Optimization of extrusion condition for an extruded snack developed from industrial by-product and moringa leaf powder

Introduction

Human body needs nutrition which involves proper intake of fats, protein and carbohydrates at balanced level for maintaining the body with appropriate amount of fiber, energy, vitamins and minerals (Bernacchia et al., 2016) [7] . Due to non-consumption and lack of nutritious food people are prone to various non communicable diseases such as diabetes, obesity, cancer etc (Ouhit et al., 2014) [36] . Food industry by-products like rice bran, broken rice, and defatted soybean meal can be valuable sources of nutrients and bioactive compounds. Subcritical water hydrolysis of rice bran and soybean meal yields protein, amino acids, and reducing sugars with antioxidant properties (Watchararuji et al., 2008) [54] . During milling, It is estimated that on milling paddy or Asian rice (Oryza sativa) around 50% whole rice produce approximately 16% broken rice produced around 20% husk 14% bran and meal produced. (Bautista & Counce, 2020) [6] . Broken rice, a by-product of rice milling, can be effectively utilized through extrusion processing to create value-added food products. Extrusion technology has been used to produce extruded product from broken rice and other non-rice materials, offering opportunities for fortification (Limberger et al., 2009) [31] . Defatted soybean flour is a valuable source of protein, amino acids, and isoflavones (Kang et al., 2017) [25] . It is found that defatted soybean meal produced from soybean mill as a byproduct contains more than 40% of the soya protein. Soya protein is very nutritious and also inexpensive and widely used in various food products such as cereal product, dairy, meat, rice, pasta and snack food (Sereewat, et [45,42,48,49,53] .

The solvent extraction process typically removes almost all oil content, resulting in defatted soybean meal with less than 1% oil (Toomer et al., 2023, Demarco & Gibon, 2020) [52,17] . Moringa oleifera, often called the "miracle tree," is renowned for its exceptional nutritional and medicinal properties. Its leaves are rich in essential nutrients, including proteins, vitamins (A, B, C, E), minerals, and antioxidants (Tamilselvi & Arumugam, 2019, Hossain et al., 2022) [51,24] . Moringa leaves can be incorporated for making extruded product as an ingredient along with defatted soybean meal and broken rice which will add fiber to the final product and also enhance its health benefit. The extrusion process parameters, such as feed proportion, feed rate, barrel temperature, screw speed are critical in determining the final product characteristics (Salgado et al., 2017; Nisha et al., 2023) [41,33] . Extrusion cooking is a process in which material is forced to flow in different and variety of conditions through a preferable shaped die at a definite rate to achieve different products. This process of food production has been practiced over fifty years (Choton et al., 2020, Ajita, 2018, Patil et al., 2007, Cortes et al., 2014, Korkerd et al., 2016) [12, 1, 38, 13, 27] . The process is cost-effective and versatile, allowing for the incorporation of by-products and the development of fortified foods (Choton et al., 2020) [12] . Extruded products typically have low moisture content and extended shelf life (Camire et al., 2007) [10] . The technology's impact on nutritional and chemical properties is attributed to high temperatures, which modify the structure of food components (Brennan et al., 2011) [9] . At the time of extrusion cooking thermal energies and shear energies were applied to raw ingredients which causes chemical nutritional and structural transformations like gelatinization of starch, denaturation of protein, oxidation of lipid, vitamins degradation, flavour formation and an increase in availability of minerals and dietary fiber (Hirth et al., 2014) [23] . Extrusion cooking is also used to partially or totally in activates various anti-nutritional compounds which limits the use of beans as the primary food source (Shimelis and Rakshit, 2005) [47] . Extrusion is particularly useful for incorporating food by-products and waste into value-added products, addressing environmental concerns and utilizing nutritionally adequate materials (Dendegh et al., 2021) [18] . The Indian snack market is worth approximately US$3 billion, with a growth rate of 15-20%. The growth of the snack market is influenced by changing lifestyles, demands, and habits of the increasing population (Dahiwale et al., 2018) [14] . The aim of this study is to investigate the optimization of extrusion conditions for the development of an innovative extruded snack using a combination of industrial byproducts and moringa leaf powder. By analyzing the effects of key extrusion parameters such as barrel temperature, screw speed, and feed rate. This research aims to identify optimal conditions that maximize the expansion ratio, porosity, water absorption index and minimize the moisture content, water activity, bulk density and true density in final product. Additionally, this study seeks to contribute to the growing body of knowledge on sustainable food processing techniques and the potential of utilizing underused agricultural and industrial by-products in value-added food products.

Materials and Methods

Raw Material and Sample Preparation

Broken rice was purchased from local Pusa market. Defatted Soybean meal was procured from soybean oil Mill (Adani groups and company) which was located at Vidisha district in Madhya Pradesh, India. The cleaned broken rice and defatted soybean meal were pulverized separately. In order to obtain homogenized flour, the pulverized powder of broken rice and defatted Soybean meal was passed through 1.45 mm screen. Then defatted soybean meal is kept under sunlight for 24 hour. Fresh Moringa leaves were collected, washed and cleaned before sun drying. Dried moringa leaves were ground using laboratory grinder and fine powder was obtained by sieving through 125 micron sieve.

Composite Flour Preparation and Conditioning

Composite flour was prepared by mixing of broken rice powder, defatted soybean meal powder and moringa leaf powder in different ratios on dry weight. Preliminary experiments were conducted to select different proportion of these flour. Conditioning of prepared blends of the flours was done by spraying a calculated amount of water and mixing by hand, the mixture was then passed through the sieve (1.45mm) and finally it was stored in zip lock polythene bag, and left it for at least 30 minutes. The moisture content of the conditioned samples ranged from 10% to 15% (w.b). After conditioning, the blended flour was then allowed to equilibrate at room temperature for 1 hour before extrusion cooking.

Extruder and extrusion condition

The BPTL lab model of co-rotating twin screw extruder (BPTL, Kolkata, India) was used for the experiment. The main drive was provided with 7.5 Hp motor (400v 3ph.). Die used for Extrusion was of 3 mm diameter. The length and diameter ratio of barrel is 11:7 whereas barrel length were 345mm and diameter was 108.6 mm. clearance between barrel and screw was 1.5 mm. the barrel is provided with two electric heater and two water cooling jacket. The temperature range for heater "1" is 90 °C-140 °C and for heater "2" at 60 °C. There was an inching switch present in the control board used for self wiping of system for its easy cleaning. At First feed containing 30% moisture should be fed, every time extruder is operated and immediately thereafter regular preconditioned feed should be fed. The moist lump gets gelatinized due to heated barrel and which will serve as lubricant between screw and barrel. There should not be any time gap between these two feed otherwise jamming will take place because there is no conditioned feed behind the initial moist lump to push it through the die hole.

Process Variables for Preparation of Extrudates

The experiment was conducted at five different screw speed (200, 250, 300, 350, 400)

Statistical Design

To study the effect of process variables and process optimization, response surface methodology is widely used. RSM (response surface methodology) has some group of statistical and mathematical techniques which were used for the development of relation between independent and dependent variables (Khuri and Cornell, 1987; Baş & Boyacı, 2007) [26,5] . Central composite rotatable design reduces the treatment up to 30 for 4 factor 5 level design. After determining coded range midpoint and centre point were obtained and levels for the decided range were found out. The interval for level was found by subtracting succeeding data from previous data shown in Table 1. The formula used for coded value is:

Total number of treatments obtained by formula described by box and Wilson, in 1951

Where, K (number of variables) = 4 Where, (I) For factorial part, number of experiment= 2 k = 16 (II) For axial part, number of experiment = 2k = 8 (III) For Central point, number of experiment= n°= 6

Total number of experiment are = 16+8+6 = 30

Physical properties 1. Determination of moisture content

The moisture content of composite flours and prepared extrudates were determined with the help of standard hot air oven method. The samples (15 g) were dried in the hot air oven at 102 °C±2 °C for 24 hours. Three replications were done for each samples. The total dry samples or the initial moisture content of flours and prepared extrudates were determined in accordance with (AOAC, 1990) [4] and moisture content was calculated using formula.

Where, Moisture content on wet basis, (percent) W m = Moisture evaporated, (g). W d = Bone dry weight, (g)

Determination of expansion ratio

To determine expansion ratio ten pieces were selected randomly from each sample. Diameter was measured by using vernier calliper and the expansion ratio was calculated by dividing the diameter of each Extruded sample with the diameter of die hole (Halek and Chang, 1992) [22] . Ten replications were taken.

Expansion ratio = Diameter of Extruded product

Diameter of die hole … … … (2.4)

Determination of bulk density

Bulk density of extrudates was determined by dividing the weight of the extrudates to the measured volume of the extrudates by using method described by (Grenus et al., 1993, Ojokoh et al., 2015) [20,34] . Three replication of each treatment was done.

Bulk density (

Determination of True density

Toluene displacement method was used to determine true density of Extruded product. Extruded product was ground and approximately 2 gm was put in a burette containing toluene of known volume. Then displaced volume was measured. Two replication of each treatment was done.

Final result was concluded by taking an average of readings taken. True density was calculated using following formula:

True density = weight of ground sample (kg) Rise in toluene level (m 3 ) … … (2.6)

Determination of Porosity

It is defined as the proportion of empty space within bulk fruits, was determined in this study. It was calculated based on the true density and bulk density values using the following relationship.

Determination of Water activity

The water activity of extrudate was determined by using water activity meter available in the department of P.F.E. laboratory. First, the sample was kept in the specimen port so as to cover full volume of port. The reading was then noted. It gives the direct reading of water activity of the sample. The reported values are the mean of three replicates

Determination of Water absorption index

Water Absorption Index was determined by using medico centrifuge (Plate-4.8) as per the method described by Carine et al., 2010 [11] . Each 1 g (dry basis) ground samples were suspended in 20 ml of distilled water in a tared 45 ml centrifuge tube and be stirred with glass rod then put in water bath for 30 min at 30 ℃ temperature then centrifuge at 3,000 rpm for 15 min. The supernatants liquid was poured into dry evaporator dishes .the weight of remaining gel was taken as WAI and expressed in the unit of g/g (Pardhi et al.,

2019) [37] . WAI would be calculating using following equation: [19,43] . For this purpose, the experiments were conducted by using 5-level factorial design. Total number of experiments was 30 with 4 independent variables (feed proportion, feed rates, screw speed, and barrel temperature) and 7 responses (expansion ratio, bulk density, true density, porosity, water activity, and water absorption index). The function was assumed to be approximated by a second-degree polynomial equation (Rendon et al., 2023) [39]

Where, Y is the predicted response (dependent variable), X i X j are the coded values of the process variables (these could be things like feed rate, temperature, screw speed, etc.), β 0 is the intercept (constant term), β i are the first-order coefficients (effects of each variable), β ii are the quadratic coefficients (effects of squared terms, representing the curvature of the response), β ij are the interaction coefficients (effects of the interaction between two variables), ϵ is the error term (accounting for random variability in the response that the model does not explain). The desirability of the experimental values was calculated by using the desirability function approach, which transforms each response variable into a desirability score (ranging from 0 to 1) based on its target (maximum, minimum, or specific value). The desirability function depends on whether the goal is to maximize, minimize, or target a specific value. The general formula for desirability (d i ) (Bhaskar & Sahoo, 2020, Datta et al., 2006) [8,15] For maximization

Where, y i is the experimental value, and s is a shape factor (typically 1 for linear).

The overall desirability (D) is the geometric mean of the individual desirability scores:

Where, d1, d2, d3, d4, d5, d6, d7 is Desirability for moisture content, expansion ratio, bulk density, true density, porosity, water activity, water absorption index

Results and Discussions

The experimental results are presented in Table 2 3. The analysis revealed that the independent variables had a highly significant effect (p<0.0001) on the dependent variables, significant effect at (p<0.05) and non significant effect at (p>0.05).

Response surface analysis of Moisture Content

The Model F-value of 133.81 implies that the model is significant (p-value < 0.0001). The value of the coefficient of determination (R² = 0.9921) obtained for the response Moisture Content (MC) adequately explains 99.21% of the total variation in the response variable. The Lack of Fit Fvalue of 1.36 implies that the lack of fit is not significant relative to the pure error. The equation 3.1 suggests that Moisture Content (MC) is most strongly influenced by Barrel Temperature (D) and Feed Rate (B), with significant effects from their interactions and quadratic terms. Increasing barrel temperature and screw speed leads to moisture loss shown in Fig. 1, while optimizing feed rate and temperature can help maintain desired moisture levels in the extruded product.

Response surface analysis of Expansion Ratio

It is evident from the The Model F-value of 376.65 implies that, it is significant (p<0.0001). The value of coefficient of determination (R 2 =0.99) obtained for the response ER, which adequately explains 99% of the total variation. The lack of fit F-value of 4.10 implies the lack of fit is not significant relative to the pure error. From equation 3.2, it was found that negative sign of coefficient value of feed proportion indicates that with the increase in feed proportion there is decreased in expansion ratio and positive sign of barrel temperature indicates that with the increase in barrel temperature there is increase in expansion ratio. As the amount of fiber and protein increase in the blend there is decrease in expansion ratio shown fig 2 . Because it may affect the starch gelatinization and rheological properties of the material present in the extruder (Leonard et al., 2015). When the ratio of starch to protein is increased, it leads to the formation of continuous starch matrix which enables the water vapour to expand. Because protein melts viscosity is higher than starch melt viscosity (Omwama et al., 2014) [35] . Moringa leaf powder is the significant factor which affects the expansion ratio. Expansion ratio is decreased significantly (p<0.05) with the increase in moringa leaf powder in the feed as indicated by negative coefficient. (Sahoo et al., 2014) [40] The second order polynomial Regression equation was developed for expressing the effect of independent variables on the bulk density (BD) in which the non-significant terms were excluded. 𝐵𝐷 = +131.08 + 2.64𝐴 + 5.40𝐵 + 2.32𝐶 -17.35𝐷 -4.11𝐴𝐷 -6.59𝐵𝐷 -8.79𝐶𝐷 -6.81𝐴 2 -3.56𝐵 2 -4.30𝐶 2 + 10.

The Model F-value of 75.17 implies that, it is significant (p<0.0001). The value of coefficient of determination (R 2 =0.98) obtained for the response BD, which adequately explains 98% of the total variation The lack of fit F-value of 0.73 implies the lack of fit is not significant relative to the pure error. Table 3 represents ANOVA for bulk density in which Screw speed and feed proportion emerge at 5% level of significance. AD, BD, CD, and A 2 , B 2 , C 2 , D 2 are interaction and quadratic significant model terms at 1% and 5% level of significance. Brennan, 2010) [16] . Positive coefficient indicates that with increase in feed rate there is increase in bulk density.

Positive sign of feed proportion indicates with increase in feed proportion bulk density increases. bulk density relationship with Barrel temperature and screw speed was showing minimal interaction with both temperature and screw speed contributing to lower bulk density at different periods similar results were found by Seker 2005 [44] , Hagenimana et al., 2006 [21] , Sharma 2012 [46] , Kumari 2020 [29] .

Response surface analysis of True Density

The Model F-value of 14.47 implies that, it is significant (p<0.0001). The value of coefficient of determination (R 2 =0.93) obtained for the response TD, which adequately explains 93% of the total variation. The lack of fit F-value of 0.0190 implies the lack of fit is not significant relative to the pure error. Table 3 represent the effect of independent variable on true density, in which highest F-value obtained at 1% level of significance. The feed rate and barrel temperature having lower F value and had the non significant effect on true density. Screw speed had higher F value which shows highly significant effect on true density. Equation 3.4 Positive sign of coefficient value of screw speed and feed rate indicates that with increase in screw speed there is increase in true density and Negative sign of coefficient value of feed proportion indicates that with increase in feed proportion there is decrease in true density. Interaction terms highlight the complexity of these relationships, suggesting that the effect of one factor can be amplified or reduced by others. Quadratic terms indicate that the effect of A, B, and C on TD are nonlinear, with higher values of these factors producing progressively larger changes in TD.

Response surface analysis of Porosity

It is evident from the Table 2 that the porosity was varied from 83. indicates that screw speed (C) and barrel temperature (D) have a positive impact on porosity, which indicates that an increase in these parameters leads to higher porosity. But feed proportion (A) and feed rate (B) had negative effect on porosity which implied that higher values of these factors reduce porosity. The presence of quadratic terms A², B², C² indicates a non-linear relationship which means increasing these factors initially enhances porosity but may reach an optimal point beyond which the effect stabilizes. However, D² has a negative coefficient which indicating that excessive barrel temperature can reduce porosity.

Response surface analysis of Water Activity

It is obvious from The second order polynomial multiple regression equation was developed for explaining the variation effect in independent variables on the water activity and the non significant terms were excluded.

𝑎 𝑤 = +0.4355 -0.0115𝐵 -0.0351𝐷 + 0.0049𝐴𝐵 -0.0099𝐴𝐶 + 0.0082𝐵𝐶 -0.0118𝐵𝐷 + 0.0060𝐶𝐷 + 0.0090𝐴 2 + 0.0144𝐵 2 + 0.0105𝐶 2 + 0.0126𝐷 2 … … … … … … (3.4)

The Model F-value of 92.27 implies that, it is significant (p<0.0001). The value of coefficient of determination (R 2 =0.98) obtained for the response Aw, which adequately explains 98% of the total variation. The lack of fit F-value of 3.77 implies the lack of fit is not significant relative to the pure error. ANOVA Table 3 shows the effect of all independent variables on water activity (a W ). Barrel temperature showed a highly significant effect on water activity (a W ), with a high F-value at the 1% significance level. Feed rate also had a significant impact at the 5% level.

The feed proportion and screw speed having lower F value had the non-significant effect on water activity (a W ). Increasing barrel temperature and feed rate will introduce more energy to the material in the barrel which would enhance the moisture evaporation at die exit and here decrease water activity.

Response surface analysis of Water Absorption Index

It is evident from the The Model F-value of 61.55 indicates that the model is highly significant (p<0.0001). The coefficient of determination (R² = 0.9829) indicates that the model explains 98.29% of the total variation in WAI which demonstrating a good fit. The lack of fit F-value of 4.31 suggests that the lack of fit is not significant relative to the pure error (p = 0.0602) which is confirming that the model is suitable for prediction. ANOVA Table 3 shows the effect of all independent variables on water absorption index. Barrel temperature showed a highly significant effect on Water Absorption Index (WAI) with a high F-value at the 1% significance level. Feed rate also had a significant impact at the 5% level. However, feed proportion and screw speed had non-significant effects on WAI, as indicated by their lower F-values. The equation (3.7) and Fig. 6 shows that feed proportion, screw speed, and barrel temperature all have positive coefficients, indicating that increasing these parameters leads to an increase in the Water Absorption Index (WAI) (Kumar, Sarkar, & Sharma, 2010) [28] . [32,2] . The decrease in WAI related to the degradation of starch that cause a reduction in the water holding capacity of the molecules as a result of decrease in the molecules size (Anderson et.al 1969; Mercier & fillet 1975) [3,32] .

Optimization and Validation of the Optimum Solution

Statistical package was used for carrying out optimization of experiments. Statease design expert version 13 was used for Central composite rotatable design. In order to obtain optimal solution, the developed model were further analyzed to determine the corresponding stationary points and to explore the nature of response surface around these points Table 4 shows the stationary points for dependent response variables. Validation of optimize condition was done to test and compare the prediction performance with experimental results. Optimum solution was selected and prepared as extrudates of their dependent variables for validation experiment. The observations recorded with respect to the optimum response dependent are summarized in Table 4.

The independent variable feed proportion(A), feed rate(B), screw speed(C) and barrel temperature(D) were optimize for minimization of moisture content(MC), bulk density(BD), true density(TD), water activity(a w ), maximization of expansion ratio(ER), porosity (P) and Water absorption index(WAI). The response variables are analyzed for their predicted and experimental values, along with their standard deviations (Std. dev) and coefficients of variation (C.V%). It was seen that experimental data for response were in close agreement with solution obtained from response surface methodology and hence the optimum condition can be recommended for extruded product at its best effectiveness. The experimental results for the response variables exhibited strong alignment with the predicted values, validating the accuracy and reliability of the optimization process. The moisture content was determined to be 5.62%, slightly lower than the predicted 5.89% was indicating improved drying efficiency. The expansion ratio was nearly identical with the experimental value of 2.99 compared to the predicted 2.98 which was confirming the expected puffing effect. Bulk density was measured at 121.88 kg/m 3 was marginally lower than the predicted value 123.78 kg/m 3 , while true density decreased to 1251.94 kg/m³ from the predicted 1269.92 kg/m³ which indicating a slightly lighter and less dense product. Porosity increased to 90.26% from the predicted 89.82% which was reflecting a more porous structure. Water activity was recorded at 0.4035 which was slightly below the predicted value 0.4096 which indicating enhanced stability against microbial growth. The water absorption index was 7.01 which is slightly lower than the predicted value 7.32, pointing a minor reduction in water retention capacity. The overall desirability of the process is 0.785 for the predicted value and 0.847 for the experimental value which reflect the process is a highly optimized. These findings indicate that the extrusion process has been successfully fine-tuned to yield a product with favorable response variable. The strong correlation between the predicted and experimental results, combined with the low variability which validates the reliability and effectiveness of the optimization process.

Conclusion

This study successfully optimized the extrusion conditions for developing novel extruded snacks from industrial byproducts (broken rice powder and defatted soybean meal) fortified with Moringa leaf powder, leveraging Response Surface Methodology (RSM) and a Central Composite Rotatable Design (CCRD). The developed second-order polynomial models demonstrated high significance (p<0.0001) and an excellent fit (R² > 0.93 for all responses). With increasing Feed rate, screw speed the moisture content of the product decreases. An increase in the Feed proportion led to increase in bulk density and Water absorption index. An increase in Feed rate led to increase in bulk density and water activity. With increase in screw speed led to increase in bulk density, True density, Porosity Water absorption index. An increase in the Feed proportion led to increase in expansion ratio, True density and Porosity. With increasing barrel temperature the moisture content and porosity of the product decreases. The optimal conditions identified are a feed proportion of 75:17.5:7.5, feed rate of 11 kg/h, screw speed of 300 rpm and barrel temperature of 145 °C for minimizing true density. These settings effectively achieved the desired outcomes for all response variables, including minimized moisture content, water activity, bulk and true densities and maximized expansion ratio, porosity and water absorption index. The experimental desirability of 0.847 was higher than the predicted desirability 0.785 which is showing that the optimized settings of feed proportion, feed rate, screw speed, and barrel temperature delivered superior results such as moisture content 5.62%, expansion ratio was 2.99, Bulk density was 121.88 kg/m 3 , true density was 1251.94 kg/m³, Porosity was 90.26, Water activity was 0.4035, water absorption index was 7.01. These findings highlight the potential of RSM and CCRD for optimizing extrusion processes to create value-added snack products from underutilized resources. This confirms the effectiveness and reliability of the optimization process in meeting the desired outcome.

Declaration of Conflicting Interest

I declared that, there is no potential conflict of interest with respect to the research, authorship, and/or publication of this article.

Acknowledgement

AcknowledgementWe would like to express our sincere gratitude to the Department of Processing and Food Engineering, Dr. Rajendra Prashad Central Agricultural University PUSA, Bihar, India for providing the laboratory facilities and necessary resources to conduct this research.

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