), respectively. The comminution of starch molecules as a result of degradation caused by high BT may have made it imperative for the extrudates to hold less water, hence a lesser WAI. Similar observations were registered by Anderson et al. (1969), Guha et al. (1997), Singh and Smith (1997), and Mercier and Feillet (1975). The negative correlation (ˇ = −0.103) between WAI and SS can be attributed to the extended time of stay of the feed formulation inside the barrel of the extruder resulting in extensive cooking, ergo an increased WAI. Guha et al. (1997) have also made similar observations in their study of extrusion cooking with rice flour. The interactions of BT with BR and DHT, and the interaction of BR with DHT were found to significantly affect WAI at p < 0.05. The negative effect of the BT–SS (ˇ = −0.193) interaction on WAI observed in this study is in agreement with earlier findings (Guha et al., 1997; Gomez and Aguilera, 1983). It can be seen from Fig. 3 that the WAI is maximum when the SS and BT both are at values lower than their centre points. The MC of the feed formulation to the extruder and the SS of extruder were variables predominantly affecting the WAI (Table 4).
3.4.
Crispness
The crispness of the extrudates ranged from 22 to 50. There was an increase in the crispness of the extrudates with an increase in the MC (p < 0.01), BR (p < 0.05) and BT. The increase in DHT and SS was marked by a decrease in the crispness (Table 3). Crispness of the extrudates was significantly affected by all the interactions of the MC and BR with other process variables. However, the interactive effect of BR and SS was outstanding (ˇ = −4.00). Perusal of Fig. 4 shows that a low SS and high BR combination resulted in high crispness values. A decreased SS means longer residence time or more cooked starch, hence crisper extrudates. A feed formulation comprising high millet content (or low BR) resulted in crispy extrudate even at high SS; this may be because of the millet starch get-
Fig. 3 – Contour plot (a) and response surface (b) depicting the behaviour of WAI at different SS and BT while the other processing variables are at centre point. ting cooked readily under the existing extruder conditions. The quadratic terms BR and BT were significantly affecting crispness at p < 0.05 and p < 0.1, respectively. MC of the feed formulation has a crucial role to play in the generation of vapour bubbles in the feed formulation during the exit of the extrudates from the extruder-die (Kokini et al., 1992). The present study also witnessed overwhelming effect of MC on the extru-
Table 5 – Applied constraints to obtain optimized values of processing variables and the predicted responses. Variables
Condition
MC BR DHT BT SS Responses Density SEI WAI Crispness
In range In range In range In range In range In range Maximize Maximize Maximize
Lower limit 12 12 160 100 100 0.234 1.825 3.9 22
Upper limit 24 28 200 140 140 0.523 5.457 9.603 50
Importancea
Optimum valueb
3 3 3 3 3
23.2 19.9 187.0 121.1 123
3 5 5 5
0.52 5.1 9.4 45
MC, moisture content; BR, blend ratio; DHT, die head temperature; BT, barrel temperature; SS, screw speed; SEI, sectional expansion index; WAI, water absorption index. a b
The value of importance is as per the default setting of the software. The desirability for this result was 0.98.
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Table 6 – Results of the t-test conducted to compare the predicted and the experimentally recorded values. Response
Predicted valuea
Actual value ± SD
Density SEI WAI Crispness
9.4 5.1 0.52 45
9.14 5.2 0.532 46
± ± ± ±
0.58 0.45 0.05 3.35
Standard error
% Variation
Mean difference
Significance (2 tailed)
0.262 0.202 0.023 1.50
2.84 1.92 2.26 1.75
0.26 0.1 0.012 1.0
1.00 0.494 0.521 0.667
Ho : o = 1 , tcal < ttable at p < 0.1, ‘Ho ’ was accepted. SEI, sectional expansion index; WAI, water absorption index. a
Mean of five replications.
date crispness, it is represented in terms of contribution to model R2 (Table 4).
3.5.
Optimization and model verification performance
Numerical optimization of the process variables was carried out with the help of a commercial software (Design Expert Version, 2002). The optimization was carried out under certain applied constraints. The software was used to generate optimum processing conditions and to predict the corresponding responses as well. The applied constraints and the
predicted optimum values obtained for the various responses are reported in Table 5. Extrusion cooking was carried out under the optimum processing conditions and the responses were recorded (mean of 5 measurements). The veracity of values of the responses predicted by the software was ascertained with the help of a two-tailed, one sample t-test (Table 6). The results of the t-test demonstrated no significant difference between the values of the predicted responses and the recorded response. Thus, establishing the suitability of the models to predict the various responses as desired for a particular application.
4.
Conclusions
Designed experiments using CCRD successfully exhibited the effect of processing variables (MC, BR, DHT, BT and SS) on the responses (density, SEI, WAI and crispness) of extrudates manufactured from different blends of millet and legume. The models were found to be statistically valid and demonstrated adequate information regarding the behaviour of the responses upon variation in the processing variables. Optimum processing conditions and the corresponding predicted response could be obtained with the help of the models. The predicted response at optimum conditions had nonsignificant difference from the experimental values.
References
Fig. 4 – Contour plot (a) and response surface (b) depicting the behaviour of crispness at different BR and SS while the other processing variables are at centre point.
AACC, 1983. Approved Methods (no. 26–95). American Association of Cereal Chemists, St. Paul, MN. Alonso, R., 2000. Effects of extrusion and traditional processing methods on anti nutrients and in-vitro digestibility of protein and starch in faba and kidney beans. Food Chem. 68 (2), 159–165. Alvarez – Martinez, L., Koundry, K.P., Harper, J.M., 1988. A general model for expansion of extruded products. J. Food Sci. 53, 609–615. Anderson, R.A., Conway, H.R., Pfeifer, V.F., Griffin Jr., E.L., 1969. Gelatinization of corn grits by roll and extrusion cooking. Cereal Sci. Today 14, 4–7. AOAC, 1984. Approved Methods of Analysis, fourteen ed. Association of Official Analytical Chemists, Washington, USA. Collar, C., Santos, E., Rosell, C.M., 2007. Assessment of the rheological profile of fibre-enriched bread doughs by response surface methodology. J. Food Eng. 78 (3), 820–826. Deosthale, Y.G., 1984. Food processing and nutritive value of legumes. In: Srivastava, H.C., Bhaskaran, S., Rao, M.V. (Eds.), Pulse Production Constraints Opportunities. Proceedings of Symposium on Increasing Pulse Production in India. Oxford and IBH Publishing Co, New Delhi, pp. 377–388. Design Expert Version 7.1.6., (2002). Stat-Ease, Inc., MN, USA. Desikachar, H.S.R., 1977. Processing of sorghum and millets for versatile food uses in India. In: Proceedings of symposium on sorghum and millets for human food, Tropical Products Institute, London, pp. 41–46.
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Dziezak, J.D., 1990. Taking the gamble out of product development. Food Technol. 44 (6), 110–117. Filli, K.B., 2009. Production, optimization and characterization of extruded fura from pearl millet and grain legume flour mixtures. Unpublished PhD Thesis, University of Maiduguri, Maiduguri, Nigeria. Filmore, E.B., Kramer, A., Gerald, K., 1976. Response surface techniques. In: Filmore, F.E., Kramer, A., Gerald, K. (Eds.), Systems Analysis for Food Industry. AVI, Westport, CT, p. 127. Gomez, M.H., Aguilera, J.M., 1983. Changes in the starch fraction during extrusion-cooking of corn. J. Food Sci. 48, 378–381. Guha, M., Ali, S.Z., Bhattacharya, S., 1997. Twin screw extrusion of rice flour without a die: effect of barrel temperature and screw speed on extrusion and extrudates characteristics. J. Food Eng. 32 (3), 251–267. Haber, A., Runyon, R., 1977. General Statistics, third ed. Addison-Wesley Publishing Company, Reading, MA. Jenkins, D.J.A., Kendall, C.W.C., Josse, A.R., Salvatore, S., Brighenti, F., Augustin, L.S.A., 2006. Almonds decrease postprandial glycemia, insulinemia, and oxidative damage in healthy individuals. J. Nutr. 136, 2987–2992. Jones, J.M., 2008. Fibre, whole grains and disease prevention. In: Hamaker, B.R. (Ed.), Technology of Functional Cereal Products. CRC Press, Boca Raton, FL, pp. 46–62. Khuri, A.I., Cornell, J.A., 1987. Response surfaces designs and analysis. Marcel Decker, New York. Kokini, J.L., Chang, C.N., Lai, L.S., 1992. The role of rheological properties on extrudate expansion. In: Kokini, J.L., Ho, C.T., Karwe, M.V. (Eds.), Food Extrusion Science and Technology. Dekker, NY, pp. 631–652. Mercier, C., Feillet, P., 1975. Modification of carbohydrate components by extrusion-cooking of cereal products. Cereal Chem. 52 (3), 283–297.
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Montgomery, D.C., 2001. Design and analysis of experiments, fifth ed. Wiley, New York, USA, pp. 455–492. Myers, R., Montgomery, D.C., 2002. Response Surface Methodology. Wiley, New York, USA. Nath, A., Chattopadhyay, P.K., 2007. Optimization of oven toasting for improving crispness and other quality attributes of ready to eat potato-soy snack using response surface methodology. J. Food Eng. 80 (1), 1282–1292. Owusu – Ansah, J., van de Voort, F.R., Stanley, D.W., 1984. Textural and micro structural changes in corn starch as a function of extrusion variables. Can. Inst. Food Sci. Technol. J. 17 (2), 65. Pelembe, L.A.M., Erasmus, C., Taylor, J.R.N., 2002. Development of a protein-rich composite sorghum-cowpea instant porridge by extrusion cooking process. Lebensm. Wiss. Technol. 35 (2), 120–127. Riccardi, G., Capaldo, B., Vaccaro, O., 2005. Functional foods in the management of obesity and type 2 diabetes. Curr. Opin. Clin. Nutr. 8 (6), 630–635. Singh, N., Smith, A.C., 1997. A comparison of wheat starch, whole wheat meal and oat flour in the extrusion cooking process. J. Food Eng. 34 (1), 15–32. Singh, U., Singh, B., 1992. Tropical grain legumes as important human foods. Econ. Bot. 46, 310–321. Wang, J., Rosell, C.M., de Barber, C.B., 2002. Effect of the addition of different fibres on wheat dough performance and bread quality. Food Chem. 79, 221–226. Weng, W., Liu, W., Lin, W., 2001. Studies on the optimum models of the dairy product Kou Woan Lao using response surface methodology. Asian Aust. J. Anim. 14 (10), 1470–1476. Yadav, S., Khetrapaul, N., 1994. Indigenous legume fermentation: effect of some anti nutrients and in vitro digestibility of starch and protein. Food Chem. 50 (4), 403–406.
Contents lists available at ScienceDirect
Food and Bioproducts Processing journal homepage: www.elsevier.com/locate/fbp
Millet–legume blended extrudates characteristics and process optimization using RSM Subir K. Chakraborty a,∗ , Daya S. Singh b , Baburao K. Kumbhar c , Shalini Chakraborty d a
Department of Food Technology, Bundelkhand University, Jhansi, Uttar Pradesh, India Department of Farm Engineering, Institute of Agricultural Sciences, Benaras Hindu University, Varanasi, Uttar Pradesh, India c Department of Post Harvest Process and Food Engineering, G.B.P.U.A.T., Pantnagar, Uttrakhand, India d Directorate of Extension Services, R.V.S. Agricultural University, Gwalior, Madhya Pradesh, India b
a b s t r a c t Designed experiments were conducted to prepare extrudates from different millet–legume blend ratios (BR) of varying moisture content (MC); the extruder was operated at varying die head temperature (DHT), barrel temperature (BT), and screw speed (SS). Second order polynomial models were developed using response surface methodology (RSM) to understand the effect of the variables on density, sectional expansion index (SEI), water absorption index (WAI) and crispness of extrudates. The MC had predominant effect upon SEI, WAI and crispness, while density was most susceptible to the variations in SS. All the models were found to be statistically valid. Optimum processing condition generated from the models was: MC, 23.2%w.b.; BR, 19.9%legume; DHT, 187 ◦ C; BT, 121.1 ◦ C and SS, 123 rpm. The predicted responses in terms of density, SEI, WAI and crispness were 0.52 kg/m3 , 5.1, 9.4 and 45, respectively. The predicted values registered non-significant (p < 0.01) difference from experimental values. © 2010 The Institution of Chemical Engineers. Published by Elsevier B.V. All rights reserved. Keywords: Extrusion; Density; Water absorption index; Sectional expansion index; Crispness; Optimization
1.
Introduction
Recent years have witnessed a spate of consumers in search for food that not only adequately answer the nutritional requirements but also have characteristics that would ensure an improved state of health and well being and/or reduction of risk of diseases (Riccardi et al., 2005). Fibre rich food lowers the glycaemic index (GI) effect and can reduce the risk of postprandial oxidative stress (a factor in the onset of several chronic diseases) (Jenkins et al., 2006). High fibre consumption is associated with lesser chances of cardio vascular diseases, certain forms of cancer, blood pressure, obesity, and a healthy gastrointestinal tract (Jones, 2008). Fibre rich extruded snacks were developed in the present research work. Millets are a rich source of dietary fibre, phytochemicals, micro-nutrients, nutraceuticals, and hence now-a-days they are rightly termed as nutricereals (Desikachar, 1977). Snacks are consumed with impunity by all groups of people around the world and can be
used as a means of administering nutritious components of foods to the consumers. Enhancement of the nutritional characteristics of the extruded snacks was carried out by incorporating it with grain legume. Grain legumes are an important source of protein, minerals and vitamins for millions of people in the world, particularly in the developing countries (Singh and Singh, 1992). They improve the nutritional quality of predominantly cereal based diets of large segments of population, as cereal proteins are deficient in lysine (Deosthale, 1984). The legumes are associated with some anti-nutritional properties because of the presence of phytic acid, condensed tannins, polyphenols, protease inhibitors (trypsin and chymotrypsin) and lectins. Moreover digestibility of legume protein and legume starch is limited by the presence of these anti-nutrients (Yadav and Khetrapaul, 1994). However, extrusion has been reported to cause the biggest effect in reduction of anti-nutritional factors and has appeared
∗ Corresponding author at: Department of Post Harvest Engineering and Technology, Anand Agricultural University, Anand 388110, Gujarat, India. Tel.: +91 9574889605; fax: +91 2692 261302. E-mail address: [email protected] (S.K. Chakraborty). Received 9 March 2010; Received in revised form 24 September 2010; Accepted 13 October 2010 0960-3085/$ – see front matter © 2010 The Institution of Chemical Engineers. Published by Elsevier B.V. All rights reserved. doi:10.1016/j.fbp.2010.10.003
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Nomenclature d1 , d2 ,. . .,dn the respective responses mass of the extrudate (kg) M1 n total number of responses Ve volume of extrudate (m3 ) X1 coded moisture content coded blend ratio X2 X3 coded die head temperature coded barrel temperature X4 X5 coded screw speed coded processing variables Xi , Xj Yk responses regression coefficients ˇk e density of extrudate (kg/m3 )
to be very effective in improving both in vitro protein digestibility and in vitro starch digestibility of legumes (Alonso, 2000). During extrusion cooking the quality of the final product is dependant on the various controls of the extrusion process. Proper manipulation of any, some or all the processing conditions through adoption of well defined experimental design influence ultimate extrudate quality and functionality (Dziezak, 1990). While developing of nutritionally rich food the organoleptic properties cannot be overlooked. Fibre is known to alter viscoelastic properties and water absorption in bakery doughs and final products (Collar et al., 2007; Wang et al., 2002). Therefore, the objective for the present work was aimed at understanding the effect of changes in characteristics of the feed formulation (moisture content and blend ratio) to the extruder and extrusion conditions (die head temperature, barrel temperature and screw speed) on the properties (density, sectional expansion index, water absorption index and crispness) of the extrudates that shall supposedly be affected by the presence of fibre. Further, prediction models were developed and optimum processing conditions were also generated.
2.
Experimental
2.1.
Materials
Locally grown dehusked barnyard millet (Echinochloa frumantacea L.) was procured from the local market and legume of pigeon pea or red gram (Cajanus cajan L.) was obtained from a local legume processing unit, were used as raw materials for the present research work. The dehulled millet and the legume were cleaned manually of all the foreign material and were ground into flour by a hammer mill, separately. The ground flour was then passed through 200 mesh I.S. sieve, the underflow was collected for further research work.
2.2.
Table 1 – Design of experiments in codeda for the manufacture of extrudates of millet–legume blends. No. of experiments
Coded variables X1
16 2 2 2 2 2 6 32 a
X2
X3
X4
X5
±1 ±1 ±1 ±1 ±2 0 0 0 0 ±2 0 0 0 0 ±2 0 0 0 0 ±2 0 0 0 0 0 0 0 0 Total no. of experiments
±1 0 0 0 0 ±2 0
Code ‘0’ is for centre point value of the processing parameter, ‘±1’ for factorial points and ‘±2’ for the axial points; X1 , coded moisture content; X2 , coded blend ratio; X3 , coded die head temperature; X4 , coded barrel temperature; X5 , coded screw speed.
2001). The experiments were conducted in a randomized order to minimize the effects of unexplained variability in the observed responses due to extraneous factors (Nath and Chattopadhyay, 2007). The processing variables for the present research work were; moisture content (MC) and blend ratio (BR) of the feed formulation expressed as per cent wet basis (w.b.) and percent legume, respectively; die head temperature (DHT), ◦ C; barrel temperature (BT), ◦ C and screw speed (SS), rpm of the extruder. In the present study, an attempt was made to understand the effect of variations in the processing variables on some properties of the extrudates produced from the blends of millet and legume flour. Response surface methodology (RSM) which involves design of experiments, selection of levels of variables in experimental runs, fitting mathematical models and finally selecting variables’ levels by optimizing the response was employed in the study (Khuri and Cornell, 1987). The coding of the values of the variables was carried, as it is a prerequisite for response surface methodology analysis, using following equations: X1 =
Moisture content − 18 3
(1)
X2 =
Blend ratio − 20 4
(2)
X3 =
Diehead temperature − 180 10
(3)
X4 =
Barrel temperature − 120 10
(4)
X5 =
Screw speed − 120 10
(5)
The actual values and the coded values of the processing variables are reported in Table 2.
Experimental design 2.3.
The central composite rotatable design (CCRD) is a powerful tool for optimization and drastically reduces the number of experiments for studies involving more than two independent variables. In the present study CCRD was used to design the experiments comprising five independent processing variables at five different levels. In all thirty-two experiments were conducted (Table 1), with six experiments at centre point to calculate the repeatability of the method (Montgomery,
Extrusion cooking
Blends of millet and legume flour were prepared as per the experimental design, i.e. 12, 16, 20, 24, and 28% legume. Simultaneously, the moisture content of the blends was also ascertained. Additional water required for desired moisture content levels in the blends i.e. 12, 15, 18, 21, and 24%w.b. was calculated and added (Pelembe et al., 2002; AACC, 1983). The samples were then fed into a single screw laboratory
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Table 2 – Coded and uncoded levels of processing variables. Codes for the values
−2 −1 0 +1 +2
Processing variables and levels MC, %w.b. (X1 )
BR, %legume (X2 )
DHT, ◦ C (X3 )
BT, ◦ C (X4 )
SS, rpm (X5 )
12 15 18 21 24
12 16 20 24 28
160 170 180 190 200
100 110 120 130 140
100 110 120 130 140
X1 , coded moisture content; X2 , coded blend ratio; X3 , coded die head temperature; X4 , coded barrel temperature; X5 , coded screw speed.
peaks obtained in the product during compression. Mean of five observations was recorded as the result.
extruder (model Brabender D47055 Duisburg, Germany). The extruder had controls available to monitor the system variables DHT, BT, and SS for adhering to the requirements of experimental design. However, there were certain system variables of the extruder which was kept fixed during the course of the research work, feed screw speed rate (rpm), length-todiameter ratio of barrel, and compression ratio, was 20 i.e. (3.48 kg/h), 20:1, and 3:1, respectively.
2.9.
2.4.
Yk = ˇko +
Moisture content
Statistical analysis
The experimental data obtained were analysed after fitting them into a second order polynomial model (7). 5
ˇki Xi +
5 5
i=1
The moisture content was determined using standard hot air oven method (AOAC, 1984).
2.5.
Density
The density (e ) of extrudate was determined by sand displacement method. A measuring cylinder of capacity 250 ml was used. Sand was filled into the measuring cylinder up to volume, V. The container was then emptied and a weighed amount of product, M1 was placed inside the cylinder. The remaining space was filled with the same amount of sand which was earlier emptied from the cylinder. The sand now fills up to the mark, V1 . Thus, the volume of the extrudate is the difference between V and V1 , say Ve . The density of the extrudate was therefore defined as: e =
2.6.
M1 Ve
(6)
Sectional expansion index (SEI)
SEI was determined by dividing the cross sectional area (mean of five measurements) of the extrudates by the cross-sectional area of the die nozzle (Alvarez – Martinez et al., 1988). The die had a diameter of 5 mm.
2.7.
Water absorption index (WAI)
WAI was determined by the method of Anderson et al. (1969).
2.8.
Crispness
The crispness was measured with the help of a texture analyzer (model TA – XT2i, Stable Micro Systems, Surrey, England) and the probe was 45◦ chisel end knife blade (TA-42) along with bell lock. The tests were conducted with texture analyzer at pre-test speed of 5.0 mm/s, test speed of 2.0 mm/s, post-test speed of 10 mm/s, distance 10 mm, trigger force of 25 g, and load cell of 5 kg. Crispness was measured in terms of number of major positive peaks (Nath and Chattopadhyay, 2007). A macro was developed which counts number of major
ˇkij Xi Xj
(7)
i=1 i≥1
The models obtained were checked for their statistical validity with the help of regression analysis and analysis of variance (ANOVA). The adequacy of the models was determined using model analysis, lack-of-fit test and R2 (coefficient of determination) analysis as outlined by Weng et al. (2001). The lack-of-fit is a measure of the failure of a model to represent data in the experimental domain at which points were not included in the regression or variations in the models cannot be accounted for by random error (Montgomery, 2001). A model is said to be adequate in describing the response if the lack-of-fit is insignificant. The R2 is defined as the ratio of the explained variation to the total variation and is a measure of the degree of fit (Haber and Runyon, 1977). If the R2 value for a model is more than 80% then the model can be considered for further analysis (Filmore et al., 1976). The predominant processing variable in relation to each response was determined by understanding contribution of particular response to the model R2 value (Alvarez – Martinez et al., 1988). Coefficient of variation (c.v.) indicates the relative dispersion of the experimental points from the prediction of the model. It is desirable to have a c.v. of less than 10%, suggesting that an experiment is reproducible (Nath and Chattopadhyay, 2007).
2.10.
Optimization
Optimum values of the processing variables were obtained with the help of the numerical optimization technique of the Design-Expert software (Design Expert ver. 7.1.6). The software necessitates assigning goals to the processing variables and the responses. All the processing variables were kept within range while the responses were either maximized (SEI, WAI, crispness) or kept in range (density). In order to search a solution satisfying the imposed constraints, the goals are combined into an overall composite function, D(x), called the desirability function (Myers and Montgomery, 2002), which is defined as: D(x) = (d1 × d2 × . . . × dn )
1/n
(8)
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Table 3 – ANOVA and regression coefficients of the second order polynomial models of the various responses. Predictor
Intercept X1 X2 X3 X4 X5 X1 X2 X1 X3 X1 X4 X1 X5 X2 X3 X2 X4 X2 X5 X3 X4 X3 X5 X4 X5 X12 X22 X12 X42 X52 ANOVA R2 , % Model, F-value c.v., % Lack of fit
Regression coefficients (ˇ) Density
SEI
WAI
Crispness
0.375 0.050*** 0.00 N.S. 0.00 N.S. 0.017*** −0.019*** 0.018** 0.032*** 0.017** 0.026*** −0.014* −0.015** −0.045*** −0.00 N.S. 0.012* −0.014* 0.00 N.S. −0.00 N.S. 0.00 N.S. 0.00 N.S. 0.00 N.S.
4.140 0.580*** 0.184** 0.03 N.S. 0.03 N.S. −0.05 N.S. 0.176* 0.367*** 0.378*** 0.240** −0.280*** −0.166* −0.533*** −0.10 N.S. 0.06 N.S. −0.07 N.S. −0.06 N.S. −0.138* −0.05 N.S. −0.126* 0.03 N.S.
8.280 0.792*** 0.01 N.S. 0.379*** −0.411*** −0.10 N.S. 0.17 N.S. −0.11 N.S. −0.12 N.S. 0.16 N.S. 0.337** 0.279** −0.07 N.S. 0.319** 0.07 N.S. −0.19 N.S. −0.01 N.S. −0.18 N.S. −0.06 N.S. −0.42 N.S. −0.77 N.S.
38.86 4.58*** 1.17** −0.0 N.S. 0.5 N.S. −0.5 N.S. 1.38** 2.38*** 3.38*** 1.75** −2.13*** −1.63** −4.00*** −0.3 N.S. – −0.7 N.S. −0.2 N.S. −1.11** −0.2 N.S. −0.99* 0.6 N.S.
95.4 11.54*** 6.99 N.S.
94.5 9.44*** 8.89 N.S.
95.9 12.92*** 6.21 N.S.
95.3 11.242*** 6.59 N.S.
X1 , coded moisture content; X2 , coded blend ratio; X3 , coded die head temperature; X4 , coded barrel temperature; X5 , coded screw speed. N.S., not significant (p > 0.1). ∗ ∗∗ ∗∗∗
Significant at 10% (p < 0.1). Significant at 5% (p < 0.05) Significant at 1% (p < 0.01).
The numerical optimization finds a point that maximizes the desirability function. The characteristics of a goal may be altered by adjusting the weight or importance of specific variables (Design Expert Version, 2002).
3.
Results and discussion
Experiments were conducted as per CCRD, and RSM was applied to the experimental data using a commercial statistical package, Design Expert Version (2002). The estimated regression coefficients of the second order polynomial models for the various responses and their statistical validity defining values are reported in Table 3. The R2 values for all the models were more than 80%, thus the models developed had the capability of being used to navigate the design space and to predict the responses correctly. Furthermore, the F-values reflected that the all the models were significant and c.v. less than 10% establishes that the experiments were conducted with reasonable accuracy and suggesting that the models can be reproducible (Montgomery, 2001). The lack-of-fit was also insignificant for all the models.
3.1.
Density
The density of the extrudates varied between 0.234 and 0.501 kg/m3 . Density is a measure of how much expansion has occurred as a result of extrusion. The heat developed during extrusion can increase the temperature of the moisture above the boiling point so that when the extrudate exits from the die, a part of the moisture would quickly flash-off as steam
and result in an expanded structure with large alveoli and low density. On the other hand, if not enough heat is generated to flash-off enough of the moisture (either through low process temperature or high feed moisture), less expansion occurs resulting in a high bulk density product with collapsed cells which usually disintegrates on cooling. High density product is an indication of more uniform and continuous protein matrix and therefore, the extrudate is dense with parallel layers, no air pockets and is not spongy upon hydration (Filli, 2009). All the processing variables except DHT and BR had significant (p < 0.01) effect upon density. The density increased with the increase in the levels of all the processing variables except SS. The decrease in density with increase in SS may be attributed to the structural breakdown that might have accompanied the high SS. Guha et al. (1997) made similar observations in their study of extrudates from rice flour. Increasing screw speed tends to increase the shearing effect, this causes protein and starch molecules to be stretched farther apart, weakening bonds and resulting in a puffer product and consequently decreasing density (Filli, 2009). All the interactions of screw speed significantly affected density (Table 3). The interaction of SS with MC and BR were effective at p < 0.01, while with DHT and BT at p < 0.1. The most prominent interaction of SS was with BR (ˇ = −0.045). It can be observed from Fig. 1 that as the levels of SS and BR increased the density would decrease. This can be attributed to dual effect of low residence time because of the high SS and lesser millet starch available for gelatinization because of high BR. However, at low SS because of enhanced residence time the density increased. The predominance of SS over other processing variables in
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Fig. 1 – Contour plot (a) and response surface (b) depicting the behaviour of density at different SS and BR while the other processing variables are at centre point.
affecting density is represented in Table 4. The SS had greater contribution to R2 followed by MC.
3.2.
Fig. 2 – Contour plot (a) and response surface (b) depicting the behaviour of SEI at different SS and BR while the other processing variables are at centre point. Expansion of the extrudates is heavily dependent upon the moisture content of the feed formulation to the extruder (Alvarez – Martinez et al., 1988). Similar observations were made in the present study as well (Table 4).
Sectional expansion index (SEI)
The maximum and minimum values of SEI of the extrudates were 5.46 and 1.83, respectively. The MC (p < 0.01), BR (p < 0.05), DHT and BT had positive effect on SEI. Increase in SEI with MC has also been observed in the extrudates made from whole wheat meal (Singh and Smith, 1997). Positive correlation between BT and SEI has been observed by Pelembe et al. (2002), Singh and Smith (1997), and Owusu – Ansah et al. (1984). The negative effect of SS is perhaps because of the complete expansion associated with low residence time resulting in an increased SEI. All the interactions of MC and BR with other variables were found to be significantly affecting SEI (Table 3). There was a marked significant (p < 0.01) interaction between BR and SS (ˇ = −0.533), Fig. 2. The SEI exhibited highest values when extrusion was carried out at low SS and high BR while other variables were at centre point. The quadratic terms of BR and BT were also observed to be significantly affecting SEI at p < 0.1.
Table 4 – Predominant effect of processing variables upon responses. Processing variables
MC BR DHT BT SS Total R2 , %
Contributiona to R2 , % Density (kg/m3 )
SEI
WAI
Crispness
32.7 7.7 13.9 23.1 36.0 95.4
58.8 33.9 15.6 14.6 24.1 94.5
57.0 27.7 13.7 11.7 33.6 95.9
59.8 32.7 11.6 19.0 23.3 95.3
MC, moisture content; BR, blend ratio; DHT, die head temperature; BT, barrel temperature; SS, screw speed; SEI, sectional expansion index; WAI, water absorption index. a
Contribution to R2 of a given variable should be interpreted as the amount by which the total R2 would be reduced if that variable was removed from the regression equation.
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3.3.
Water absorption index (WAI)
The extrudates exhibited WAI in the range of 3.90–9.60. Positive correlation was demonstrated by the significant effects of MC (p < 0.01), BR, and DHT (p < 0.01) towards WAI. The result shows that an increase in BT was significant at p < 0.01 and the SS led to a decrease in WAI (Table 3). WAI has been reported to increase with increase in MC and BR while extruding corn (Gomez and Aguilera, 1983) and sorghum-cowpea composites (Pelembe et al., 2002), respectively. The comminution of starch molecules as a result of degradation caused by high BT may have made it imperative for the extrudates to hold less water, hence a lesser WAI. Similar observations were registered by Anderson et al. (1969), Guha et al. (1997), Singh and Smith (1997), and Mercier and Feillet (1975). The negative correlation (ˇ = −0.103) between WAI and SS can be attributed to the extended time of stay of the feed formulation inside the barrel of the extruder resulting in extensive cooking, ergo an increased WAI. Guha et al. (1997) have also made similar observations in their study of extrusion cooking with rice flour. The interactions of BT with BR and DHT, and the interaction of BR with DHT were found to significantly affect WAI at p < 0.05. The negative effect of the BT–SS (ˇ = −0.193) interaction on WAI observed in this study is in agreement with earlier findings (Guha et al., 1997; Gomez and Aguilera, 1983). It can be seen from Fig. 3 that the WAI is maximum when the SS and BT both are at values lower than their centre points. The MC of the feed formulation to the extruder and the SS of extruder were variables predominantly affecting the WAI (Table 4).
3.4.
Crispness
The crispness of the extrudates ranged from 22 to 50. There was an increase in the crispness of the extrudates with an increase in the MC (p < 0.01), BR (p < 0.05) and BT. The increase in DHT and SS was marked by a decrease in the crispness (Table 3). Crispness of the extrudates was significantly affected by all the interactions of the MC and BR with other process variables. However, the interactive effect of BR and SS was outstanding (ˇ = −4.00). Perusal of Fig. 4 shows that a low SS and high BR combination resulted in high crispness values. A decreased SS means longer residence time or more cooked starch, hence crisper extrudates. A feed formulation comprising high millet content (or low BR) resulted in crispy extrudate even at high SS; this may be because of the millet starch get-
Fig. 3 – Contour plot (a) and response surface (b) depicting the behaviour of WAI at different SS and BT while the other processing variables are at centre point. ting cooked readily under the existing extruder conditions. The quadratic terms BR and BT were significantly affecting crispness at p < 0.05 and p < 0.1, respectively. MC of the feed formulation has a crucial role to play in the generation of vapour bubbles in the feed formulation during the exit of the extrudates from the extruder-die (Kokini et al., 1992). The present study also witnessed overwhelming effect of MC on the extru-
Table 5 – Applied constraints to obtain optimized values of processing variables and the predicted responses. Variables
Condition
MC BR DHT BT SS Responses Density SEI WAI Crispness
In range In range In range In range In range In range Maximize Maximize Maximize
Lower limit 12 12 160 100 100 0.234 1.825 3.9 22
Upper limit 24 28 200 140 140 0.523 5.457 9.603 50
Importancea
Optimum valueb
3 3 3 3 3
23.2 19.9 187.0 121.1 123
3 5 5 5
0.52 5.1 9.4 45
MC, moisture content; BR, blend ratio; DHT, die head temperature; BT, barrel temperature; SS, screw speed; SEI, sectional expansion index; WAI, water absorption index. a b
The value of importance is as per the default setting of the software. The desirability for this result was 0.98.
498
food and bioproducts processing 8 9 ( 2 0 1 1 ) 492–499
Table 6 – Results of the t-test conducted to compare the predicted and the experimentally recorded values. Response
Predicted valuea
Actual value ± SD
Density SEI WAI Crispness
9.4 5.1 0.52 45
9.14 5.2 0.532 46
± ± ± ±
0.58 0.45 0.05 3.35
Standard error
% Variation
Mean difference
Significance (2 tailed)
0.262 0.202 0.023 1.50
2.84 1.92 2.26 1.75
0.26 0.1 0.012 1.0
1.00 0.494 0.521 0.667
Ho : o = 1 , tcal < ttable at p < 0.1, ‘Ho ’ was accepted. SEI, sectional expansion index; WAI, water absorption index. a
Mean of five replications.
date crispness, it is represented in terms of contribution to model R2 (Table 4).
3.5.
Optimization and model verification performance
Numerical optimization of the process variables was carried out with the help of a commercial software (Design Expert Version, 2002). The optimization was carried out under certain applied constraints. The software was used to generate optimum processing conditions and to predict the corresponding responses as well. The applied constraints and the
predicted optimum values obtained for the various responses are reported in Table 5. Extrusion cooking was carried out under the optimum processing conditions and the responses were recorded (mean of 5 measurements). The veracity of values of the responses predicted by the software was ascertained with the help of a two-tailed, one sample t-test (Table 6). The results of the t-test demonstrated no significant difference between the values of the predicted responses and the recorded response. Thus, establishing the suitability of the models to predict the various responses as desired for a particular application.
4.
Conclusions
Designed experiments using CCRD successfully exhibited the effect of processing variables (MC, BR, DHT, BT and SS) on the responses (density, SEI, WAI and crispness) of extrudates manufactured from different blends of millet and legume. The models were found to be statistically valid and demonstrated adequate information regarding the behaviour of the responses upon variation in the processing variables. Optimum processing conditions and the corresponding predicted response could be obtained with the help of the models. The predicted response at optimum conditions had nonsignificant difference from the experimental values.
References
Fig. 4 – Contour plot (a) and response surface (b) depicting the behaviour of crispness at different BR and SS while the other processing variables are at centre point.
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