ISSN Original Article FORMULATION OF FOXTAIL MILLET INCORPORATED DOSA USING RESPONSE SURFACE METHODOLOGY

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1 Available online at International Journal of Agricultural and Food Science Universal Research Publications. All rights reserved ISSN Original Article FORMULATION OF FOXTAIL MILLET INCORPORATED DOSA USING RESPONSE SURFACE METHODOLOGY T.Sanggethalakshmi 1 and P.Nazni 2* 1 Ph.D Scholar, Department of Foods and Nutrition, Mother Teresa University, Kodaikanal, 2 Departmetn of Clinical Nutrition and Dietetics, Periyar University, Salem-11, Tamilnadu, India, * Corresponding Author: naznip@gamil.com Received 02 June 2017; accepted 03 July 2017 Abstract Introduction: The foxtail millet is the fastest growing of all millet produces a crop in six weeks. Dosa is a thin, crispy textured and desirably sour flavour fermented product. The present work was focused on the sensory qualities of foxtail millet dosa produced by foxtail millet: dhal ratio and fermentation time at various combinations (13 combinations). Objective: Response surface methodology (RSM) was used to optimize the ingredients like: millet: dhal (3:1 to 3:1.25) and fermentation time (12 to 12.5) to formulate 13 variations of dosa. Methods: Response Surface Methodology (RSM) was used to investigate the effects of foxtail millet: dhal ratio (A) and fermentation time (B) on the dosa. Data obtained from RSM on foxtail millet dosa developed were subjected to the analysis of variance (ANOVA) and analyzed using a second order polynomial equation. Result: Results of this study revealed that, maximum desirable score that can be achieved with the desirable value of appearance was 6.73, colour 7.75, flavour 6.37, texture 6.94, taste 6.69 and overall acceptability scores On the basis of these calculations good millet based dosa could be made when the millet to black gram dhal ratio is 3:1.60 (w/w), fermented time for 13.17h as the best proportion of these components. This sample was considered a best optimizing source for sensory attributes. Conclusion: Moreover, RSM was shown to be an adequate approach for modeling the organoleptic parameters and the degree of liking of good fermented foxtail millet dosa. Millet based dosa formulations showed a remarkable level glycemic response and best substitute instead of rice based foods. Developing such value-added products from coarse cereals often dubbed as poor man s crops could bring them to the regular dietary regime of consumers offering them nutritional benefits Universal Research Publications. All rights reserved Key words: Foxtail millet, response surface methodology, dhal, fermentation time. INTRODUCTION The production of fermented foods is one of the oldest food processing technologies known to man. The diversity of the population of India has given rise to a large number of traditional fermented foods which have been extensively reviewed (Soni and Sandhu 1990, Achaya 1994). Cereals are used world-wide as staple food, they are considered to be one of the most important sources of dietary proteins, carbohydrates, vitamins, minerals and fiber for people all over the India. Most commonly cereals are utilized in combination with legumes to improve the overall protein quality of the fermented product. This combination can be replaced by the small seeded grains that are known as millets in breakfast food, convenience foods, and snack foods. The total production of millet grains of world was metric tons (FAO 2012). Millets termed as nutria-cereals can be exploited for the use as functional foods (Chandrasekhar, 2003). Millet grains are not only nutritionally comparable but also superior to major cereals with respect to protein, energy, vitamins and minerals. Besides, they are a rich source of dietary fiber, phytochemicals and micronutrients. They have a higher proportion of non-starchy polysaccharides and dietary fiber. They release sugars very slowly and thus have a low glycemic index (GI) (3), and hence can be used in therapeutic diet. Among the millets, foxtail millet is a good source of protein (12.3 g/100g) and dietary fiber (14 g/100g). The carbohydrate content is low (60.9 g/100g). Besides, it is rich in minerals (3 g/100g) and phytochemicals. Foxtail millet is a good source of ß carotene ( µg/100g). Black gram dhal (Phaseolusmungo) commonly known as urad is rich in vitamins, minerals and devoid of cholesterol. It is store house of calcium (154 mg), potassium, iron (4 mg) and 23

2 protein (24 g) (Hulse et.al. 1980). Dosa is a thin, crispy textured and desirably sour flavour fermented product (Sekar and Mariappan, 2007). The fermentation process causes enrichment and improvement of food through flavour, aroma and change in texture, preservation by producing organic acids, nutritional enrichment, reduction of endogenous toxins and reduction in the duration of cooking and thereby fuel requirement. Normally, dosa is prepared from wet ground batter of rice and black gram (Sulochana and Bakiyalakshmi, 2011). In India, dhal (dehulled split pulse) milling is the third largest food grain processing industry after rice and wheat milling. The average yield of dhal in the commercial mills is about 75 % and the rest material (about 25 %) is obtained in the form of various types of by-products (Kurien and Parpia, 1968). These by-products comprises of seed coat/husk, powder, large and small brokens, shriveled and underprocessed grains. Presently, these are disposed of only as feed grade material, fetching low remunerative prices (Ramakrishnaiah, et al., 2004). Most of these by-products are rich in protein, calcium, iron, zinc and fibre, so these can be utilized for making health foods for different age groups (Yadav, et al., 2007). Response surface methodology is a collection of statistical and mathematical techniques useful for developing, improving, optimizing processes and achieving the optimum conditions for desirable responses with a limited number of planned experiments, it also provides a mathematical model, which describes the relationships between the independent and dependent variables (Myers and Montgomery 1995). MATERIALS AND METHODS Ingredients The ingredients used for this investigation such as foxtail millet, urad dhal, fenugreek, and salt were purchased from local departmental store in Salem, All the ingredients were selected by considering its availability, nutritional and health benefits. Basic formulation of dosa Thirteen variations of dosa with varying proposition of ingredients and fermentation time were Ingredients 1. Foxtail Millet -100g 2. Black gram dhal - 25g 3. Fenugreek -5g 4. Salt - To taste 5. Water -As requirement Black gram Methods of preparation optimized (Foxtail millet: Black gram dhal and temperature were mixed at ratios of 3:1; 12, 3:0.75; 13 and 3:1.5; 14) to the entire proposition. The composition is given in the Table 1. Optimization of the foxtail millet dosa- experimental design for the process of optimization The Central Composite Rotatable Design (CCRD) was used for selecting the level of parameters in the experiments. RSM was performed using the Design-Expert software program version 7.0. The coded and un-coded independent variables used in the RSM design are listed in Table 1 and Table 2. The levels of the independent parameters were based on preliminary experimental results. The experimental design was based on the CCD as shown below. Each design point consists of the replicates. For the statistical analysis the numerical levels are standardized to -2, -1, 0 and +1, +2. The experiments were carried out in randomized order (Gacula& Singh, 1984). The relationship between standardized variables values is given as follows Response surface methodology Response Surface Methodology (RSM) was applied to optimize the levels of two variables Foxtail millet: Black gram dhal (X1) and Temperature (X2). Central Composite Rotatable Design (CCRD) was used in selecting the levels of the four variables. The variables were standardized on the basis of their effect of responses i.e., product weight, no of pores in a square inch in idly, carbohydrate, protein, fat, fiber and overall acceptability. The standardized variables (Xi) were obtained using the following second order polynomial equation. The model proposed of each response of Y was Where: β0 constant, βi linear coefficient, βii quadratic coefficient, βij cross product coefficient, Xi, Xj levels of the independent variables, 3 Number of the factors tested. The model permitted evaluation of quadratic terms of the independent variables on the dependent variable. Foxtail millet Fenugreek Wash Wash Fine grind in mortar Fine grind in mortar Combine slurries into thick batter mix well Ferment it overnight for 10 to 14 hours 24

3 Statistical analysis Data was analyzed by the least-squares method and response surfaces were generated using the Design Expert software (Stat Ease Inc., Minneapolis, MN). Analysis of variance (ANOVA) was used to test the significance of each variable (p 0.05) and to verify the adequacy of the model. Interaction effects were determined using LS means (p 0.05). All experiments were carried out in triplicate. RESULTS AND DISCUSSION The experimental findings obtained from the present study have been discussed in following heads: appearance on attribute of developed dosa The magnitude of P and F values in table 3 indicates the maximum positive contribution of foxtail millet, dhal and fermentation time on appearance attribute of developed dosa. The Model F-value of 0.12 implies the model is not significant relative to the noise. There is a % chance that F-value this large could occur due to noise. Values of "Prob > F" less than 0.05 indicate model terms are significant. In this case there are no significant model terms. The quadratic terms of, millet: dhal and fermentation ratio have showed positive effect on appearance. Further the integrations of AB, A 2 and B 2 have positive effect on appearance. The magnitude of P-values indicates that is not significant. The quadratic terms of all the process variables have not significant effect on appearance. Whereas the interaction terms of all the process variables have not significant effect on appearance. The "Lack of Fit F-value" of 2.34 implies the Lack of Fit is not significant relative to the pure error. There is a 21.50% chance that a "Lack of Fit F-value" this large could occur due to noise. Non-significant lack of fit is good. A negative "Pred R-Squared" implies that the overall mean is a better predictor of appearance response of the current model (3D response surface plot is present in figure1). Coded: Appearance = * A+0.18* B+1.10* AB+0.025*A^ *B^ Equation 1 Uncoded: Appearance = *Millet and dhal ratio-1.455*time *Millet and dhal ratio*time+0.27*millet and dhal ratio^ *time^ Equation 2 colour on attribute of developed dosa The magnitude of P and F values in table 4 indicates the maximum positive contribution of foxtail Diluting batter with little water to spread evenly on pan Heating pan, spreading batter in a circle using the back of the ladle Greasing batter with oil on the edges and the middle Cooking each side till the crust crackles and becomes golden Ready to serve Flow chart for dosa preparation millet, dhal and fermentation time on appearance attribute of developed dosa. The Model F-value of 0.60 implies the model is not significant relative to the noise. There is a % chance that an F-value this large could occur due to noise. Values of "Prob > F" less than indicate indicate the model terms were not significant. The quadratic terms of, millet: dhal and fermentation ratio have showed positive effect on colour. Further the integrations of AB, A 2 and B 2 have positive effect on colour. The magnitude of P-values indicates that is not significant. The quadratic terms of all the process variables have not significant effect on colour. Whereas the interaction terms of all the process variables have not significant effect on colour. The "Lack of Fit F-value" of 0.76 implies the Lack of Fit is not significant relative to the pure error. There is a 57.42% chance that a "Lack of Fit F-value" this large could occur due to noise. Non-significant lack of fit is good for the model to fit. A negative "Pred R-Squared" implies that the overall mean is a better predictor of colour response than the current model."adeq Precision" measures the signal to noise ratio (3D response surface plot is present in figure 2). Coded: Colour = * A-0.052* B+0.75* AB+0.15* A^2+0.40* B^ Equation 3 Uncoded: Colour= * Millet and dhal ratio * Time+4.16* Millet and dhal ratio * Time+1.66* Millet and dhal ratio^2+1.11* Time^ Equation 4 flavour attribute of developed dosa The Model F-value of 0.09 implies the model is not significant relative to the noise. There is a % chance that an F-value this large could occur due to noise (table -5). Values of "Prob > F" less than indicate indicate the model terms are not significant. If there are many insignificant model terms (not counting those required to support hierarchy), model reduction may improve your model. The "Lack of Fit F-value" of 0.62 implies the Lack of Fit is not significant relative to the pure error. There is a 63.56% chance that a "Lack of Fit F-value" this large could occur due to noise. Non-significant lack of fit is good and the model to fit. A negative "Pred R- Squared" implies that the overall mean is a better predictor of flavour response than the current model. "Adeq 25

4 Precision" measures the signal to noise ratio. A ratio of 0.90 indicates an inadequate signal and we should not use this model to navigate the design space (3D response surface plot is present in figure 3). Coded: Flavour = * A+0.13* B-0.25* AB+0.11* A^2+0.11* B^ Equation 5 Uncoded: Flavour = * Millet and dhal ratio * Time-1.38* Millet and dhal ratio * Time+1.25* Millet and dhal ratio^2+0.31* Time^ Equation 6 texture attribute of developed dosa The Model F-value of 3.88 implies there is a 5.28% chance that an F-value this large could occur due to noise (table -6). Values of "Prob > F" less than indicate model terms are significant. In this case A^2 is a significant model term. Values greater than indicate the model terms are not significant. If there are many insignificant model terms (not counting those required to support hierarchy), model reduction may improve your model. The "Lack of Fit F-value" of 0.32 implies the Lack of Fit is not significant relative to the pure error. There is an 81.31% chance that a "Lack of Fit F-value" this large could occur due to noise. Non-significant lack of fit is good and the model to fit (3D response surface plot is present in figure 4). Coded: Texture = * A-0.43* B+3.040* AB+1.04* A^2+0.29* B^ Equation 7 Uncoded: Texture= * Millet and dhal ratio * Time+3.137* Millet and dhal ratio * Time * Millet and dhal ratio^2+0.79* Time^ Equation 8 taste attribute of developed dosa The Model (table -7) F-value of 2.00 implies the model was not significant relative to the noise. There is a % chance that an F-value this large could occur due to noise. Values of "Prob > F" less than indicate indicate the model terms are not significant. If there are many insignificant model terms (not counting those required to support hierarchy), model reduction may improve your model. The "Lack of Fit F-value" of 1.17 implies the Lack of Fit is not significant relative to the pure error. There is a 42.47% chance that a "Lack of Fit F-value" this large could occur due to noise. Non-significant lack of fit is good and the model to fit. A negative "Pred R- Squared" implies that the overall mean is a better predictor of taste response than the current model. "Adeq Precision" measures the signal to noise ratio. A ratio greater than 4 is desirable. Taste ratio of indicates an adequate signal. This model can be used to navigate the design space (3D response surface plot is present in figure 5). Coded: Taste= * A-0.30* B-0.25* AB+0.16* A^2+0.66* B^ Equation 9 Uncoded: Taste= * Millet and dhal ratio * Time-1.38* Millet and dhal ratio * Time+1.805* Millet and dhal ratio^2+1.84* Time^ Equation 10 OVA attribute of developed dosa The Model (table -8) F-value of 1.78 implies the model was not significant relative to the noise. There is a % chance that an F-value this large could occur due to noise. Values of "Prob > F" less than 0.05 indicate model terms are significant. In this case A^2 was a significant model term. Values greater than 0.10 indicate the model terms were not significant. If there are many insignificant model terms (not counting those required to support hierarchy), model reduction may improve OVA model. The "Lack of Fit F-value" of 1.33 implies the Lack of Fit was not significant relative to the pure error. There was a 38.13% chance that a "Lack of Fit F-value" this large could occur due to noise. Non-significant lack of fit was good and the model to fit. A negative "Pred R-Squared" implies that the overall mean was a better predictor of OVA response than the current model. "Adeq Precision" measures the signal to noise ratio. A ratio of 2.92 indicates an inadequate signal and we should not use this model to navigate the design space (3D response surface plot is present in figure 6). Coded: OVA= * A-3.614* B+0.50* AB+0.75* A^2+0.25* B^ Equation 11 Uncoded: OVA = * Millet and dhal ratio * Time * Millet and dhal ratio * Time * Millet and dhal ratio^ * Time^2 ----Equation 12 Predicted optimization of process parameters by desirability approach Predicted optimization was performed for sensory attributes parameters like appearance, color, flavour, texture, taste and overall quality by imposing desirability constraints. Table-9 shows the constraints imposed for dosa with better sensory attributes with the desirable value for both independent and dependant variables. The maximum desirable score that can be achieved with the desirable value of appearance was 6.73, colour 7.75, flavour 6.37, texture 6.94, taste 6.69 and overall acceptability scores On the basis of these calculations good millet based dosa could be made when the millet to black gram dhal ratio is 3:1.60 (w/w), fermented time for 13.17h. The optimum results were validated by performing the experiment at the optimized ratio and fermentation time by comparing the observed and the predicted values. The observed and predicted values were not significantly different (P>0.05) which confirmed the optimization results and proved the predicted model to be correct. CONCLUSION RSM was successfully optimized the ingredient formulation and processing parameter of developed millet dosa. The optimum results indicated that the optimum ratio millet: dhal was 3:1.60 with fermentation time was 13.17hour. In conclusion, the present studies showed that millet based dosa developed by incorporating foxtail millet were acceptable and they markedly reduce the glycemic responses. Similar kinds of result could be expected in rice based other foods after substituting with foxtail millet. Developing such value-added products from coarse cereals often dubbed as poor man s crops could bring them to the 26

5 regular dietary regime of consumers offering them nutritional benefits. This could also lead to product development leading to diverse uses of millet. REFERENCES 1. Achaya KT. Indian Foods: A traditional companion Oxford Univ. Press, Oxford, 1994, Chandrasekhar U. Appropriate technology for utilization of nutrient dense local plant foods. Proc IX Asian Cong Nutr 2003; FAO. Economic and Social Department: The Statistical Division. Statistics Division posted on September, 2012, Hulse JH, Laing EM, Peason OE. Sorghum and millets: Their composition and nutritive Value. Academic Press, London, 1980; Kurienn, P.P. and Papria, H.A.B. Pulse Milling in India. Journal of Food Science and Technology ; Meyers RH, Montgomery DC. Response surface methodology: Process and product optimization using designed experiments, John Wiley and sons, Inc. New York, 1995, Ramakrishnaiah, N., Pratape, V.M., Sashikala, V.B. and Narasimaha, H.V. Value-Addition to by-products from Dhal Milling Industry in India. Journal of Food Science and Technology ; Sekar, S. and Mariappan, S. Usage of Traditional Fermented Products by Indian Rural Folks and IPR. Indian Journal of Traditional Knowledge ; Soni SK, Sandhu DK. Indian fermented foods: microbiological and biochemical aspects. Indian J Microbiol. 1990; 30: Sulochana, S. and Bakiyalakshmi, S.V. Effect of Nutraceuticals Dosa on Antimicrobial Activity. International Journal of Environmental Sciences ; Veena B, Chimmad BV, Naik RK, Shantakumar G. Physico-chemical and nutritional studies in barnyard millet. Karnataka J. Agril. Sci. 2005; 18(1): Yadav, S.S., Redde, R.J., Chen, W. and Sharma, B. Chickpea Breeding and Management. Technology Engineering ; Source of support: Nil; Conflict of interest: None declared 27