Development of Rice based Protein Rich Extruded Food Product of Lentil and Oat

Transcription

1 Development of Rice based Protein Rich Extruded Food Product of Lentil and Oat THESIS Submitted to the Jawaharlal Nehru Krishi Vishwa Vidyalaya, Jabalpur In partial fulfillment of the requirements for the Degree of MASTER OF TECHNOLOGY In AGRICULTURAL ENGINEERING (PROCESSING & FOOD ENGINEERING) By SWATI CYRIL Department of Post-Harvest Process and Food Engineering College of Agricultural Engineering, Jabalpur Jawaharlal Nehru Krishi Vishwa Vidyalaya, Jabalpur (M.P.) 2014

2 CERTIFICATE I This is to certify that the thesis entitled Development of Rice based Protein Rich Extruded Food Product of Lentil and Oat. submitted in partial fulfillment of the requirements for the degree of MASTER OF TECHNOLOGY in Agricultural Engineering (Processing and Food Engineering) of Jawaharlal Nehru Krishi Vishwa Vidyalaya, Jabalpur is a record of the bonafide research work carried out by Miss Swati Cyril under my guidance and supervision. The subject of the thesis has been approved by the Student s Advisory Committee and the Director of Instruction. All the assistance and helps received during the course of the investigation has been acknowledged by her. Place : Jabalpur Date : (Dr. A.K. Gupta) Chairman of the Advisory Committee THESIS APPROVED BY THE STUDENT S ADVISORY COMMITTEE Committee Name Signature Chairman Dr. A.K. Gupta Member Dr. Mohan Singh Member Dr. H.L. Sharma Member Prof. Sheela Pandey

3 CERTIFICATE II This is to certify that the thesis entitled Development of Rice based Protein Rich Extruded Food Product of Lentil and Oat submitted by Miss Swati Cyril to the Jawaharlal Nehru Krishi Vishwa Vidyalaya, Jabalpur in partial fulfillment of the requirement for the degree of MASTER OF TECHNOLOGY in Agricultural Engineering in the Department of Post Harvest Process and Food Engineering has been, after evaluation, approved by the External Examiner and by the Student s Advisory Committee after an oral examination on the same. Place: Jabalpur Date: Dr. A.K. Gupta Chairman of the Advisory Committee MEMBER OF THE ADVISORY COMMITTEE Committee Name Signature Chairman Dr. A. K. Gupta Member Dr. Mohan Singh Member Dr. H. L. Sharma Member Prof. Sheela Pandey Head of the Department (PHP&FE) Director Instruction (JNKVV Jabalpur)

4 DECLARATION AND UNDERTAKING BY THE CANDIDATE I, Swati Cyril, D/o Cyril Michael, certify that the work embodied in thesis entitled, Development of Rice based Protein Rich Extruded Food Product of Lentil and Oat is my own first hand bonafide work carried out by me under the guidance of Dr. A.K. Gupta at Department of Post Harvest Processing and Food Engineering, College of Agricultural Engineering JNKVV, Jabalpur during The matter embodied in the thesis has not been submitted for the award of any other degree / diploma. Due credit has been made to all the assistance and help. I, undertake the complete responsibility that any act of misinterpretation, mistakes, and errors of fact are entirely of my own. I, also abide myself with the decision taken by my advisor for the publication of material extracted from the thesis work and subsequent improvement, on mutually beneficial basis, provided the due credit is given, thereof. Place: Jabalpur (Swati Cyril) Date:

5 Copyright Jawaharlal Nehru Krishi Vishwa Vidyalaya, Jabalpur, Madhya Pradesh, 2014 Copyright Transfer Certificate Title of the Thesis : Development of Rice based Protein Rich Extruded Food Product of Lentil and Oat" Name of the candidate : Swati Cyril Subject : Processing and Food Engineering Department : Post Harvest Process and Food Engg College : College of Agricultural Engg., JNKVV, Jabalpur Year of thesis submission : 2014 Copyright Transfer The undersigned Swati Cyril assigns to the Jawaharlal Nehru Krishi Vishwa Vidyalaya, Jabalpur, Madhya Pradesh, all rights under Copyright Act, that may exists in and for the thesis entitled, "Development of Rice based Protein Rich Extruded Food Product of Lentil and Oat" submitted for the award of M.Tech. Processing and Food Engineering degree. Date : Place: Jabalpur Dr. A.K. Gupta (Major Advisor) Swati Cyril (Student)

6 ACKNOWLEDGEMENT Prostration and adoration to the lotus feel of almighty God and my parents for giving the opportunity to express my heartfelt gratitude, to all those who have extended help to make this study a success. First and foremost I offer my sincerest gratitude to my supervisor and Chairman of Advisory Committee Dr. A.K. Gupta, Assistant Professor, Department of Post-Harvest Process and Food Engineering, College of Agricultural Engineering, JNKVV, Jabalpur, for the unconditional support extended to me. I shall remain indebted to him for his patience, encouragement and unfailing enthusiasm. His willingness to share knowledge and resources made my research life smooth and rewarding. I would like to take the opportunity to put on record my deep sense of reverence to the esteemed members of my Advisory Committee. Dr. Mohan Singh, Associate Professor, Dr. H.L. Sharma Associate Professor and Prof. Sheela Pandey Assistant Professor Post Harvest Process and Food Engineering, College of Agricultural Engineering, JNKVV, Jabalpur. With deep sense of humility and gratefulness, I express my sincere thanks to my respected teachers Prof. Dr. B.M. Khandelwal, Head, Department Of Post Harvest Process and Food Engineering, College of Agricultural Engineering, JNKVV and Dr. Mohan Singh for their useful guidance and constructive suggestions, Prof. Sheela Pandey for providing an experienced ear for my doubts. My sincere thanks for Dr. G.S. Rajput, Dean, and Faculty of Agricultural Engineering JNKVV Jabalpur for the constant support constructive criticism and facilities provided. He was a true mentor and his contribution shall always be remembered. Beside I am very much thankful to all the professors and staff members of College of Agricultural Engineering, JNKVV, Jabalpur who directly or indirectly helped me during my study and for completing the work on time.

7 It gives me immense pleasure in expressing my humble gratitude to Mr. Suraj Patel, Librarian College of Agricultural Engineering, J.N.K.V.V, Jabalpur for providing necessary book facilities accordingly. It is my duty to thank the non- technical staff of the department Mr. Mustaq and Mr. Ramashish for their assistance and cooperation from time to time throughout the period of this work. I feel good to express my sincere thanks to Er. Mohammad Azam and all my seniors. I find myself lucky to have friends Er. Swati Mahobiya, Er. Ritesh Ranjan, Er. Varsha Kanojiya, Er. Shraddha Bhople, Er. Prabha Haldakar, Er. Satyendra Thakur who helped me throughout my work selflessly. Diction is not enough to express my gratitude from the deepest core of my soul to my respected father Mr. Cyril Michael, mother Mrs. Shobha Cyril and brother Mr. Sanjeet Cyril whose selfless love, constant encouragement, obstinate sacrifices, sincere prayers, expectations and blessings have always been the most vital source of inspiration in my life. I also give special thanks to my close friends Er. Priyanka Sharma, Miss. Deepti Sagar and Er. Sarwar Hossain. They will always be a special part of my life. Above all, I am thankful to the almighty for his grace and immense blessing always showered upon me. Place: Jabalpur Date: Swati Cyril

8 List of Contents S. No Title Page 1 Introduction Review of literature Extrusion cooking technology for foods Food extruder Effect of processing parameters of feed on extrudate Physico-chemical and sensory attributes of extruded 8 product 2.5 Physical changes during extrusion cooking Extrusion and texture Extruded product as snack food Experimental design for analysis of extrusion process 13 3 Material and methods Material Methods Experimental plan Experimental design Experimental procedure Preparation of samples Moisture content of blends Preparation of rice based extrusion cooked 20 food product of lentil and oat 3.4 Experiment and instrument used Hammer mill Digital vernier calliper Screw gauge Measuring cylinder Food extruder Components of food extruder Extrusion drive Feed assembly Extrusion screw Extrusion barrel Bore L/D Ratio Grooves or splines Jacket Extruder discharge Die head assembly Dies Cutting device 30

9 S. No. Title Page 3.6 Determination of experimental parameters Determination of physico-chemical parameters Mass Flow Rate (MFR) Specific Length (SL) Sectional Expansion Index (SEI) Bulk Density (BD) Water Absorption Index (WAI) Protein Content Determination of texture properties Determination of Sensory parameters Statistical Analysis 37 4 Result and Discussion Effect of process and operational parameters on the physico-chemical properties of rice based extrusion cooked food product of lentil and oat Mass flow rate (MFR) Specific length (SL) Sectional expansion index (SEI) Bulk density (B.D) Water absorption index (wai) Protein Effect of process and operational parameters on the textural properties of rice based extrusion cooked food product of lentil and oat Hardness Crispness Optimization of process parameters Sensory evaluation 66 5 Summary, conclusion and suggestion for future work Summary Conclusion Suggestion for future work 71 6 Bibliography Appendices

10 List of Tables S. No. Title Page 3.1 Design details of process variables Constant parameters and dependent parameters for rice based extrusion cooked food product of lentil and oat 3.3 Experimental design matrix of process parameter levels for various blend ratios of rice, lentil and oat Settings for Texture Analyzer (model TA-XT2i) Sensory scores on 9 point Hedonic scale ANOVA for mass flow rate of rice based extruded food product of lentil and oat 4.2 ANOVA for specific length of rice based extruded food product of lentil and oat 4.3 ANOVA for sectional expansion index of rice based extruded food product of lentil and oat 4.4 ANOVA for bulk density of rice based extruded food product of lentil and oat 4.5 ANOVA for water absorption index of rice based extruded food product of lentil and oat 4.6 ANOVA for protein of rice based extruded food product of lentil and oat 4.7 ANOVA for hardness of rice based extruded food product of lentil and oat 4.8 ANOVA for crispness of rice based extruded food product of lentil and oat 4.9 Optimized process parameters for highly acceptable extrudates

11 List of Plates S. No. Title Page 3.1 Extrudates having different proportion of rice, lentil and oat 3.2 Extrudates having different proportion of rice, lentil and oat 3.3 Extrudates having different proportion of rice, lentil and oat 3.4 Extrudates having different proportion of rice, lentil and oat Hammer Mill Brabender Single Screw Extruder Feed Assembly Extrusion Screw Extruder Barrel Die Head Assembly Die Cutting Device Kjeldhal Apparatus Measurement of Crispness using Texture Analyzer Measurement of Hardness using Texture Analyzer Sensory evaluation of rice based extrusion cooked food product of lentil and oat 37

12 List of Abbreviations JNKVV : Jawaharlal Nehru Krishi Vishwa Vidyalaya ANOVA : Analysis of Variance % : Percent BD : Bulk density CAE : College of Agricultural Engineering w.b. : wet basis (Moisture content) Df : Degree of freedom Engg. : Engineering eq. : Equation et al. : and others Fig. : Figure G : Gram H : Hour H 2 SO 4 : Sulphuric acid i.e. : that is KJ : Kilo joule J. : Journal kg/h : Kilogram per hour Kwh : Kilo watt hour M.C. : Moisture content Kcal : Kilo calorie Mm : Millimeter Cl : Confidence level MFR : Mass flow rate rpm : Revolution per minute s : Second S. No. : Serial number SEI : Sectional expansion index B.R : Blend ratio

13 WAI : Water absorption index 0 C : Degree Celsius Ml : Milliliter RSM : Response surface methodology SL : Specific length SS : Screw speed s.s : Sum of square SE : Standard error DHT : Die head temperature BT : Barrel temperature Sec. : Second cm 3 : Cubic centimeter CCRD : Central composite rotatable design CV : Coefficient of variance PPS : Parts per second NaOH : Sodium hydroxide N 2 K 2 SO 4 CuSO 4 HCL : Nitrogen : Potassium sulphate : Copper sulphate : Hydrochloric acid

14 INTRODUCTION Extrusion cooking can be defined as a cooking process consisting of forcing the food material through a specially designed opening after prior treatment. It aims to produce a voluminous, expanded and crispy product. In principle there is no limitation for type of raw materials which can be used for extrusion. It is popular because of reduced processing cost, higher production rate, versatility to produce wide variety of products and effluent free operation. Extrusion cooking has the potential to become one of the most promising frontier technologies suitable to prepare good quality engineered food products. An extruder cooker combines several unit operations such as mixing, cooking, kneading, shearing, cooling and forming. Various combinations of operation are possible because of a multitude of controllable variable such as feed rate, total moisture in barrel, screw speed, barrel temperature, screw profile and die configuration. The energy input comes mainly from the conversion of mechanical energy into thermal energy. Extrusion cooking is an HTST process where starches are gelatinized, proteins are denatured and extrudates are texturally and histological restructured. HTST extrusion is a thermal process which reduces the microbial contamination and eliminates undesirable flavors. A large number of foods ranging from breakfast cereals, snacks, baby foods etc. can be prepared from a variety of low cost materials like cereals and pulses. Several studies on the extrusion of cereals and pulses and other biological materials using their different blends have been conducted. Rice (Oryza Sativa L.) is primarily a high energy or high calorie food and one of the leading food crops of the world. It is the staple food crops of over half of world s population. In India, the area under rice cultivation is about million hectare with a production of million tones. (Agricultural Statistics at a glance, ). One hundred gram of rice mainly contains 80 1

15 g of carbohydrate, 7.13 g of protein, 1.3 g of dietary fiber, 0.66 g of fat and 1527 KJ (365 Kcal) energy (USDA, Nutrient Database). Grain legumes are an important source of protein, minerals and vitamins for millions of people in the world, particularly in the developing countries. Legumes contribute to solving some health-related problems like reduced risk of coronary heart disease, diabetes and obesity. Lentil (Lens Culinaris) is one of the important rabi pulses containing 60.8 g of carbohydrate, 24.2 g of protein, 3.1 g of fiber, 1.8 g of fat and 1,460 KJ (346 Kcal) of energy in 100 g (USDA, Nutrient Database). In India, it is cultivated on about 1.02 million hectare with a production of 1.27 million tones. (Agricultural Statistics at a glance, ). Oats (Avena Sativa) have a beneficial effect on cholesterol levels. They contain a fiber called beta-gluten. Numerous studies have shown that this particular fiber can lower cholesterol levels. By consuming just 3 g of oat fiber per day, cholesterol levels can be lowered by 8-23 percent. One hundred grams of oats contain 66.3g of carbohydrates, 16.9 g of protein, 10.6 g of dietry fiber, 6.9 g of fat and 1, 628 KJ (389 Kcal) energy (USDA, Nutrient Database). In India, it occupies about 28.2 million hectare of land with 5.25 million tones of production (Agricultural Statistics at a glance, ). Extrusion cooking is a popular method for the preparation of snacks and ready-to-eat breakfast cereals using starch based raw material (Harper, 1981). Rice starch provides the structure, texture and all the features for production of highly acceptable extruded snacks food, which are low in nutritional value. By blending the lentil flour with rice and oat flour will develop a healthy food and fulfill the protein needs of poor population. In view of these facts there is a possibility of using lentil flour in combination with rice and oat flour to produce extruded snack having higher amount of protein and also rich in fiber content. Extruding this blend will further improve the texture, sensory attributes and digestibility of the product. 2

16 Keeping the above mentioned facts in consideration, the present study is undertaken with the following specific objectives. 1. To develop a ready to eat extrusion cooked food product using different combination of rice, lentil and oat. 2. To optimize the feed (blend ratio and m.c. of blend) and machine parameter (barrel temperature, die temperature, screw speed) for extrusion cooking process. 3. To study the physico-chemical, textural and sensory characteristic of the developed extruded food product. 3

17 REVIEW OF LITERATURE Extrusion of food is an emerging technology for food industries to process and market a large number of products of varying size, shape, texture and taste. A large number of foods ranging from breakfast cereals, snacks, baby foods etc. can be prepared from a variety of low cost materials like cereals, pulses, dry fruits and medicinal plant products. Several studies on the extrusion of cereals and pulses by using various proportions of cereals and pulses have been conducted. However, very few references in the literature are available relating to the use of lentil flour with rice and oat for making of an extruded food product. Available literature on various aspect of extrusion cooking of food is presented in the following sections. 2.1 Extrusion cooking technology for foods Ficarella et al., (2003) stated that extrusion cooking of cereals involves a wide range of food products e.g. snack, baby foods, cereals for breakfast and pasta. Extrusion of cereals is a complex process, because it involves chemical and physical transformation, molecular modifications (gelatinization of starch and cooking of material) and structural properties alternation (hardness, elasticity, color). An investigation on the extrusion cooking process for cereals in a co-rotating twin-screw extruder was carried out, using a finiteelement fluid dynamics model. Several numerical tests were carried out with varying temperature, screw rotation velocity, mass flow rate and extruder geometry. Fluid dynamic parameters inside the extruder such as shear rate and residence time were analyzed. Fundamentals regarding the evolution of fluid-dynamic system and the quality of the final product were showed. Hardacre et al., (2006) produced crisp expanded wafers from extruded pelf containing specialty starches and flours derived from peas, lentils, corn and blends of lentils and corn and found that the most acceptable products were produced from corn, lentils and blends of these two ingredients. The weight, density, stiffness and hardness of the wafers increased with the proportion of lentil flour. Sensory analysis suggested that consumer 4

18 acceptability was optimal for blends containing between 40 and 80% of lentil flour. The glycemic potential measured by in vitro digestion of the wafers was lower for the 100% lentil wafers compared with the 100% corn wafers because of the amount of starch in the grain. For combinations of corn and lentils, the carbohydrate digestion rate and hence the glycemic potential was lower than for either 100% corn or 100% lentil wafers. Bouvier et al., (2008) stated that the flexibility and the versatility of the extrusion cooking process has unquestionably opened minds of food processing industry and contributed decisively to the development of extrusion technology. Several studies on the extrusion of cereals, pulses and other biological material using their different proportion have been conducted. Berrious et al., (2010) stated that extrusion cooking technology is commercially used in the production of a various types of a snack and ready to eat foods made from cereal grains with the exception of soybean, pulses such as lentil, dry pea and chick pea have not been used for the development of extruded food products. Formulated pulse flours demonstrated a beneficial increase in dietary fiber. Research work also indicated that value added nutritious snacks with higher content of dietary fiber can be fabricated successfully by extrusion processing of formulations based on lentil, dry pea, chickpea and represent good alternative to traditional cereal-based snack. 2.2 Food extruder George and Yeh (2003) found the effect of three types of screw elements, forward mixing disc and pin- mixing element on residence distribution and starch gelatinization of glutinous rice flour in a single- screw extruder with different die opening areas. Both mixing and pin- mixing elements, yielded longer residence time, higher SME and higher extrudate temperature compared to those for the forward screw element. The degree of starch gelatinization in extrudates was a function of the residence time. The variation in die diameter did not significantly affect the mean residence time when the forward element was used. The mixing disc was found to raise the die pressure. 5

19 Altan et al., (2009) evaluated the potential use of barley grits and barley flour for expanded snacks food by using a twin screw extruder. The 2 2 factorial design had two levels of raw material (barley grits and barley flour) and two levels of screw configuration severity. The experiment responses were the following extrudate properties: specific mechanical energy (SME), sectional expansion index (SEI), bulk density, water absorption index (WAI), hardness, breaking strength and color. Means of the response were significantly different for all the response except bulk density and WAI with respect to screw configuration. For raw material, the means of the response were significantly different for all response except SEI and WAI. Barley flour extrudates produced by severe screw configuration had significantly lower SME than barley grits extrudates. Severe screw configuration produced more expanded product with low bulk density than that of medium screw configuration. Correlations were found between product responses. 2.3 Effect of processing parameter of feed on extrudate Karacan et al., (2001) produced a puffed, expanded, snack-type food from corn flour and defatted oat flour blends (20:80) by using Brabender counter- rotating twin- screw extruder under various processing condition. The effect of extrusion condition (feed moisture content, barrel temperature and screw speed) on the functional (water absorption index and degree of gelatinization) and physical (expansion ratio and cutting strength) properties of the extrudates were investigated by means of response surface methodology (RSM) by using three level, three variable central composite design. The second order model indicated that optimum product could be obtained by maximization of starch gelatinization and expansion ratio and minimization of cutting strength which corresponded to a feed moisture content of 15%, barrel temperature of C and screw speed of 95 rpm. Singh (2005) studied the extrusion characteristics of soy-rice blend for the preparation of extruded snacks. Five levels of moisture content (19, 20, 22, 14, 16%, wb) and three levels of barrel temperature (180,190, 200 C) are used. A remarkable effect of feed moisture content on the physical properties, textural characteristics and protein content of the extrudate was observed. 6

20 The best quality products were obtained at 19% feed moisture content, 190 C barrel temperature and 8% blend ratio of soy-rice followed by 20% moisture content, C temperature and 12% blend ratio. Seth and Mandhyan (2008) conducted experiments for the preparation of extruded snack from various blends of rice, defatted soy flour and ashwagandha (winter cherry) powder. Moisture content of feed were 9, 12, 15, 18 and 21% (w.b.), blend ratio i.e. rice flour, defatted soy flour and ashwagandha powder blends (70:25:5, 70:20:10, 70:15:15, 70:10:20 and 70:5:25,) and operational parameters of extruder i.e. die head temperature (160, 170, 180, 190 and C), screw speed (80, 90, 100, 110 and 120 rpm), barrel temperature (120, 130, 140, 150 and C) and analyzed for effect on physical properties (i.e. mass flow rate, moisture content of extrudates, specific length, sectional expansion index, water absorption index and bulk density of extrudates), biochemical properties (i.e. protein content and withanolide and withanolide B content), textural properties (i.e. crispness, hardness and cutting strength) of extrudates prepared out of rice flour, defatted soy flour and ashwagandha powder blends. The best quality products were obtained at 15-18% feed moisture content, 70:10:20 and 70:20:10 blend ratio, C barrel temperature, C die head temperature and 110 rpm screw speed. Meng et al., (2010) used Response Surface Methodology (RSM) to study the effects of feed moisture content (16 18%, wb), screw speed ( rpm) and barrel temperature ( C) on extruder system parameters (product temperature, die pressure, motor torque, specific mechanical energy, SME) and physical properties (expansion, bulk density, hardness) of a chickpea flour-based snack. Second-order polynomials were used to model the extruder responses and product properties as a function of process variables. Product temperature and die pressure were affected by all three process variables, while motor torque and SME were only influenced by screw speed and barrel temperature. All three variables affected product responses significantly. Desirable products, characterized by high expansion ratio and low bulk density and hardness were obtained at low feed moisture, high screw speed and medium to high barrel temperature. It was also demonstrated that chickpeas can be used to produce nutritious snacks with desirable expansion and texture properties. 7

21 2.4 Physico-Chemical and Sensory attributes of extruded product Liu et al., (2000) studied the effect of processing variables on the physical properties of extrudates. Increasing the percentage of oat flour in the feed resulted in extrudates with a lower specific length, higher bulk density, higher hardness and chewiness. Higher moisture content reduced expansion for the 100% oat flour puffs. Screw speed had no significant effect on the bulk density, specific length and expansion ratio. The decreasing moisture content and increasing screw speed resulted in increased product temperature which was highly correlated with attributes of a more expanded product such as lightness, crispness, shininess and an open cell structure. High correlation between physical and sensory attributes was observed. Chang et al., (2003) determined the physical properties of extruded combination of dietary fiber supplements and legume flours extrudates. Seeds of two legume cultivars, Yellow Split Pea (YSP), Red Chief Lentil (RCL) and apple pomace in wheat bran fiber were processed using a twin-screw extruder. The properties of extrudates containing apple pomace fiber and wheat bran fiber were studied at different concentration levels (5%, 10%, 15% and 20%) by determining expansion index (EI), relative diameter difference (RD), density, color and texture profiles of the corresponding extrudates and concluded that fiber may reduce the quality of legume extrudates slightly, but have potential as a nutritional supplement in expanded food based on legume flours. Jha and Prasad (2003) developed ready to eat extruded food product from the blends of rice and mung flours (70:30) using a single screw laboratory extruder. It was observed that increase in die temperature, feed moisture content and screw speed resulted in increase in expansion ratio, whereas bulk density and hardness of extruded products decreased with increase in die temperature. Sensory evaluation of the products was conducted and product extruded at C die temperature, 12.6 (%, wb) feed moisture and 170 rpm screw speed was found to be most acceptable. 8

22 Berrios et al., (2004) studied some physico-chemical and nutritional characteristics of lentils, dry peas, and garbanzo flours processed under specific extrusion conditions i.e % (wb) moisture content, 500 rpm screw speed, 160 and 180 C barrel temperatures by using an 18 mm twinscrew extruder and observed that the expansion ratio, torque and die pressure values of the different legume extrudates were directly related to barrel temperature and inversely related to moisture content. It was also concluded that extruded flours at 20 per cent moisture showed higher hot viscosity and set back than extruded flours at 24 and 28% (wb) moisture. However, these differences were found lower as the extrusion temperature was increased from 160 to 180 C and proximate compositions were not found significantly affected by extrusion cooking but there was a significant improvement in the protein digestibility of the legume extrudates. Ding et al., (2006) reported the effect of extrusion conditions, including feed rate (20-32% capacity), feed moisture content 14-22% (wb), screw speed ( rpm) and barrel temperature ( C) on the physico-chemical properties (density, expansion, water absorption index and water solubility index) and sensory characteristics (hardness and crispness) of an expanded wheat based snack. Increasing feed rate results in extrudates with a higher expansion, lower WSI and higher hardness. Increasing feed moisture content results in extrudates with a higher density, lower expansion, higher WAI, lower WSI, higher hardness and lower crispness. Higher barrel temperature increased the extrudate expansion but reduced density, increased the WSI and crispness of extrudate. Screw speed had no significant effect on the physico-chemical and sensory characteristics of the extrudates. Lobato et al., (2011) explained extrusion cooking as a food processing technique that is used worldwide to transform various ingredients. The develop a functional puffed ingredient with defatted soy flour and oat bran and the minimum amount of corn starch the properties of the feed ingredients and the processing condition (extruder temperature, moisture and insulin were optimized). Applying mixture experimental design to study the effect of feed ingredients on expansion and to formula containing 250 g/kg corn starch, 375 g/kg soy flour and 375 g/kg oat bran was selected as the best between incomplete factorial design, the best process condition 250 g/kg moisture, 45 g/kg insulin and C ) were optimized. 9

23 Kasprzak et al., (2013) conducted a study on the application of highfiber components (everlasting pea whole meal, oat bran) for the modification of microstructure and physical properties of corn extrudates. The extrusion was conducted using a single screw extruder type S-45 (Metalchem, Poland). In the study the effect of the material blend composition and of the variable process parameters i.e. material blend moisture (11, 13.5, 16%, wb), barrel temperature distribution profile (120/145/115, 130/155/115, 140/165/115 0 C) on the microstructure and the physical properties of the extrudates was analyzed. All extrudates obtained were characterized by typical cellular structure and crunchy texture characteristic of the ready to eat type products. The microstructure of the products obtained was determined both by the material composition of the blend and by the process parameters. The differences observed in the size, number of shapes of air cells and in the cell wall thickness indicate extensive possibilities of modification of the physical properties and sensory evaluation of extrudates. 2.5 Physical changes during extrusion cooking Ziobro et al., (2000) investigated the influence of extrusion on the physico-chemical properties of starch extrudates derived from different biological sources (wheat rye, oat, corn and potato starches). Starches were process in a single screw-extruder and reported that extrudates varied mainly in terms of hardness, density, solubility and water-binding capacity. Conversely molecular weight and pasting properties of the extruded starches were similar and therefore, showed little dependence on the botanical origin of raw material. Gonzalez et al., (2006) analyzed the effect of several factors, like screw rpm, die restriction (l/r) and moisture together with corn endosperm hardness and rice amylase content, on melt viscosity, using a Brabender single screw extruder. Relationships among different responses such as mass output die pressure, specific mechanical energy consumption, melt viscosity and degree of cooking and all responses were found to be dependent on both extrusion conditions and material structural characteristics. It was concluded that as screw speed increased, structural damage (degree of cooking) also increased, which in turn produced a reduction in pressure. 10

24 Hagenimana et al., (2006) used screw extruder to extrude flour from long-grain and high-amylase, milled rice. Response surface methodology (RSM) was used to evaluate the effects of operating variables, namely the screw speed ( rpm), barrel temperature ( C) and feed moisture content (16-22%, wb) on some functional, physical, pasting and digestibility characteristics of the extrudates. Regression analyses showed that water absorption index (WAI) was significant (P < 0.05) and viscosity values of extruded rice flours were far less than those of their corresponding unprocessed rice flour, indicating that the starches had been partially pregelatinized by extrusion process. Senol et al., (2006) investigated the effect of screw speed ( rpm) and feed rate ( kg/h, db) on the firmness, expansion ratio, color and sensory properties of a nutritionally balanced gluten free extruded snack. Regression equations-describing the effect of each variable on the responses were obtained. Results indicated that the feed rate and screw speed both had an effect on the firmness of the product at 95% confidence interval. The effect of screw speed was significant whereas the quadratic effect of feed rate was found significant on the lateral expansion (96% CI). Lateral expansion increased with screw speed. The results also indicated that changes in the extrusion variable did not affect the flavor and overall acceptability of the final product at 95% CI for the final product at 95% CI for the feed rate and screw speed ranges studied. 2.6 Extrusion and texture Apruzzese et al., (2000) used a fiber-optic equipped visible NIR spectrometer to monitor both color and composition during the extrusion of yellow corn flour. Screw speeds of 40, 50 and 60 rpm and temperature of 130, 150 and C were used in 3x3 factorial designs. The effect of screw speed and temperature and their interaction were found to be highly significant to the color co-ordinates obtained in the lab from the reflectance spectra of the reacting corn flour. The NIR spectra were also greatly affected. 11

25 Cheng et at., (2003) investigated the effect of added fiber on microstructure and textural characteristics of dry pea extrudates and used SEM to evaluate extrudates microstructure. The stress-strain curve was measured with a Texture Analyzer which interpreted the jaggedness of the curve and characterized the crunchiness of the extrudate. Dry pea flours combined with two different fiber sources, apple pomace fiber (APF) and wheat bran (WB) at concentrations of 5%, 10%, 15% or 20% were extruded at a die temperature of C and 15% moisture w.b. and found that fiber addition decreased cell size but did not significantly change cell wall thickness also concluded that microstructure and textural attributes of food extrudates are important that can be used in development of extruded snack foods. Andriana and Krokida (2010) investigated structural and textural properties of extruded corn and corn lentil mixtures as a result of process conditions, including extrusion temperature ( C), feed rate ( kg/h) and feed moisture content (13 19%, wb). Lentil was used in mixtures with corn flour at a ratio of 10 50% (legume/corn). Apparent density increased with feed rate, moisture content and lentil flour and decreased with temperature. Expansion ratio increased with temperature and decreased with feed rate, moisture content and lentil to corn ratio. 2.7 Extruded product as snack food Tang et al., (1998) used flours of garbanzo beans, lentils, whole peas and split peas to develop healthy and convenient expanded snack food. The flours were mixed with 20% high amylase starch and 0.4% sodium bicarbonate and processed on twin-screw extruder at moisture content 15% wb and die temperature 160 C and reported that expansion ratio was 1.70 for garbanzo beans followed by 9.77 for Lentils, for whole peas and for split peas. Also, the addition of high amylase starch to the legumes flours increased the expansion ratio conversely. Banerjee et al., (2003) developed lentil based extrusion cooked expanded snacks to study the effect of temperature ( C) and moisture content (12-18%, wb) on different properties of extrudates by using RSM. It was concluded that the temperature had a significant effect and peak shear stress and expansion ratio whereas variation in temperature and moisture 12

26 content had significant effect on bulk density of extrudates. Emulsion activity index of exuded protein decreased as compare to raw protein within the experimental range feed moisture content had a significantly decreasing effect on emulsion activity index, Langmuir film balance. Study also showed protein were surface active, but extent of lowering of surface tension in case of extruded proteins were less compared to unextruded protein. Berrios et al., (2005) studied on the effect of external flavor coating to legume extrudates on its acceptability among the consumers. They used Clextral twin screw extruder (patent pending) for producing cylindrical snacks and spherical breakfast type products from lentil flour blended with starch and fiber, and suggested that legume extrudates both in the form of snacks or breakfast-type products showed improvement in flavor and overall acceptability due to external coating of flavors. However, the type of flavors did not have significant effect on acceptability of snacks by consumers. 2.8 Experimental design for analysis of extrusion process Cocharan and Cox (1957) Response surface methodology (RSM) is used to reduce the number of experimental runs without affecting the accuracy of result and determine interactive effect of variables on the response. The RSM is a combination of mathematical and statistical techniques that are useful for modeling and analysis of problem, in which response of interest is influenced by several variables and objective is to optimize the response. The main advantage of RSM is that it requires less number of experimental runs to provide sufficient information for statistically acceptable result. It is a faster and less expensive method compared to the full factorial experimental approach. Haper (1981) observed that response surface sometimes also called contour plots can be made in two or three dimensions. The model contains more than two independent variable such plot can still be made while all other variable such plot can still be made while all other variables are fixed at preset variables and these can be overlaid to give a three dimensional representation of the variables. Using response surface it is possible to develop a clear mental picture of the important process parameters, the process itself and product characteristics. 13

27 Ficarella et al.,(2006) carried out an analysis of variance (ANOVA) on the extrusion cooking process for cereals in a co-rotating twin- screw extruder, using a finite element fluid dynamic simulation model, to study shear rate, residence time and mixing index inside the extruder, varying temperature, screw rotation velocity, flow rate and extruder geometry. The numerical simulation have revealed numerous aspects that can be used to improve the extrusion process: the flow temperature can be varied without modifying the gelatinization of materials that is mainly influenced by screw rotation velocity and screw axis ratio. Singh et al., (2006) used response surface methodology to study the properties of three different moisture levels and three levels of barrel temperature with five levels of blending ratio of Bengal gram brokenssorghum blends. After optimization of physical properties of extrudates the optimum level of independent parameters for maximum value of specific length, SEI and minimum values of mass flow rate; the best quality extruded snacks were found at C barrel temperature, 15:85 blends and 15% moisture content. 14

28 MATERIAL AND METHODS Present chapter describes the detail of materials used and methodology employed for the development of Rice based Extrusion Cooked Food Product of Lentil and Oat. All the experiments were conducted at Advance Food Engineering Laboratory of the Department of Post Harvest Process and Food Engineering, College of Agricultural Engineering, Jawaharlal Nehru Krishi Vishwa vidyalaya, Jabalpur (M.P.). 3.1 Material Rice, lentil and oat grains were procured from the local market for the present study. All the chemicals and packaging material required for the present study were available in the department laboratory. 3.2 Methods Experimental Plan Present study was conducted to study the effect of processing and operational parameters on the physico-chemical, textural and sensory properties of the rice based extrusion cooked food product of lentil and oat. Present study was conducted to study the effect of processing parameters of feed i.e. m.c. of feed (8, 10, 12, 14 and 16%, wb), blend ratio (70:25:5, 70:20:10, 70:15:15, 70:10:20 and 70:5:25) and operational parameters of extruder i.e. barrel temperature (130, 140, 150, 160, C), screw speed (60, 70, 80, 90 and 100 rpm) and die head temperature (180, 190, 200, 210 and C) on the physico-chemical, textural properties and sensory properties of rice based extrusion cooked food product of lentil and oat. The responses were selected to optimize ready to eat snack of acceptable quality. The details of levels of process variables with coded and uncoded values are presented in Table 3.1. The constant parameters and dependent variables of the experiment are presented in the Table

29 Table 3.1 Design details of process variables Variables Parameters Code Levels Blend ratio (Rice : Lentil : Oat) 70:25:5 70:20:10 70:15:15 70:10:20 70:5:25 Moisture Content (%, wb) Screw Speed (rpm) Barrel temperature ( 0 C) Die Head Temperature ( 0 C) Table 3.2 Constant parameters and Dependent parameters for rice based extrusion cooked food product of Lentil and Oat S. No. Constant Parameters Value Dependent Variables 1. Feed screw speed rate (rpm) 20 Physico-chemical parameters 1. Mass flow rate (g/sec) 2. Barrel temperature ( 0 C) zone 1 3. Barrel temperature ( 0 C) zone Specific length (mm/g) 3. Sectional expansion index 4. Bulk density (g/cm 3 ) 5. Water absorption index 6. Protein content (%) 4. Length-to-diameter (L/D) ratio 20:1 Texture analysis 1. Hardness (kg) 5. Compression ratio 2: 1 6. Diameter of die (mm) 5 2. Crispness Sensory evaluation 1. Color 2. Texture 3. Flavor 4. Taste 5. Overall acceptability 16

30 3.2.2 Experimental Design Response Surface Methodology (RSM) was used to reduce the number of experimental runs without affecting the accuracy of results and to determine interactive effect of variables on the response (Cochran and Cox 1957). Response Surface Methodology is a combination of mathematical and statistical technique that are useful for modeling and analysis of problems. The main advantage of Response Surface Methodology is that it requires less number of experimental runs to provide sufficient information for statistically acceptable results. It is a faster and less expensive method compared to the full factorial experimental approach. In the design of experiment several factors as in case of factorial design and all the factors represent quantitative variables like die head Temperature ( 0 C), barrel Temperature ( 0 C), moisture content (%), blend ratio (Rice: Lentil: Oat) etc. Then responses of these factors will be the function of the level of variable. It can be written as: Yu =Ø (X 1U1 X 2U1..., X KU ) + e u...(3.1) Where; u = 1, 2, 3...N represents the N observations in factorial experiment and X 1U represents the level of i th factor in the U th observation. The function Ø is called response surface. The residual e u measures the experimental error of the U th observation.in the response surface plotting the values of the dependent variables are represented as a surface and the values of the dependent variable for any two values of independent variables can be determined from the plot. The graph is drawn on the basis of mathematical relationship. Y = β0+ β1x1+ β2x2+ β3x3+ β4x4+ β11x12+ β22x22+ β33x32+ β44x42+ β12x1x2 + β13x1x3+ β14x1x4++ β23x2x3+ β24x2x4+ β34x3x4... (3.2) Where Y = dependent variables; and X1, X2, X3, X4 and X5 are independent variables. 17

31 In this study central composite rotatable design (CCRD) with half replicate of 5 independent variables with the five levels of each was chosen and the design matrix is presented in Table 3.3. Table 3.3 Experimental design matrix of process parameter levels for various blend ratios of Rice, Lentil and Oat Run CODED VALUES UNCODED VALUES MCE BR SS BT DHT MCE BR SS BT DHT :20: :10: :15: :10: :15: :15: :5: :10: :15: :20: :15: :10: :20: :15: :15: :20: :10: :20: :15: :10: :20: :15: :20: :25: :20: :15: :15: :10: :15: :10: :15: :15:

32 The central composite rotatable design can be fitted into a sequential programme starts with an exploratory 2k factorial to which a linear response surface is fitted (Cochran and Cox, 1957). Based on the information available in the literature and preliminary trial five independent variables were selected to prepare extruded product. The details of levels of variable with experimental plan are given in Table 3.3. The experimental plan consisted of 32 treatment combinations of each independent variable chosen. The data obtained from the experiment outlined were processed in Design Expert The adequacy of model was tested using F ratio and coefficient of regression R 2 the model was considered when the calculated F ratio was more than that of table value.the effect of variables at linear, quadratic and interactive level on the response was described using significance at 1, 5 and 10 percent level of confidence. 3.3 Experimental Procedure Preparation of samples Grains of Lentil, rice and oat were procured from local market and flour was obtained by grinding them in a hammer mill. Samples of different compositions were prepared by mixing the calculated amount of flours. The moisture contents of different blends were then measured by hot air oven method. Samples of various proportions of the rice, lentil and oat flours of desired moisture content (8, 10, 12, 14 and 16%, wb) were prepared by adding or removing the calculated amount of water. Tempering of samples was done by keeping them in aluminum foil packets for 24 h at room temperature to get uniform moisture throughout the blends Moisture content of blends Moisture content of blends is an independent parameter. In order to arrive at the predetermine m.c. of blends, the amount of the water present in all the components of blends was determined separately based upon their already existing m.c. and the amount of water present in the sample was worked out. Now in order to arrive at the required moisture level it was calculated to find out the amount of water that should be added or removed to the sample 19

33 to bring the pre-determined moisture content of the blended samples, then based upon the difference between the amount of water required for predetermined moisture level and the actual amount of water already present in the sample the different amount was added to the sample and the sample was left for 24 h conditioning so that the moisture content of entire sample becomes uniform and arrives at the required moisture level. Moisture content (%, w.b.) = Weight of water in product Weight of product sample X 100 (3.3) Preparation of rice based extrusion cooked food product of lentil and oat Before starting the actual experiment the extruder was started and the first set of operational condition was established. Then it was operated with pure rice flour to ensure proper functioning of the extruder. The conditioned samples were then fed to the feeder unit of Brabender single screw extruder under set operational conditions. As these samples came out of the extruder through die it suddenly expanded due to sudden release of moisture and pressure. To obtain a desired length of extrudate, a manual cutter was used at the end of die. Whenever operational parameter of the machine were to be changed then during this switch over period a trial sample of the pure rice was fed to the extruder to keep the machine in running condition. Samples collected were stored in property labeled thick aluminum foil packets. Later the samples (Plate 3.1, 3.2, 3.3, 3.4) were analyzed according to the requirement. 20

34 Rice, Lentil and Oat grains Grinding to obtain flour Preparation of samples of various blend ratio Moisture adjustment of samples to 8, 10, 12, 14 &16 (%, wb) Tempering (for 24 hrs) Extrusion cooking Extruded product Physico- chemical analysis Texture Analysis Sensory evaluation Fig. 3.1 Flow chart for the preparation of rice based extrusion cooked food product of lentil and Oat 21

35 Plate 3.1 Extrudates having different proportion of rice, lentil and oat Plate 3.2 Extrudates having different proportion of rice, lentil and oat 22

36 Plate 3.3 Extrudates having different proportion of rice, lentil and oat Plate 3.4 Extrudates having different proportion of rice, lentil and oat 23

37 3.4 Equipment and Instruments Used Hammer Mill Milling process refers to the reduction of size of any particulate matter into various finer particles like flour and splitted product. Hammer mill was used to obtain the required quantity of rice, lentil and oat flours for the preparation of samples. (Plate 3.5) Digital Vernier Calliper: Length of the extrudates prepared from different blends of rice lentil and oat flours was measured with the help of a digital Vernier calliper. The least count of digital vernier calliper was 0.1 mm Screw Gauge: The average diameter of the extrudate prepared from different blends of rice, lentil and oat flours was measured by screw gauge to calculate the sectional expansion index. Least count of the screw gauge was 0.01 mm Measuring Cylinder For measuring the bulk density and water absorption index measuring cylinder of capacity 100 ml were used. 3.5 Food Extruder Brabender single screw laboratory model extruder (D47055 DUISBURG) was used to prepare rice based extrusion cooked food product of lentil and oat. The extruder consisted of grooved barrel with covered heating elements and cooling jackets. The constructional elements of the extruder are motor and gear unit, coupling, loading unit, extruder barrel with screw and control cabinet (Plate 3.6). The extruder screw has three zones and one die zone. The temperatures of all zones are controlled by a temperature controller. The maximum temperature of each zone is limited to 450 C. The feeding zone of the extruder is water-cooled while the compression and metering zones are air-cooled. A round die head was fixed at the end of the barrel. The feed screw-feeding device was mounted above the feed opening. Electronic equipment and sensors are available for measuring melt pressure and melt temperature within the extruder and die head. 24

38 Plate 3.5 Hammer Mill Control Cabinet Plate 3.6 Brabender Single Screw Extruder 25

39 The Brabender extruder can either be operated from a computer (CAN BUS) or manually through the control elements of the instrument. The extruder is operated from a computer with the help of WinExt extruder software. Speed of screw was controlled through computer. Nominal speed of the drive unit is to be preset. By means of the speed potentiometer the nominal speed can be set manually. The temperature control cabinet fixed below the mounted extruder regulates the temperature. Operating switches and display of the temperature control system are situated on the front side of the instrument. The temperature of the individual zones of the extruder is recorded by separate control thermocouples and can be displayed and set by an electronic temperature controller. The controller has six displays for the temperature control zones and two melt temperature displays with digital display of actual and set temperatures Components of Food Extruder Extrusion drive The drive mechanism used to operate the extruder consisted of a stand, drive motor, transmission system and thrust bearing. A 1.5 kw inverter electric power driven DC motor is used to drive the extruder Feed Assembly The feed hopper was mounted above the feed opening to hold food ingredients. Feeder bin vibrators have been used to aid movement of ingredients into feeder and prevent bridging. The vertical feed screw was provided for uniform delivery of food ingredients Brabender extruder has variable speed auger as feeder (Plate 3.7) Extrusion screw As the only moving part feed-screw moves the feed through the barrel chamber in a steady and predictable manner. Plate 3.8 shows the feed screws with three defined sections feed zone, compression zone, metering zone. 26

40 Plate 3.7 Feed Assembly Plate 3.8 Extrusion Screw 27

41 Feed zone takes feed from the hopper and conveys it along. During the journey, food particle encounters friction from feed screw surfaces, barrel surfaces and with each other. This mechanical friction generates about 85% of the required heat. The drive mechanism should have the sufficient capacity to overcome friction and turn the feed screw at a steady and controlled rate. Compression zone, also known as transition section, lies in between feed section and metering section. Compression is achieved by the gradual decrease in flight depth in the direction of discharge. Food ingredients are normally heated and worked into continuous dough mass during passage through the transition section. The character of the feed materials changes from a granular or particulate state to amorphous or plasticized dough. It is the longest portion of the screw. Metering zone is the portion of the screw nearest to the discharge section and is characterized by having very small flight. The viscous dissipation of mechanical energy is large in this section resulting in rapid increases in temperature. The high shear rate also enhances internal mixing to produce temperature uniformity in the extrudate Extruder Barrel It is the cylindrical member, which fits tightly around the rotating extruder screw. To prevent abrasion, the barrel was made of high quality nitride steel (Plate 3.9) Bore The internal diameter of the barrel is the bore and represents the nominal size of the extruder. The size of bore determines the capacity of the extruder Length to diameter ratio (L/D Ratio) L/D ratio is the distance between the rears edges of the feed opening to the discharge end of the barrel divided by the bore diameter to express a ratio where diameter is reduced to one. 28

42 Plate 3.9 Extruder Barrel Grooves or splines Brabender has provided non-removable sleeve within the interior surfaces of the barrel of the food extruder often, have small and straight grooves to prevent slippage of food material at the walls. The presence of grooves increases the ability of the extruder to pump food material against high back pressures. In the Brabender Food extruder straight grooves along the length of barrel were provided Jacket Hollow cavities around the outside of the barrel in which a heat transfer medium can be circulated such as water, steam or air. The feed zone of the extruder is water cooled and rest zones are air cooled Extruder Discharge Once the food material leaves the extrusion screw, it enters the discharge of the extruder which is called die head assembly. 29

43 Die head assembly Die head assembly is the holder for the die and serves as the support for the cutter. The die head assembly (Plate 3.10) is covered by the heating element and provides the hollow cavity to circulate cold air stream. The pressure transducer and electronic temperature sensor are connected to die head assembly to display pressure and temperature in this section Dies The opening that allows feed material to form particular shapes is also a highly engineered part (Plate 3.11). The dies are available in different shapes, the simplest being a hole. Often the opening on the feed side of the die is streamlined to improved uniformity of the extrudate Cutting device The extrudates coming out of the die is required to be cut into the required lengths by a series of knives that rotate across the face of the die. The cutting device (Plate 3.12) is mounted directly to the round strand die head of the extruder. Cutting is done directly in front of the round strand die by means of a rotating cutting knife, driven by a servo-motor with electronic speed control unit. In the single screw extruder considered for the study there was a provision for fixing a cutting device, which can be rotated at different speeds to yield extrudates of different length. Plate 3.10 Die Head Assembly 30

44 Plate 3.11 Die Plate 3.12 Cutting Device 31

45 3.6 Determination of experimental parameters Determination of physico-chemical parameters Mass flow rate (MFR) It was measured by collecting the extrudate in polyethylene bags for a specific period of time, as soon as it came out of the extruder and its weight was taken instantly. Mass Flow Rate (MFR), g/sec. = Weight of sample collected Time taken to collect sample... (3.4) Specific length (SL) A random sample of extrudate was selected from the extruded mass and its average length was measured using digital varnier calliper. The weight of selected was determined and specific length was calculated using the formula. Specific Length (SL), mm/g = Length of specimen Weight of specimen Sectional expansion index (SEI):... (3.5) Samples were randomly selected from the extruded mass and their diameter was measured using digital screw gauge. SEI was calculated by the following expression. Diameter of Extrudate Sectional Expansion Index (SEI) = Diameter of Die Average of the three samples was calculated Bulk Density (BD): 2... (3.6) The extrudates were filled in measuring cylinder of capacity 25 ml up to full capacity and tapped for 5 to 10 times. Then the entire 25 ml of extrudate was weighed. 32

46 Bulk Density (B.D.), g/cm 3 = Weight of 25 ml sample Volume of sample (25 ml X 10-6)... (3.7) Water Absorption Index (WAI): equation: It was calculated by grinding the extrudate and applying the following Water Absorption Index (WAI) % = W 2 W 1 W 1 X (3.8) where, W 1 = Weight of grind extrudate sample (10 g) W 2 = Weight of grind extrudate sample after keeping in water (About 10 times of weight of sample) for half an hour Protein content Protein content of rice based extrusion food product of lentil and oat was determined by the method recommended by Raganna.(1942). Protein content was determined by micro-kjeldhal digestion and distillation procedure (Plate 3.13). The conversion factor to convert total nitrogen into protein for pulses is 6.25 and is calculated by the following formula:- Protein, (%) = MI of 0.1 H 2 SO 4 X 1.4 X 6.25 (conversion factor) Weight of sample X 1000 X (3.9) Nitrogen content was estimated by K-jeldhal method. The weighed sample (4 g) was digested with concentrated H 2 SO 4 and in the presence of catalyst (K 2 SO 4 ; CuSO 4 ) 100:102.5 gm for 6-8 hrs in a digestion flask. The digested sample is then cooled and quantitatively transformed to a volumetric flask. The volume of the solution is made up to the mark by addition of N 2 free water (water which had been boiled and cooled) shaken well for uniform concentration. A known volume is pipette in the distillation unit and an excess 33

47 of 40% NaOH is to be added. The distillation ammonia is absorbed in 2% boric acid and titrated against standard HCL solution mixed indicator from the titrate value the nitrogen value is calculated. A factor of 6.25 is to be used to content N2 in to protein. It is followed by digestion under which 5 ml of standard ammonia sulphate is transformed into distillation flask usually 1 ml of 40% NaOH is to be added and distilled ammonia is to be absorbed in 2% Boric acid and titrate the distillate using 0.05 N HCl Determination of Texture Properties: Textural analysis of extrudates was made using Texture Analyzer System (model TA-XT2i) available at the Department of Post Harvest Process and Food Engineering, CAE, Jabalpur. Hardness and crispness were determined with the help of different kinds of probes i.e. needle probe, cylindrical probe. Texture Analyzer is totally a graphical user interface based tool. A graphical image is created which could be easily available to the users for further analysis depending on their requirement (Plate 3.14, 3.15). Initial settings for various textural characteristics i.e. hardness and crispness is as follows: Table 3.4 Settings for Texture Analyzer (model TA-XT2i) Mode Option Pre-test speed Test speed Post test speed Distance Trigger force Data acquisition rate Accessory Measure force in compression Return to start 5.0 mm/s 2.0 mm/s 10 mm/s 10.0 mm/s Auto-25 g 200 pps Compression probe, heavy-duty platform 34

48 Plate 3.13 Kjeldhal Apparatus Needle Probe Cylindrical Probe Plate 3.14 Measurement of crispness using Texture Analyzer Plate 3.15 Measurement of hardness using Texture Analyzer 35

49 3.6.3 Determination of Sensory Parameters Sensory evaluation of the samples was done on the basis of 9-Point Hedonic scale for different sensory attributes such as color, taste, flavor, texture and overall acceptability. Sensory panel was consisted of 10 members. Faculty members, students and staff members of College of Agriculture Engineering, Jabalpur evaluated the prepared rice based extruded product of lentil and oat on 9 point hedonic scale. Sensory score sheet was provided to each of them for recording their scores. The samples were placed before the judges for their evaluation and to rates accordingly. Table 3.5 Sensory Score on 9 point Hedonic scale Sensory Score Particular 9 Liked extremely 8 Liked very much 7 Like moderately 6 Liked slightly 5 Neither liked nor disliked 4 Dislike slightly 3 Dislike moderately 2 Dislike very much 1 Dislike extremely 36

50 Plate 3.16 Sensory Evaluation of rice based extrusion cooked food product of lentil and oat 3.7 Statistical Analysis: Data analysis was done for optimization of processing and operational parameters for physico-chemical and textural properties (hardness and crispness) of rice based extrusion cooked food product of lentil and oat by using Design- Expert Best fit regression equations were also developed. Response surface graphs and contour plots were drawn with the help of Design- Expert 9.03 to analyze the effect of independent variable on the response. 37

51 RESULTS AND DISCUSSION Experiment were conducted to study the effect of process and operational parameters on physico-chemical, textural properties and sensory properties of rice based extrusion cooked food product of lentil and oat. All the experiments were carried out at Food Processing Laboratory, Department of post harvest Process and Food Engineering, College of Agricultural Engineering, JNKVV, Jabalpur. To analyze the relationship between process parameters (moisture content and blend ratio) and operational parameters (barrel temperature, screw speed and die head temperature). Central Composite Rotatable Design (CCRD) at five levels (-2, -1, 0, 1, 2) was used to study and observations were recorded accordingly. Analysis of variance was also conducted to interpret the results. Based on the observation, response surface were generated by using statistical analysis software (Design Expert 9.03). The observations recorded along with ANOVA analysis, regression coefficient and standard error are listed in APPENDIX A, B and C respectively. The results are further encompassed with second order polynomial models whose adequacy was also tested by following standard statistical process. Response surface graphs were drawn to illustrate the effect of different pair of variables on the selected responses. The results are discussed in the following sections. 4.1 Effect of process and operational parameters on the physicochemical properties of rice based extrusion cooked food product of lentil and oat Mass Flow Rate (MFR) The variation of mass flow rate was studied against the selected combination of variation of moisture content, blend ratio, screw speed, barrel temperature and die head temperature. The variation in mass flow rate (MFR) is described by a polynomial equation of second order. The equation in coded values generated by multiple regression analysis using CCRD is as follows: 38

52 MFR = MC F BR SS B.T DHT E -003 M.C B.R E-003 M.C SS E-003 M.C B.T E-003 M.C DHT E-003 B.R SS E-003 B.R B.T E-003 B.R DHT E-003 S.S B.T OE-003 S.S DHT E- 004 B.T DHT M.C B.R E-004 S.S E-004 B.T DHT. 2[ (4.1) The three dimensional graphs representing the interactive effect of process parameters (moisture content and blend ratio) and operational parameters (barrel temperature, screw speed and die head temperature) on the mass flow rate of rice based extrusion cooked food product of lentil and oat are shown in Fig. 4.1 to 4.6. It can be observed from Fig. 4.1 that the mass flow rate of extrudates increases with increase in moisture content and it decreases with increase in oat flour in the blend. The mass flow rate is low at lower moisture content which goes on increasing with increases in moisture content. At higher feed moisture level the amount of heat supplied during extrusion by shearing as well as by direct heating cannot create enough vapor pressure to vaporize all the moisture by flash off as it comes out of the die. The mass of water that could not be vaporized increases the mass of extrudates. A decrease in mass flow rate with an increment in proportion of oat flour in blend which has higher fiber content was observed and at higher fiber content puffing reduces. Fig. 4.2 reveals a linear relationship between MFR and screw speed because the worm is basically a screw conveyor. Therefore, higher the screw speed more will be mass flow rate. The Fig.4.3 shows the response contours in reverse umbrella shape where the contours were spreading in outward direction which means the lowest value of moisture content of feed and barrel temperature lies nearly in the centre of contour and moving either side will increase the value of MFR. Fig. 4.4 shows that the mass flow rate increases with respect to die head temperature. The response surface graph showed the maximum value of mass flow rate lies at C die head temperature. Fig 4.5 shows that the variation of MFR with respect to barrel temperature is 39

53 M a s s F l o w R a t e ( g / s e c ) M a s s F l o w R a t e ( g / s e c ) M a s s F lo w R a t e ( g / s e c ) M a s s F l o w R a t e ( g / s e c ) increasing linearly along the axis of screw speed. Similar trend was followed in Fig. 4.6 it shows with increase in die head temperature and barrel temperature, mass flow rate is also increased. The mass flow rate ranged from 0.20 to 0.60 g/sec with a mean Design-Expert Software Factor Coding: Actual Mass Flow Rate (g/sec) Design-Expert Software Factor Coding: Actual Mass Flow Rate (g/sec) Design points above predicted value Design points below predicted value 1.5 X1 = A: Moisture Content X2 = B: Blend Ratio Actual Factors C: Screw Speed = D: Barrel Temperature = E: Die Head Temperature = X1 = A: Moisture Content X2 = C: Screw Speed Actual Factors B: Blend Ratio = 15 D: Barrel Temperature = 150 E: Die Head Temperature = B: Blend Ratio A: Moisture Content (%, wb) C: Screw Speed (RPM) A: Moisture Content (%, wb) Fig.4.1 Effect of moisture content and blend ratio on mass flow rate of extrudate Fig.4.2 Effect of moisture content and screw speed on mass flow rate of extrudate Design-Expert Software Factor Coding: Actual Mass Flow Rate (g/sec) Design points above predicted value Design points below predicted value 1.5 Design-Expert Software Factor Coding: Actual Mass Flow Rate (g/sec) Design points above predicted value Design points below predicted value X1 = A: Moisture Content X2 = D: Barrel Temperature Actual Factors B: Blend Ratio = 15 C: Screw Speed = 80 E: Die Head Temperature = X1 = A: Moisture Content X2 = E: Die Head Temperature Actual Factors B: Blend Ratio = 15 C: Screw Speed = 80 D: Barrel Temperature = D: Barrel Temperature (0C) A: Moisture Content (%, wb) E: Die Head Temperature (0C) A: Moisture Content (%, wb) Fig.4.3 Effect of moisture content and barrel temperature on mass flow rate of extrudate Fig.4.4 Effect of moisture content and die head temperature on mass flow rate of extrudate 40

54 M a s s F l o w R a t e ( g / s e c ) M a s s F l o w R a t e ( g / s e c ) Design-Expert Software Factor Coding: Actual Mass Flow Rate (g/sec) Design points above predicted value Design points below predicted value 1.5 Design-Expert Software Factor Coding: Actual Mass Flow Rate (g/sec) Design points above predicted value Design points below predicted value X1 = C: Screw Speed X2 = D: Barrel Temperature 2 X1 = D: Barrel Temperature X2 = E: Die Head Temperature 2 Actual Factors A: Moisture Content = 12 B: Blend Ratio = 15 E: Die Head Temperature = Actual Factors A: Moisture Content = 12 B: Blend Ratio = 15 C: Screw Speed = D: Barrel Temperature (0C) C: Screw Speed (RPM) E: Die Head Temperature (0C) D: Barrel Temperature (0C) Fig 4.5 Effect of screw speed and barrel temperature on mass flow rate of extrudate Fig 4.6 Effect of barrel temperature and die head temperature on mass flow rate of extrudate. The R 2 had a value of 0.75 for the model. The result of analysis of variance (ANOVA) for the model 4.1 is presented in Appendix-A 1 and brief information is presented in table 4.1. Table 4.1 ANOVA for mass flow rate of rice based extruded food product of lentil and oat Source DF SS MSS F P Regression Residual Total Regression coefficient and standard error of second order mathematical model are reported in Appendix-B 1. The significance of each term is also reported. The positive coefficient at linear level indicates that there was increase in response with increase in level of selected parameters and vice-versa. Negative quadratic terms indicated that the maximum value of the response was at the center point. The standard deviation, coefficient of variance, mean and predicted residual error sum of square values, coefficient of determination and predicted R 2 and adequate precision are given in Appendix-C. 41

55 4.1.2 Specific length (SL) The variation of specific length was studied against the selected combination of variation of moisture content, blend ratio, screw speed, barrel temperature and die head temperature. The variation in specific length is described by a polynomial equation of second order. The equation in coded values generated by multiple regression analysis using CCRD is as follows: SL= MC F 1.87 BR SS B.T DHT E-003 M.C B.R E M.C SS M.C B.T M.C DHT E -003 B.R SS 4.870E B.R B.T E -003 B.R DHT 3.338E- 003 S.S B.T 5.07 S.S DHT 1.930E -003 B.T DHT M.C E B.R E -004 S.S E B.T E DHT 2. (4.2) The three dimensional graphs representing the interactive effect of process parameters (moisture content and blend ratio) and operational parameters (barrel temperature, screw speed and die head temperature) on the specific length of rice based extrusion cooked food product of lentil and oat are shown in Fig. 4.7 to 4.12 respectively. The moisture content of feed has a vital influence on specific length of extrudates. Fig 4.7 shows with an increase in the moisture content specific length of extrudates also increase. Increment in moisture content and temperature causes the more formation of more numbers of air pockets and bubbles in the product consequently larger specific length. It can also be seen from Fig.4.7 that specific length of the extrudates increases with increase in proportion of oat flour in blend ratios. Fig. 4.8 shows that an increment in screw speed increases the specific length of extrudates. At higher screw speed the shear rate is high and porous structures is generated as well as at high shear the fibers remains at outer layers leading providing restriction in cross sectional expansion and thereby extrudates are forced to expand longitudinally only. 42

56 S p e c if ic L e n g t h ( m m / g ) S p e c if ic L e n g t h ( m m / g ) Fig. 4.9 shows that the specific length of extrudates increases with increase in barrel temperature. The movements of contour towards the higher value of moisture means that as the value of moisture content increases the specific length of extrudates also increase correspondingly. Fig 4.10 shows that specific length of extrudates increases with increase in die head temperature. This increase is more prominent at lower moisture value as compared to higher moisture value. Fig shows a minor increase in specific length with increases in barrel temperature. Fig.4.12 shows the effect of barrel temperature and die head temperature on specific length. The highest value of specific length lies at C die head temperature and C barrel temperature. It can be seen that specific length varied from 3.09 to 9.15 mm/g with a mean Design-Expert Software Factor Coding: Actual Specific Length (mm/g) X1 = A: Moisture Content X2 = B: Blend Ratio Actual Factors C: Screw Speed = 60 D: Barrel Temperature = 130 E: Die Head Temperature = Design-Expert Software Factor Coding: Actual Specific Length (mm/g) X1 = A: Moisture Content X2 = C: Screw Speed Actual Factors B: Blend Ratio = D: Barrel Temperature = 130 E: Die Head Temperature = B: Blend Ratio A: Moisture Content (%, wb) C: Screw Speed (RPM) A: Moisture Content (%, wb) Fig 4.7 Effect of moisture content and blend ratio on specific length of extrudate Fig 4.8 Effect of moisture content and screw speed on specific length of extrudate 43

57 S p e c if ic L e n g t h ( m m / g ) S p e c if ic L e n g t h ( m m / g ) S p e c if ic L e n g t h ( m m / g ) S p e c if ic L e n g t h ( m m / g ) Design-Expert Software Factor Coding: Actual Specific Length (mm/g) X1 = A: Moisture Content X2 = D: Barrel Temperature Actual Factors B: Blend Ratio = C: Screw Speed = 60 E: Die Head Temperature = Design-Expert Software Factor Coding: Actual Specific Length (mm/g) X1 = A: Moisture Content X2 = E: Die Head Temperature Actual Factors B: Blend Ratio = C: Screw Speed = 60 D: Barrel Temperature = D: Barrel Temperature (0C) A: Moisture Content (%, wb) 200 E: Die Head Temperature (0C) A: Moisture Content (%, wb) Fig 4.9 Effect of moisture content and barrel temperature on specific length of extrudate Fig 4.10 Effect of moisture content and die head temperature on specific length of extrudate Design-Expert Software Factor Coding: Actual Specific Length (mm/g) X1 = C: Screw Speed X2 = D: Barrel Temperature Actual Factors A: Moisture Content = B: Blend Ratio = E: Die Head Temperature = Design-Expert Software Factor Coding: Actual Specific Length (mm/g) X1 = D: Barrel Temperature X2 = E: Die Head Temperature Actual Factors A: Moisture Content = B: Blend Ratio = C: Screw Speed = D: Barrel Temperature (0C) C: Screw Speed (RPM) E: Die Head Temperature (0C) D: Barrel Temperature (0C) Fig 4.11 Effect of screw speed and barrel temperature on specific length of extrudate Fig 4.12 Effect of barrel temperature and die head temperature on specific length of extrudate 44

58 The R 2 had a value of 0.74 for the model. The results of analysis of variance (ANOVA) for the model 4.2 are presented in Appendix-A 2 and brief information is presented in table 4.2. Table 4.2 ANOVA for specific length of rice based extruded food product of lentil and oat Source DF SS MSS F P Regression Residual Total Regression coefficient and standard error of second order mathematical model are reported in Appendix-B 2. The significance of each term is also reported. The positive coefficient at linear level indicates that there was increase in response with increase in level of selected parameters and vice-versa. Negative quadratic terms indicated that the maximum value of the response was at the center point. The standard deviation, coefficient of variance, mean and predicted residual error sum of square values, coefficient of determination and predicted R 2 and adequate precision are given in Appendix-C Sectional expansion index (SEI) The variation of sectional expansion index was studied against the selected combination of variation of moisture content, blend ratio, screw speed, barrel temperature and die head temperature. The variation in Sectional expansion index is described by a polynomial equation of second order. The equation in coded values generated by multiple regression analysis using CCRD is as follows: SEI = MC F BR SS B.T DHT M.C B.R 2.00E 003 M.C SS M.C B.T E 003 M.C DHT E-003 B.R SS E -003 B.R B.T B.R DHT E-004 S.S B.T E-004 S.S DHT E-004 B.T DHT M.C E -003 B.R E-003 S.S E-004 B.T E-003 DHT 2. (4.3) 45

59 The response surface graphs (Fig 4.13 to 4.16) represents the interactive effect of feed moisture content with other parameter i.e. blend ratio, screw speed, barrel temperature, die head temperature on SEI of extrudates. From Fig 4.13 it is clear that sectional expansion index increases with increase in the moisture content from 8% to 14%, however a decrease in SEI can be observed if moisture is increased beyond 14%. It can also be observed from 4.13 that the SEI of extrudates decreases with an increment in proportion of oat flour in the blend ratio. Oat flour contains higher fiber content and thus reduction in SEI of extrudates was observed. Fig 4.14 shows that the screw speed did not affect the sectional expansion index. It can be observed from Fig 4.15 that sectional expansion index increases with increase in the barrel temperature of zone 3 this may be of the fact that because at higher barrel temperature, the temperature of feed melt is more and the shear in completed more uniformly therefore after evaporation the moisture of central part is retained in the extrudates. From Fig it can be observed that with increases in die head temperature the sectional expansion index of extrudates also increases. This is because of the facts that increase in die head temperature increases the amount of moisture evaporated by flash off and as the more amount of moisture is flashed off more capillaries are formed and pores structure is created which ultimately results in increased diameter of extrudates. In the Fig the highest value of SEI lies at C barrel temperature. The same trend had been observed in Fig Maximum value of SEI was obtained at about C die head temperature and C barrel temperature. The sectional expansion index ranged from 4.2 to 7.9 with a mean

60 S e c t io n a l E x p a n s io n I n d e x S e c t io n a l E x p a n s io n I n d e x S e c t io n a l E x p a n s io n I n d e x S e c t io n a l E x p a n s io n I n d e x Design-Expert Software Factor Coding: Actual Sectional Expansion Index 7.9 Design-Expert Software Factor Coding: Actual Sectional Expansion Index X1 = A: Moisture Content X2 = B: Blend Ratio Actual Factors C: Screw Speed = D: Barrel Temperature = E: Die Head Temperature = X1 = A: Moisture Content X2 = C: Screw Speed Actual Factors B: Blend Ratio = D: Barrel Temperature = E: Die Head Temperature = B: Blend Ratio A: Moisture Content (%, wb) C: Screw Speed (RPM) A: Moisture Content (%, wb) Fig 4.13 Effect of moisture content and blend ratio on sectional expansion index of extrudate Fig 4.14 Effect of moisture content and screw speed on sectional expansion index of extrudate Design-Expert Software Factor Coding: Actual Sectional Expansion Index X1 = A: Moisture Content X2 = D: Barrel Temperature Actual Factors B: Blend Ratio = C: Screw Speed = E: Die Head Temperature = Design-Expert Software Factor Coding: Actual Sectional Expansion Index X1 = A: Moisture Content X2 = E: Die Head Temperature Actual Factors B: Blend Ratio = C: Screw Speed = D: Barrel Temperature = D: Barrel Temperature (0C) A: Moisture Content (%, wb) E: Die Head Temperature (0C) A: Moisture Content (%, wb) Fig 4.15 Effect of moisture content and barrel temperature on sectional expansion index of extrudate Fig 4.16 Effect of moisture content and die head temperature on sectional expansion index extrudate 47

61 S e c t io n a l E x p a n s io n I n d e x S e c t io n a l E x p a n s io n I n d e x Design-Expert Software Factor Coding: Actual Sectional Expansion Index 7.9 Design-Expert Software Factor Coding: Actual Sectional Expansion Index X1 = C: Screw Speed X2 = D: Barrel Temperature Actual Factors A: Moisture Content = B: Blend Ratio = E: Die Head Temperature = X1 = D: Barrel Temperature X2 = E: Die Head Temperature Actual Factors A: Moisture Content = B: Blend Ratio = C: Screw Speed = D: Barrel Temperature (0C) C: Screw Speed (RPM) E: Die Head Temperature (0C) D: Barrel Temperature (0C) Fig 4.17 Effect of screw speed and barrel temperature on sectional expansion index of extrudate Fig 4.18 Effect of barrel temperature and die head temperature on sectional expansion index of extrudate The R 2 had a value of 0.90 for the model. The results of analysis of variance (ANOVA) for the model 4.3 are presented in Appendix-A 3 and brief information is presented in table 4.3. Table 4.3 ANOVA for sectional expansion index of rice based food product of lentil and oat extruded Source DF SS MSS F P Regression Residual Total Regression coefficient and standard error of second order mathematical model are reported in Appendix-B 3. The significance of each term is also reported. The positive coefficient at linear level indicates that there was increase in response with increase in level of selected parameters and vice-versa. Negative quadratic terms indicated that the maximum value of the response was at the center point. The standard deviation, coefficient of variance, mean and predicted residual error sum of square values, coefficient of determination and predicted R 2 and adequate precision are given in Appendix-C. 48

62 4.1.4 Bulk density (BD) The effect of the selected combination of variation of moisture content, blend ratio, screw speed, barrel temperature and die head temperature on bulk density of extrudate is discussed in following section. The variation in bulk density is described by a polynomial equation of second order. The equation in coded values generated by multiple regression analysis using CCRD is given below: Bulk density = MC F 0.06 BR SS B.T DHT E-003 M.C B.R E 004 M.C SS E-004 M.C B.T E-005 M.C DHT E-003 B.R SS E -004 B.R B.T E-004 B.R DHT E-004 S.S B.T E-004 S.S DHT E-004 B.T DHT M.C E -005 B.R E-005 S.S E-004 B.T E-004 DHT 2. (4.4) It can be seen from Fig 4.19 that the bulk density of extrudate increases with increase in proportion of oat flour in blend it is because with increase in moisture content the mass per unit volume of extrudates increases which is ultimately responsible for increase in bulk density. The Figure 4.20 reveals that screw speed had no effect on bulk density of extrudates. Fig 4.21 shows the look of response contour in reverse umbrella shape which means the lowest value of independed variable moisture content and barrel temperature lies nearly in the centre of contour and moving either side will increases the value of bulk density. The minimum value of bulk density was observed at 12% m.c. and C. Fig 4.22 shows that the variation of bulk density of extrudate with respect to die head temperature is as curvilinear along the axis of moisture content as that of feed. Fig 4.23 reveals that bulk density increases with increase in barrel temperature and screw speed did not affect the bulk density of extrudate. Fig shows that minimum value of bulk density observed at C die head temperature. The bulk density ranged from 0.13 to 0.67 g/cm 3 with a mean

63 B u l k D e n s i t y ( g / c m 3 ) B u l k D e n s i t y ( g / c m 3 ) B u l k D e n s it y ( g / c m 3 ) B u l k D e n s i t y ( g / c m 3 ) Design-Expert Software Factor Coding: Actual Bulk Density (g/cm3) X1 = A: Moisture Content X2 = B: Blend Ratio Actual Factors C: Screw Speed = D: Barrel Temperature = E: Die Head Temperature = Design-Expert Software Factor Coding: Actual Bulk Density (g/cm3) Design points above predicted value Design points below predicted value X1 = A: Moisture Content X2 = C: Screw Speed Actual Factors B: Blend Ratio = 15 D: Barrel Temperature = 150 E: Die Head Temperature = B: Blend Ratio A: Moisture Content (%, wb) C: Screw Speed (RPM) A: Moisture Content (%, wb) Fig 4.19 Effect of moisture content and blend ratio on bulk density of extrudate Fig 4.20 Effect of moisture content and screw speed on bulk density of extrudate Design-Expert Software Factor Coding: Actual Bulk Density (g/cm3) Design points above predicted value Design points below predicted value X1 = A: Moisture Content X2 = D: Barrel Temperature Actual Factors B: Blend Ratio = 15 C: Screw Speed = 80 E: Die Head Temperature = Design-Expert Software Factor Coding: Actual Bulk Density (g/cm3) Design points above predicted value Design points below predicted value X1 = A: Moisture Content X2 = E: Die Head Temperature Actual Factors B: Blend Ratio = 15 C: Screw Speed = 80 D: Barrel Temperature = D: Barrel Temperature (0C) A: Moisture Content (%, wb) E: Die Head Temperature (0C) A: Moisture Content (%, wb) Fig 4.21 Effect of moisture content and barrel temperature on bulk density of extrudate Fig 4.22 Effect of moisture content and die head temperature on bulk density of extrudate 50

64 B u l k D e n s i t y ( g / c m 3 ) B u l k D e n s i t y ( g / c m 3 ) Design-Expert Software Factor Coding: Actual Bulk Density (g/cm3) Design points above predicted value Design points below predicted value X1 = C: Screw Speed X2 = D: Barrel Temperature Actual Factors A: Moisture Content = 12 B: Blend Ratio = 15 E: Die Head Temperature = Design-Expert Software Factor Coding: Actual Bulk Density (g/cm3) Design points above predicted value Design points below predicted value X1 = D: Barrel Temperature X2 = E: Die Head Temperature Actual Factors A: Moisture Content = 12 B: Blend Ratio = 15 C: Screw Speed = D: Barrel Temperature (0C) C: Screw Speed (RPM) E: Die Head Temperature (0C) D: Barrel Temperature (0C) Fig.4.23 Effect of screw speed and barrel temperature on bulk density of extrudate Fig Effect of barrel temperature and die head temperature on bulk density of extrudate The R 2 had a value of 0.85 for the model. The results of analysis of variance (ANOVA) for the model 4.4 are presented in Appendix-A 4 and brief information is presented in table 4.4. Table 4.4 ANOVA for bulk density of rice based extruded food product of lentil and oat Source DF SS MSS F P Regression Residual Total Regression coefficient and standard error of second order mathematical model are reported in Appendix-B 4. The significance of each term is also reported. The positive coefficient at linear level indicates that there was increase in response with increase in level of selected parameters and vice-versa. Negative quadratic terms indicated that the maximum value of the response was at the center point. The standard deviation, coefficient of variance, mean and predicted residual error sum of square values, coefficient of determination and predicted R 2 and adequate precision are given in Appendix-C. 51

65 4.1.5 Water Absorption Index The variation of water absorption index was studied against the selected combination of variation of moisture content, blend ratio, screw speed, barrel temperature and die head temperature. The variation in water absorption index is described by a polynomial equation of second order. The equation in coded values generated by multiple regression analysis using CCRD is as follows: WAI = MC F BR SS B.T DHT M.C B.R M.C SS M.C B.T M.C DHT B.R SS B.R B.T B.R DHT S.S B.T S.S DHT E-003 B.T DHT 2.34 M.C B.R S.S B.T DHT 2. (4.5) The three dimensional graphs representing the interactive effect of process parameters (moisture content and blend ratio) and operational parameters (barrel temperature, screw speed and die head temperature) on the water absorption index of rice based extrusion cooked food product of lentil and oat are shown in Fig to 4.30 respectively. It can be seen from Fig there is umbrella shaped response surface which shows highest value of WAI of extrudate at intersection of the centre points of independent variables, i.e. at 12% m.c. of feed and blend ratio 70:15:15. The probable reason for such a trend may be due to the increases in lentil flour in blend there is an increase in the protein content and similarly an increase in oat flour in blend increases the fiber content. In both the cases sectional expansion index reduces and less capillaries are formed and hence pore space available to absorb the water are reduced thereby giving the lesser values of the WAI. Fig 4.26 shows that at lower screw speed rpm WAI decreases however, an increase in WAI can be observed if screw speed is increases beyond 80 rpm. This is because of the fact that at higher screw speed more shear is generated which results in more porous structure and thus increases 52

66 W a t e r A b s o r p t io n I n d e x W a t e r A b s o r p t io n I n d e x water absorption Index. Fig 4.27 shows that water absorption index increases with an increment in barrel temperature. Fig.4.28 shows a slight increase in WAI with increment in die head temperature. At higher die head temperature the water absorption index was higher because it decreases the moisture content of extrudates and therefore more ability of extrudates to absorb moisture. Design-Expert Software Factor Coding: Actual Water Absorption Index From Fig 4.29 it can be observed that at a constant barrel temperature the WAI decreases initially and then starts rising if the screw speed is in increased beyond 80 rpm. Highest value of water absorption index lies at C barrel temperature and C. The water absorption index ranged from 500% to 720% with a mean of Design-Expert Software Factor Coding: Actual Water Absorption Index 720 X1 = A: Moisture Content X2 = B: Blend Ratio Actual Factors C: Screw Speed = 60 D: Barrel Temperature = E: Die Head Temperature = X1 = A: Moisture Content X2 = C: Screw Speed Actual Factors B: Blend Ratio = D: Barrel Temperature = E: Die Head Temperature = B: Blend Ratio A: Moisture Content (%, wb) C: Screw Speed (RPM) A: Moisture Content (%, wb) Fig.4.25 Effect of moisture content and blend ratio on water absorption index of extrudate Fig.4.26 Effect of moisture content and screw speed on water absorption index of extrudate 53

67 W a t e r A b s o r p t io n I n d e x W a t e r A b s o r p t io n I n d e x W a t e r A b s o r p t io n I n d e x W a t e r A b s o r p t io n I n d e x Design-Expert Software Factor Coding: Actual Water Absorption Index 720 Design-Expert Software Factor Coding: Actual Water Absorption Index X1 = A: Moisture Content X2 = D: Barrel Temperature 750 X1 = A: Moisture Content X2 = E: Die Head Temperature 750 Actual Factors B: Blend Ratio = C: Screw Speed = 60 E: Die Head Temperature = Actual Factors B: Blend Ratio = C: Screw Speed = 60 D: Barrel Temperature = D: Barrel Temperature (0C) A: Moisture Content (%, wb) E: Die Head Temperature (0C) A: Moisture Content (%, wb) Fig.4.27 Effect of moisture content and barrel temperature on water absorption index of extrudate Fig.4.28 Effect of moisture content and die head temperature on water absorption index of extrudate Design-Expert Software Factor Coding: Actual Water Absorption Index 720 Design-Expert Software Factor Coding: Actual Water Absorption Index X1 = C: Screw Speed X2 = D: Barrel Temperature 900 X1 = D: Barrel Temperature X2 = E: Die Head Temperature 900 Actual Factors A: Moisture Content = B: Blend Ratio = E: Die Head Temperature = Actual Factors A: Moisture Content = B: Blend Ratio = C: Screw Speed = D: Barrel Temperature (0C) C: Screw Speed (RPM) E: Die Head Temperature (0C) D: Barrel Temperature (0C) Fig.4.29 Effect of screw speed and barrel temperature on water absorption index of extrudate Fig Effect of barrel temperature and die head temperature on water absorption index of extrudate 54

68 The R 2 had a value of 0.77 for the model. The results of analysis of variance (ANOVA) for the model 4.5 are presented in Appendix-A 5 and brief information are presented in table 4.5 Table 4.5 ANOVA for water absorption index of rice based extruded food product of lentil and oat Source DF SS MSS F P Regression Residual Total Regression coefficient and standard error of second order mathematical model are reported in Appendix-B 5.The significance of each term is also reported. The positive coefficient at linear level indicates that there was increase in response with increase in level of selected parameters and vice-versa. Negative quadratic terms indicated that the maximum value of the response was at the center point. The standard deviation, coefficient of variance, mean and predicted residual error sum of square values, coefficient of determination and predicted R 2 and adequate precision are given in Appendix-C Protein The variation in protein is described by a polynomial equation of second order. The equation in coded values generated by multiple regression analysis using CCRD reads as follows: Protein = MC F BR SS B.T DHT E-003 M.C B.R E-003 M.C SS E-003 M.C B.T M.C DHT E- 003 B.R SS E-003 B.R B.T E-003 B.R DHT E-003 S.S B.T -1.85OE-003 S.S DHT E- 003 B.T DHT M.C B.R E- 003 S.S E-005 B.T E-003 DHT 2. (4.6) 55

69 Fig reveals that the protein content of extrudates increases with decrease in percentage of oat flour in the blends. A reduction in percentage of oat flour in the blends is substituted by lentil flour. Lentil flour contains higher percentage of protein as compare to the oat flour. Therefore, the blends having higher percentage of lentil gave higher values of protein content in the extrudates. Also it is seen that with increase in moisture content the protein content of extrudates increases. This may be because of two reasons; firstly with increase in moisture content of feed it has been found that the mass flow rate also increases and secondly increase in moisture content absorbs all the heat supplied to melt inside the cooking zone and thus leaving the protein unchanged in its biochemical characteristics. Fig 4.32 shows that the protein content of extrudates decreases as the screw speed increases. At higher screw speed, the shear rate inside the barrel is high which not only results in denaturation of protein but it may also destroy the nature of some protein molecules. Thereby, ultimately reducing the protein content of extrudates. Effect of barrel temperature and moisture content on protein content of extrudates is presented in Fig It shows a little increase in protein content which may be mainly because of increased barrel temperature results in increased loss of moisture from extrudates. Thus, increasing the percentage of protein in extrudates as compared to that with the feed. It is seen from Fig that the highest value of protein obtained at centre value of die head temperature C. Fig 4.35 shows the effect of barrel temperature and screw speed on protein content of extrudates. It shows that increases in protein with increase in barrel temperature and screw speed. Fig 4.36 shows that the maximum protein was obtained at C die head temperature and C barrel temperature. The maximum value of protein content of extrudate was found to be 8.02% whereas its minimum value was 13.62%. 56

70 P r o t e in ( % ) P r o t e in ( % ) P r o t e i n ( % ) P r o t e i n ( % ) Design-Expert Software Factor Coding: Actual Protein (%) Design points above predicted value Design points below predicted value X1 = A: Moisture Content X2 = B: Blend Ratio Actual Factors C: Screw Speed = 80 D: Barrel Temperature = 150 E: Die Head Temperature = Design-Expert Software Factor Coding: Actual Protein (%) Design points above predicted value Design points below predicted value X1 = A: Moisture Content X2 = C: Screw Speed Actual Factors B: Blend Ratio = 15 D: Barrel Temperature = 150 E: Die Head Temperature = B: Blend Ratio A: Moisture Content (%, wb) C: Screw Speed (RPM) A: Moisture Content (%, wb) Fig.4.31 Effect of moisture content and blend ratio on protein content of extrudate Fig.4.32 Effect of moisture content and screw speed on protein content of extrudate Design-Expert Software Factor Coding: Actual Protein (%) Design-Expert Software Factor Coding: Actual Protein (%) X1 = A: Moisture Content X2 = D: Barrel Temperature 14 X1 = A: Moisture Content X2 = E: Die Head Temperature 14 Actual Factors B: Blend Ratio = C: Screw Speed = E: Die Head Temperature = Actual Factors B: Blend Ratio = C: Screw Speed = D: Barrel Temperature = D: Barrel Temperature (0C) A: Moisture Content (%, wb) E: Die Head Temperature (0C) A: Moisture Content (%, wb) Fig.4.33 Effect of moisture content and barrel temperature on protein content of extrudate Fig.4.34 Effect of moisture content and die head temperature on protein content of extrudate 57

71 P r o t e in ( % ) P r o t e in ( % ) Design-Expert Software Factor Coding: Actual Protein (%) Design-Expert Software Factor Coding: Actual Protein (%) X1 = C: Screw Speed X2 = D: Barrel Temperature 14 X1 = D: Barrel Temperature X2 = E: Die Head Temperature 13 Actual Factors A: Moisture Content = B: Blend Ratio = E: Die Head Temperature = Actual Factors A: Moisture Content = B: Blend Ratio = C: Screw Speed = D: Barrel Temperature (0C) C: Screw Speed (RPM) E: Die Head Temperature (0C) D: Barrel Temperature (0C) Fig.4.35 Effect of screw speed and barrel temperature on protein content of extrudate Fig.4.36 Effect of barrel temperature and die head temperature on protein content of extrudate The R 2 had a value of 0.71 for the model. The result of analysis of variance (ANOVA) for the model 4.6 is presented in Appendix-A 6 and brief information is presented in table 4.6. Table 4.6 ANOVA for protein of rice based extruded food product of lentil and oat Source DF SS MSS F P Regression Residual Total Regression coefficient and standard error of second order mathematical model are reported in Appendix-B 6. The significance of each term is also reported. The positive coefficient at linear level indicates that there was increase in response with increase in level of selected parameters and vice-versa. Negative quadratic terms indicated that the maximum value of the response was at the center point. The standard deviation, coefficient of variance, mean and predicted residual error sum of square values, coefficient of determination and predicted R 2 and adequate precision are given in Appendix-C. 58

72 4.2 Effect of process and operational parameters on the textural properties of rice based extrusion cooked food product of lentil and oat Hardness The variation of hardness was studied against the selected combination of variation of moisture content, blend ratio, screw speed, barrel temperature and die head temperature. The variation in hardness is described by a polynomial equation of second order. The equation in coded values generated by multiple regression analysis using CCRD reads as follows: Hardness = MC F BR SS E-003 B.T DHT M.C B.R M.C SS E-003 M.C B.T E-003 M.C DHT E-003 B.R SS E-003 B.R B.T E-003 B.R DHT E E-003 S.S B.T E-003 S.S DHT E-004 B.T DHT M.C E-003 B.R E-003 S.S E-005 B.T E-004 DHT 2.(4.7) Hardness of extrudates is the resistance offered for breaking when subjected to a compressive load. As seen from Fig 4.37 that hardness of extrudates increases with increase in oat content in blend ratio. Increases in amount of oat flour resulted in increases in fiber content of extrudates thereby increasing the strength of outer crust of extrudates. Hardness of the extrudates decreases with increase in moisture content of feed till moisture content reaches a value of 12%. Hardness of extrudates shows an increment as the moisture content is increased beyond 12%. It may be because the feed materials become hard due to increasing moisture content. From Fig it can be observed that with increases in screw speed the hardness of extrudates increase because the increases the screw speed creates a better homogeneous mass by better mixing of melt inside the barrel, which creates uniform structure of extrudates and places fibers uniformly on the upper layer of extrudates and imparting a hard upper coat to the extrudates. 59

73 H a r d n e s s ( k g ) H a r d n e s s ( k g ) Design-Expert Software Factor Coding: Actual Hardness (kg) Design points above predicted value Design points below predicted value X1 = A: Moisture Content X2 = B: Blend Ratio Actual Factors C: Screw Speed = 80 D: Barrel Temperature = 150 E: Die Head Temperature = As seen from Fig that hardness increases gradually with increase in barrel temperature. Fig 4.40 shows that hardness increases with increase in die head temperature, which may be mainly due to the strengthening of fiber content of oat flour present in outer layers of extrudates at higher temperature. Fig.4.41 shows minimum value of hardness lies at C barrel temperature and rpm screw speed. The same trend had been observed in Fig which shows the minimum value of hardness lies at C and C barrel temperature. The maximum value of hardness of extrudates was found to 4.7 kg whereas its minimum value was 2.1 kg. Design-Expert Software Factor Coding: Actual Hardness (kg) Design points above predicted value Design points below predicted value X1 = A: Moisture Content X2 = C: Screw Speed Actual Factors B: Blend Ratio = 15 D: Barrel Temperature = 150 E: Die Head Temperature = B: Blend Ratio A: Moisture Content (%, wb) C: Screw Speed (RPM) A: Moisture Content (%, wb) Fig.4.37 Effect of moisture content and blend ratio on hardness of extrudate Fig.4.38 Effect of moisture content and screw speed on hardness of extrudate 60

74 H a r d n e s s ( k g ) H a r d n e s s ( k g ) H a r d n e s s ( k g ) H a r d n e s s ( k g ) sign-expert Software ctor Coding: Actual rdness (kg) Design points above predicted value Design points below predicted value 4.7 Design-Expert Software Factor Coding: Actual Hardness (kg) Design points above predicted value Design points below predicted value = A: Moisture Content = D: Barrel Temperature tual Factors Blend Ratio = 15 Screw Speed = 80 Die Head Temperature = X1 = A: Moisture Content X2 = E: Die Head Temperature Actual Factors B: Blend Ratio = 15 C: Screw Speed = 80 D: Barrel Temperature = D: Barrel Temperature (0C) A: Moisture Content (%, wb) E: Die Head Temperature (0C) A: Moisture Content (%, wb) Fig.4.39 Effect of moisture content and barrel temperature on hardness of extrudate Fig.4.40 Effect of moisture content and die head temperature on hardness of extrudate Design-Expert Software Factor Coding: Actual Hardness (kg) Design points above predicted value Design points below predicted value 4.7 Design-Expert Software Factor Coding: Actual Hardness (kg) Design points above predicted value Design points below predicted value X1 = C: Screw Speed X2 = D: Barrel Temperature Actual Factors A: Moisture Content = 12 B: Blend Ratio = 15 E: Die Head Temperature = X1 = D: Barrel Temperature X2 = E: Die Head Temperature Actual Factors A: Moisture Content = 12 B: Blend Ratio = 15 C: Screw Speed = D: Barrel Temperature (0C) C: Screw Speed (RPM) E: Die Head Temperature (0C) D: Barrel Temperature (0C) Fig.4.41 Effect of screw speed and barrel temperature on hardness of extrudate Fig.4.42 Effect of barrel temperature and die head temperature on hardness of extrudate 61

75 The R 2 had a value of 0.73 for the model. The results of analysis of variance (ANOVA) for the model 4.7 are presented in Appendix-A 7 and brief information is presented in table 4.7. Table 4.7 ANOVA for hardness of rice based extruded food product of lentil and oat Source DF SS MSS F P Regression Residual Total Regression coefficient and standard error of second order mathematical model are reported in Appendix-B 7. The significance of each term is also reported. The positive coefficient at linear level indicates that there was increase in response with increase in level of selected parameters and vice-versa. Negative quadratic terms indicated that the maximum value of the response was at the center point. The standard deviation, coefficient of variance, mean and predicted residual error sum of square values, coefficient of determination and predicted R 2 and adequate precision are given in Appendix-C Crispness The variation of crispness was studied against the selected combination of variation of moisture content, blend ratio, screw speed, barrel temperature and die head temperature. The variation in crispness is described by a polynomial equation of second order. The equation in coded values generated by multiple regression analysis using CCRD reads as follows: 62

76 Crispness = MC F BR SS B.T DHT M.C B.R M.C SS M.C B.T M.C DHT E-003 B.R SS E-003 B.R B.T E-003 B.R DHT E-004 S.S B.T S.S DHT E-003 B.T DHT M.C E-003 B.R E-003 S.S E-004 B.T E-003 DHT 2.(4.8) Crispness of any product is a measure of its quality and freshness. It is measured by counting the no. of peaks formed on textural profile analysis curve when subjected to uniaxial compressive loading by a needle probe. From Fig it can observed that crispness increases with decrease in moisture content of feed which is an indication of creation of more porous structure of extrudates leading to increase in crispness. It can also be seen that crispness of extrudates increases as the proportion of oat flour increases in blend ratio and correspondingly as increases in oat flour decrease in lentil flour due to which increased in fiber content among all other component, reducing the formation of pores structure of extrudates and hence, reduction in crispness of extrudates. Fig 4.44 shows a reverse umbrella shape which may be because at lower temperature due to less screw speed the water presented in the melt may not be distributed evenly throughout the mass and hence less capillary are generated which gives less crispness of extrudates. Crispness increases as the barrel temperature increases as it can be seen from Fig 4.45 and Fig 4.46 shows that as the die head temperature increases crispness of extrudates increases this mainly because high die head temperature is mainly responsible for harder outer surface which reduces the crispness of extrudates. Fig 4.47 shows the maximum value of crispness observed at C barrel temperature and rpm screw speed. The same trend had been observed in Fig which shows the maximum value of crispness observed at C DHT. The maximum value of crispness of extrudates was found to 12 whereas its minimum value was 4. 63

77 C r i s p n e s s C r i s p n e s s C r i s p n e s s C r i s p n e s s Design-Expert Software Factor Coding: Actual Crispness Design points above predicted value Design points below predicted value 12 4 X1 = A: Moisture Content X2 = B: Blend Ratio Actual Factors C: Screw Speed = 80 D: Barrel Temperature = 150 E: Die Head Temperature = Design-Expert Software Factor Coding: Actual Crispness Design points above predicted value Design points below predicted value 12 4 X1 = A: Moisture Content X2 = C: Screw Speed Actual Factors B: Blend Ratio = 15 D: Barrel Temperature = 150 E: Die Head Temperature = B: Blend Ratio A: Moisture Content (%, wb) C: Screw Speed (RPM) A: Moisture Content (%, wb) Fig.4.43 Effect of moisture content and blend ratio on crispness of extrudate Fig.4.44 Effect of moisture content and screw speed on crispness of extrudate Design-Expert Software Factor Coding: Actual Crispness Design points above predicted value Design points below predicted value 12 Design-Expert Software Factor Coding: Actual Crispness Design points above predicted value Design points below predicted value 12 4 X1 = A: Moisture Content X2 = D: Barrel Temperature Actual Factors B: Blend Ratio = 15 C: Screw Speed = 80 E: Die Head Temperature = X1 = A: Moisture Content X2 = E: Die Head Temperature Actual Factors B: Blend Ratio = 15 C: Screw Speed = 80 D: Barrel Temperature = D: Barrel Temperature (0C) A: Moisture Content (%, wb) E: Die Head Temperature (0C) A: Moisture Content (%, wb) Fig.4.45 Effect of moisture content and barrel temperature on crispness of extrudate Fig.4.46 Effect of moisture content and die head temperature on crispness of extrudate 64

78 C r i s p n e s s C r i s p n e s s Design-Expert Software Factor Coding: Actual Crispness Design points above predicted value Design points below predicted value 12 4 Design-Expert Software Factor Coding: Actual Crispness Design points above predicted value Design points below predicted value 12 X1 = C: Screw Speed X2 = D: Barrel Temperature Actual Factors A: Moisture Content = 12 B: Blend Ratio = 15 E: Die Head Temperature = X1 = D: Barrel Temperature X2 = E: Die Head Temperature Actual Factors A: Moisture Content = 12 B: Blend Ratio = 15 C: Screw Speed = D: Barrel Temperature (0C) C: Screw Speed (RPM) E: Die Head Temperature (0C) D: Barrel Temperature (0C) Fig Effect of screw speed and barrel temperature on crispness of extrudate Fig.4.48 Effect of barrel temperature and die head temperature on crispness of extrudate The R 2 had a value of 0.72 for the model. The results of analysis of variance (ANOVA) for the model 4.8 are presented in Appendix-A 8 and brief information is presented in table 4.8. Table 4.8 ANOVA for crispness of rice based extruded food product of lentil and oat Source DF SS MSS F P Regression Residual Total Regression coefficient and standard error of second order mathematical model are reported in Appendix-B 8. The significance of each term is also reported. The positive coefficient at linear level indicates that there was increase in response with increase in level of selected parameters and vice-versa. Negative quadratic terms indicated that the maximum value of the response was at the center point. 65

79 The standard deviation, coefficient of variance, mean and predicted residual error sum of square values, coefficient of determination and predicted R 2 and adequate precision are given in Appendix-C. 4.3 Optimization of process parameters Optimization of process parameters was carried out by setting the following goal of responses which are namely; moisture content of the feed, blend ratio, screw speed, barrel temperature and die head temperature and their effect on physico-chemical properties (mass flow rate, specific length, sectional expansion index, bulk density, water absorption index and protein) and textural properties (hardness and crispness) on the basis of minimum and maximum position using 45 days trial pack of Design Expert The optimum condition for maximum acceptable extrudates has been reported in table 4.9. Table 4.9 Optimized process parameters for highly acceptable extrudates Actual values of parameters Rank Moisture Screw Barrel Die head content Blend ratio speed temperature temperature (%, wb) (rpm) 0 C 0 C 1 st 10 70:10: nd 10 70:10: rd :20: The optimum process parameters ranked as first, second, third for most acceptable extrudates have been reported in Table Sensory evaluation 32 samples of rice based extrusion cooked food product prepared with 5 selected proportions of rice, lentil and oat and processed under selected operating conditions as discussed in section were evaluated by a 10 member s sensory panel constituted for the purpose. The average sensory scores awarded by the panel for different sensory attributes of rice based extrusion cooked food product of lentil and oat are presented in Appendix-E. 66

80 Color Color of extrudates prepared with rice, lentil and oat in 70:15:15 with 12% moisture content and extrudate at screw speed at 60 rpm, C barrel temperature, C die head temperature was liked very much by the sensory panel by awarding the sensory score of 8.0 on 9 point hedonic scale. However, for the samples having rice, lentil and oat in 70:15:15 with 12% moisture content with a change in die head temperature from C and screw speed from rpm the sensory scores for color reduces to 7.3. The samples prepared with rice, lentil, and oat 70:15:15 with 12% feed moisture content and extruded at screw speed 80 rpm, C barrel temperature and die head temperature C was slightly dislike by the sensory panel with a sensory scores of 4.1. Texture Texture of extrudates prepared with rice, lentil and oat in 70:20:10 with 10% moisture content and extrudate at screw speed at 90 rpm, C barrel temperature, C die head temperature was liked very much by the sensory panel by awarding the sensory score of 7.6 on 9 point hedonic scale. However, with a change in blend ratio from 70:20:10-70:10:20 and screw speed rpm the sensory scores for texture reduces to 7.4. The samples prepared with rice, lentil, and oat 70:15:15 with 12% feed moisture content and extruded at screw speed 80 rpm, C barrel temperature and C die head temperature was slightly dislike by the sensory panel with a sensory scores of 4.2. Flavor Flavor of extrudates prepared with rice, lentil and oat in 70:20:10 with 10% moisture content and extrudate at screw speed at 90 rpm, C barrel temperature, C die head temperature and blend ratio 70:10:20 with 14% moisture content and extrudate at screw speed at 70 rpm, C barrel temperature, C die head temperature was liked very much by the sensory panel by awarding the sensory score of 7.3 on 9 point hedonic scale. The samples prepared with rice, lentil, and oat 70:15:15 with 12% feed moisture content and extruded at screw speed 80 rpm, C barrel temperature and C die head temperature was slightly dislike by the sensory panel with a sensory scores of

81 Taste Taste of extrudates prepared with rice, lentil and oat in 70:10:20 with 10% moisture content and extrudate at screw speed at 90 rpm, C barrel temperature, C die head temperature was liked very much by the sensory panel by awarding the sensory score of 7.3 on 9 point hedonic scale. However, for the samples having rice, lentil and oat in 70:20:10 with 10% moisture content, screw speed 90 rpm, die head temperature C with a change in the barrel temperature from 160 to C sensory scores for taste is reduces to 7.1. The samples prepared with rice, lentil, and oat 70:15:15 with 12% feed moisture content and extruded at screw speed 80 rpm, C barrel temperature and C die head temperature was slightly dislike by the sensory panel with a sensory scores of 4.3. Overall acceptability The extrudates having rice lentil and oat in 70:20:10 ratio with 10% moisture content and extruded at barrel temperature C, die head temperature C, screw speed 90 rpm received the highest score (7.15) for the overall acceptability followed by the product with at 10% moisture content and rice: lentil: oat in 70:10:20 ratio and extruded at barrel temperature C, die head temperature C, screw speed 90 rpm. 68

82 SUMMARY, CONCLUSION AND SUGGESTION FOR FUTURE WORK 5.1 Summary The studies were conducted to optimize the process and operational parameters of rice based extrusion cooked food product of lentil and oat. All the samples contained 70% rice while lentil and oat varied from 5 to 25%. Response surface methodology was used for the optimization. The process and operational parameter on physico-chemical, textural and sensory characteristics of extrudates were studied. The obtained results are summarized as under: 5.2 Conclusion The major findings are summarized as follows: The mass flow rate of extrudates increases with increase in moisture content, screw speed and die head temperature. MFR decreases with increase in oat percentage in blend. Minimum value of mass flow rate was obtained at 10% m.c. of feed, 70 rpm screw speed, 70:10:20 blend ratio, C barrel temperature and C die head temperature. The specific length of extrudates increases with increase in moisture content, screw speed and increase in oat percentage in blend. The maximum value of specific length obtained at 70:20:10 blend ratio, 10%, wb moisture content, C die head temperature, C barrel temperature and 70 rpm screw speed. The sectional expansion index of extrudates decreases with an increment of oat flour in the blend and it increases with increase in the barrel temperature and die head temperature. The screw speed did not affect the sectional expansion index. The maximum value of sectional expansion index obtained at 70:20:10 blend ratio, 14%, wb moisture 69

83 content, C die head temperature, C barrel temperature and 70 rpm screw speed. The bulk density of extrudate increases with increase in proportion of oat in blend ratio, moisture content and barrel temperature and screw speed had no effect on bulk density of extrudates. The minimum value of bulk density observed at 70:10:20 blend ratio, 10%, wb moisture content, C die head temperature, C barrel temperature and 90 rpm screw speed. At lower screw speed rpm WAI decreases, beyond this level it starts to increase. Water absorption index also increases with increase in barrel temperature and die head temperature. The maximum value of water absorption index was obtained at 70:15:15 blend ratio, 12% moisture content, 60 rpm screw speed, C barrel temperature and C die head temperature. The protein content of extrudates increases with increase in lentil flour, moisture content and barrel temperature. Protein content of extrudates decreases as the screw speed increase. The maximum value of protein observed at 70:25:5 blend ratio, 12%, wb moisture content, C die head temperature, C barrel temperature and 80 rpm screw speed. Hardness of extrudates increases with increase in proportion of oat in blend, screw speed and die head temperature. The minimum value of hardness observed at 70:15:15 blend ratio, 12%, wb moisture content, C die head temperature, C barrel temperature and 60 rpm screw speed. Crispness increases with increase in proportion of oat in blend. Decrease in moisture content of feed increases the crispness of extrudates. As the die head temperature increases crispness of extrudates decrease. The maximum value of crispness observed at 70:10:20 blend ratio, 10%, wb moisture content, C die head temperature, C barrel temperature and 70 rpm screw speed. 70

84 The optimum process parameters characterized for most acceptable extrudates for different grades are as follows: Actual values of parameters Rank Moisture content (%, wb) Blend ratio Screw speed (rpm) Barrel temperature ( 0 C) Die head temperature ( 0 C) 1 st 10 70:10: nd 10 70:10: rd :20: It was observed that the maximum value of overall acceptability of sensory scores was 7.15 and it was extruded at 70:20:10 blend ratio, 10%, wb moisture content, C die head temperature, C barrel temperature and 90 rpm screw speed. 5.3 Suggestion for future work Further studies should be carried out on storage and packaging of extruded product. Cost analysis with different blend ratio need to be studied. After consumption of the product, after effects of the consumed product should be checked using Histro- Pathological test. 71

85 BIBLIOGRAPHY Agricultural Statistics at glance (2012), Ministry of Agricultural, Govt. of India. Altan A., Kathryer L., Mcc Arthy and M. Maskan, (2009). Extrusion cooking of barley flour and process parameter optimized by using response surface methodology. Journal of the Science of Food and Agriculture Vol. 88(9) Anastase H., Xiaolin D. and Fang T., (2006). Evaluation of rice flour modified by extrusion cooking. Journal of Cereal science Vol. 43(1): Andriana Lazou and Magdalini Krokida (2010), structural and textural characteristics of corn- lentil extruded snacks, Journal of Food Engineering, Vol. 100(3), pp Apruzzese, F, S.T. Balke and L.L Disady (2000). In line color and color and composition monitoring in the extrusion cooking process. Food Research International, 33(7): Banerjee, S., Ghose, A. and P. Chakraborty (2003). Characteristics of effect of temperature and moisture on lentil based extruded expanded product and process optimization. J. Food Sci. Technol. 40(6): Berrious, J.D.,J.Tang, B.G.Swansion, J. Pan (2004). Extrusion cooking of selected legume flours. Meeting abstract paper 1. AACC Symposium, September 2004, San Diego, Ca. Berrious, J.D., R.T. Patil, J. Tang, B.C. Swanson (2005). The use of Extrusion Cooking Technology for the Fabrication of Novel-Based value added products. Meeting Abstract No. 99f st Annual Meeting on July 2005, New Orleans, La. Berrious, J. De. J.P. Morales, M.Camara and M.C Sanchez-mata (2010). Carbohydrate composition of raw and extrudate pulse flour, Food Research International, 43(2), pp Bouvier, Jean-Marie, Le Corre and Anne Sophie (2008), Food Engineering Cooking Technology Technical Bulletin, XXX, Issue 1:1-10. Chang, Y.K. and A.A EI-Dash (2003). Effects of acid concentration and extrusion variable on some physical characteristics and energy requirement of cassava starch, Braz. J. Chemistry Engineering. 20 (2). Cocharan, W.G. and Cox, G.M. (1957). Some method for the study of response surface, experimental designs (2 nd ed.), John Wiley and Sons, Inc., New York, pp Ding Quing- BO, Paul Ainsworth, Andrew Plunkett, Gregory Tucker and Hayley Marson (2006). The effect of extrusion condition on the functional and physical properties of wheat-based expanded snacks. Journal of Food Engineering, Vol. 73(2):

86 Ficarella A., Milanese M. and Laforgia D. (2003). Numerical simulation of cereals extrusion and influence of design and process on the final quality. Tecnica- Molitoria (Italy), Jan (1) pp Ficarella A., Milanese M. and Laforgia D. (2006). Numerical study of the extrusion process in cereals production: Part 11: Analysis of Variance. J. Food Engineering Vol. 72 (2): George C. C. Chuang and A.I. Yeh (2003). Effect of screw profile on residence time distribution and starch gelatinization of rice flour during single screw extrusion cooking. Graduate (Institute of Food Science and Technology). National Taiwan University, Taiwan. Gonzalez R. J., Roberto L. T., Dardo M.D.G and Bonaldo A.G. (2006). Effect of extrusion conditions and structural characteristics on melt viscosity of starchy materials. Journal of Food Engineering, Vol. 74(1): Hagenimana Anastase, Xiaolin Ding and Tao Fang (2006). Evaluation of rice flour modified by extrusion cooking. Journal of Cereal Science Vol. 43(1): Hardacre, A.K., S.M. Clark, S. Riviere, J.A. Monro and A.J., Hawkins (2006). Some textural, sensory and nutritional properties of expanded snack food wafers made from corn, lentil and other ingredients. Journal of Texture Studies, 37 (1): Harper J.M. (1981). Extrusion of Foods, Boca Raton, FL, CRC Press, Inc. Jha, S. K. and Prasad S. (2003). Studies on extrusion cooking of rice and mung blend with salt and sugar. J. Food Sci. Technol. 40(3): Karacan, Seylam H.A., Olmez H. and Koskel H. (2001). Optimization of process conditions on the physical and functional properties of extruded defatted oat- corn mix. Annual Meeting of AACC. Kasprzak M., Rzedzickiz., Wirkijowska A., (2013). Effect of fiber-protein additional and process parameter on microstructure of corn extrudate. J. Cereal Sci., 58: Liu, Y., Hsich, F., Heymann, H., and Huff, H. E. (2000). Effect of process condition on the physical and sensory properties of extruded oat- corn puff. Journal of Food Science. 65: Lobato, L.P., Anibal, D., Lazaretti, M.M., Grossmann, M.V.E, (2011). Extruded puffed functional ingredient with oat bran and soy flour. LWT Food Science and Technology. 44(4): Meng X., Therinen D., Hansen M., Driedges D., (2010). Effect of extrusion conditions on system parameters and physical properties of a chickpea flourbased snack. Food Research International. 43: Ranganna S. (Eds) A handbook of analysis and quality for fruit and vegetable products. 21p. 73

87 Senol Banoglu, Paul Anisworth, Emir Ayse Ozer and Andrew plunkett (2006). Physical and sensory evaluation of a nutritionally balanced gluten-free extruded snack. Journal of Food Engineering. Vol. 1: Seth N.K and Mandhyan B.L (2008) Studies on preparation of ready-to eat snacks from rice-defatted soy flour and winter cherry powder blends by using extrusion cooking technology. Unpublished Ph.D. Thesis, submitted to JNKVV, Jabalpur, India. Singh, P. (2005). Extrusion Characteristics of soy-rice blend on the preparation of extruded snack food. M. Tech. Thesis, college of Agricultural Engineering, JNKVV, Jabalpur. Singh, D.S, S.K. Garg, Singh M and Goyal N. (2006) Optimization of the processing parameters of extrudate prepared from Bengal gram broken and sorghum blends by using Wenger X-5 extruder. Tang, J., J.D.J. Berons, R. Maokand and B.G. Swanson (1998). To develop healthy and convenient expanded snack food product from dry peas, lentils and garbanzo beans cutting modern extrusion cooking techniques extrusion cooking, URL: google.co.in. Ziobro, R., A. Nowotha, H. Gambus, A. Golachowski, K. Surowka and W. Praznik (2000). Susceptibility of starch from various biological sources on degradation due to extrusion process, zywnose, 7(2):

88 APPENDICES APPENDIX-A 1 ANOVA table for Quadratic model of Response Surface generated by CCRD for Mass Flow Rate of Rice based Extrusion Cooked Food Product of Lentil and Oat. Source Sum of Squares df Mean Sum of Square F value p-value Prob>F Model A-MC 8.067E E B-BR C-SS D-BT E-DT AB AC 4.225E E AD 6.400E E AE BC BD BE CD 2.250E E E CE DE A B C D E Residual Lack of Fit Pure Error Cor Total

89 APPENDIX-A 2 ANOVA table for Quadratic model of Response Surface generated by CCRD for Specific Length of Rice based Extrusion Cooked Food Product of Lentil and Oat. Source Sum of Squares df Mean Sum of Square F value p-value Prob>F Model A-MC B-BR 1.500E E E C-SS D-BT E-DT AB 9.506E E E AC AD AE BC BD 9.506E E E BE CD CE DE A B C D E Residual Lack of Fit Pure Error Cor Total

90 APPENDIX-A 3 ANOVA table for Quadratic model of Response Surface generated by CCRD for Sectional Expansion Index of Rice based Extrusion Cooked Food Product of Lentil and Oat. Source Sum of Squares df Mean Sum of Square F value p-value Prob>F Model A-MC B-BR C-SS D-BT E-DT AB AC AD AE BC BD BE CD CE DE 7.225E E A B C D E Residual Lack of Fit Pure Error Cor Total

91 APPENDIX-A 4 ANOVA table for Quadratic model of Response Surface generated by CCRD for Bulk Density of Rice based Extrusion Cooked Food Product of Lentil and Oat. Source Sum of Squares df Mean Sum of Square F value p-value Prob>F Model A-MC B-BR C-SS 7.704E E D-BT E-DT 3.504E E AB AC 1.056E E AD 3.906E E AE 5.625E E E BC BD 6.006E E BE 2.256E E CD 3.906E E CE 1.806E E DE 6.006E E A B E E E C E E D E Residual Lack of Fit Pure Error E-003 Cor Total

92 APPENDIX-A 5 ANOVA table for Quadratic model of Response Surface generated by CCRD for Water Absorption Index of Rice based Extrusion Cooked Food Product of Lentil and Oat. Source Sum of Squares df Mean Sum of Square F value p-value Prob>F Model A-MC B-BR C-SS D-BT E-DT AB AC AD AE BC BD BE CD CE DE E A B C D E Residual Lack of Fit Pure Error Cor Total

93 APPENDIX-A 6 ANOVA table for Quadratic model of Response Surface generated by CCRD for Protein of Rice based Extrusion Cooked Food Product of Lentil and Oat. Source Sum of Squares df Mean Sum of Square F value p-value Prob>F Model A-MC B-BR C-SS D-BT E-DT 2.817E E E AB AC AD AE BC BD BE CD 2.500E E E CE DE A B C D E E E E Residual Lack of Fit Pure Error Cor Total

94 APPENDIX-A 7 ANOVA table for Quadratic model of Response Surface generated by CCRD for Hardness of Rice based Extrusion Cooked Food Product of Lentil and Oat. Source Sum of Squares df Mean Sum of Square F value p-value Prob>F Model A-MC B-BR C-SS D-BT E-DT AB AC AD AE BC BD BE CD CE DE A B C D E E E E Residual Lack of Fit Pure Error Cor Total

95 APPENDIX-A 8 ANOVA table for Quadratic model of Response Surface generated by CCRD for Crispness of Rice based Extrusion Cooked Food Product of Lentil and Oat. Source Sum of Squares df Mean Sum of Square F value p-value Prob>F Model A-MC B-BR C-SS D-BT E-DT AB AC AD AE BC BD BE CD CE DE A B C D E Residual Lack of Fit Pure Error Cor Total

96 APPENDIX-B 1 Regression Coefficient of Second Order Mathematical Model for Mass Flow Rate of Rice based Extrusion Cooked Food Product of Lentil and Oat Factor Coefficient Estimate df Standard Error 95 %CI Low 95 %CI High Intercept A-MC B-BR C-SS D-BT E-DT AB AC AD AE BC BD BE 1.744E CD E CE DE A B C D E

97 APPENDIX-B 2 Regression Coefficient of Second Order Mathematical Model for Specific Length of Rice based Extrusion Cooked Food Product of Lentil and Oat Factor Coefficient Estimate df Standard Error 95 %CI Low 95 %CI High Intercept A-MC B-BR E C-SS D-BT E-DT AB AC AD AE BC BD BE CD CE DE A B C D E

98 APPENDIX-B 3 Regression Coefficient of Second Order Mathematical Model for Sectional Expansion Index of Rice based Extrusion Cooked Food Product of Lentil and Oat Factor Estimate Coefficient df Standard Error 95 % CI Low 95 %CI High Intercept A-MC E B-BR C-SS D-BT E-DT AB AC AD AE BC BD BE CD CE DE A B C D E

99 APPENDIX-B 4 Regression Coefficient of Second Order Mathematical Model for Bulk Density of Rice based Extrusion Cooked Food Product of Lentil and Oat Factor Coefficient Estimate df Standard Error 95 % CI Low 95 %CI High Intercept A-MC B-BR E C-SS D-BT E E-DT AB E AC 8.125E AD AE 1.875E BC E-003 BD BE CD CE DE A B E C E D E E

100 APPENDIX-B 5 Regression Coefficient of Second Order Mathematical Model for Water Absorption Index of Rice based Extrusion Cooked Food Product of Lentil and Oat Factor Coefficient Estimate df Standard Error 95 %CI Low 95 %CI High Intercept A-MC B-BR C-SS D-BT E-DT AB AC AD AE BC BD BE CD CE DE A B C D E

101 APPENDIX-B 6 Regression Coefficient of Second Order Mathematical Model for Protein of Rice based Extrusion Cooked Food Product of Lentil and Oat Factor Coefficient Estimate df Standard Error 95 %CI Low 95 %CI High Intercept A-MC B-BR C-SS D-BT E-DT AB AC AD AE BC BD BE CD CE DE A B C D E E

102 APPENDIX-B 7 Regression Coefficient of Second Order Mathematical Model for Hardness of Rice based Extrusion Cooked Food Product of Lentil and Oat Factor Coefficient Estimate df Standard Error 95 %CI Low 95 %CI High Intercept A-MC B-BR C-SS D-BT E-DT AB AC E AD AE BC BD BE CD CE DE A B C D E E

103 APPENDIX-B 8 Regression Coefficient of Second Order Mathematical Model for Crispness of Rice based Extrusion Cooked Food Product of Lentil and Oat Factor Coefficient Estimate df Standard Error 95 %CI Low 95 %CI High Intercept A-MC B-BR C-SS D-BT E-DT AB AC AD AE BC BD BE CD CE DE A B C D E

104 APPENDIX-C The standard error, mean, coefficient of variation, predicted residual error of sum of square (PRESS), coefficient of determination, adjusted and Predicted R- Squared and adequate precision values for developed models. Model No. St. deviation Mean c.v. % PRESS R 2 Adj. R 2 Pred. R 2 Adeq Precision E

105 Spread Sheet of different data of various responses (Parameters) under study for generation of response surfaced by CCRD. APPENDIX-D S. No M.C % B.R S.S rpm B.T 0 C DHT 0 C MFR g/sec S.L mm/g SEI B.D g/cm 3 WAI Protein % Hardness Kg Crispness :20: :10: :15: :10: :15: :15: :5: :10: :15: :20: :15: :10: :20: :15: :15: :20: :10: :20: :15: :10: :20: :15: :20: :25: :20: :15: :15: :10: :15: :10: :15: :15:

106 APPENDIX-E Sensory scores of development of rice based extrusion cooked food product of lentil and oat Run No. Blend ratio Color Sensory attributes Texture Flavor Taste Overall acceptability 1 70:20: :10: :15: :10: :15: :15: :5: :10: :15: :20: :15: :10: :20: :15: :15: :20: :10: :20: :15: :10: :20: :15: :20: :25: :20: :15: :15: :10: :15: :10: :15: :15:

107 CURRICULUM VITAE Miss. Swati Cyril was born on 01 January 1990 at Bettiah, Bihar. She passed her Higher Secondary School from Allahabad Agricultural Inter College (Allahabad) with first division in the year She was admitted to B. Tech. at Allahabad Agricultural Institute - Deemed University in She completed her degree with an aggregate of 7.5 in the year She joined Master of Technology two year Post Graduation degree course in the Department of Post Harvest Process and Food Engineering at College of Agricultural Engineering, JNKVV, Jabalpur in the year 2012.