Optimisation of value added Gulab Jamun by utilising dairy by-products #

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M. 678 Optimisation of Gulab Jamun by utilising dairy by-products _____________________________________ or a hard core.According to Selvanayagan (1983), the ideal frying temperature -time of Gulab Jamun was 140°C for 7 minutes.It has a shelf life of five to seven days in sugar syrup at room temperature (Pal, 2000).As per BIS standards (1986), the product should have a moisture content of not more than 30 per cent and milk fat content of not less than eight per cent.
Incorporating dairy by-products in traditional dairy sweets to increase the functional attributes of the existing product is an ideal way of by-product utilisation, especially since traditional dairy products contribute to 50 per cent share of Indian dairy product market (Vaswani, 2002).Whey, ghee residue, buttermilk, etc. are some of the commercially important dairy by-products.The solids not fat (SNF) part of cream, coagulated out during ghee preparation is known as ghee residue (Janghu et al., 2014).It is rich in milk proteins, phospholipids and nitrogenous compounds which contribute towards its antioxidant properties (Santha and Narayanan, 1979).Sweet cream buttermilk (SCBM) is a by-product obtained during the butter-manufacturing.It has significant amount of milk fat globule membrane (MFGM) material, which remains from the butter-making process (Walstra et al., 2005).Due to higher content of proteins, glycoproteins and phospholipids in MFGM, buttermilk has excellent emulsifying properties (Elling et al., 1996;Corredig and Dalgleish, 1997;Wong and Kitts, 2003) and good water holding capacity (Raval and Mistry, 1999;Turcot et al., 2001).These by-products are of high nutritive value and when discharged carelessly in to water bodies these may release some harmful gases which can change the taste, odour, colour and turbidity of water bodies (Srivastava, 2020).
In the current study, efforts were taken to develop a standardised process for the preparation of Gulab Jamun with addition of other health beneficial dairy by-products such as ghee residue and sweet cream buttermilk powder.

Materials and methods
The study was carried out in the Dairy Technology Department, Verghese Kurien Institute of Dairy and Food Technology, Mannuthy.

Raw materials
Buffalo milk khoa, base material for Gulab Jamun, was procured from Kerala Veterinary and Animal Sciences University Dairy plant.Ghee residue and whey were also collected from Kerala Veterinary and Animal Sciences University Dairy plant.Sweet cream buttermilk powder was procured from Vidhya dairy, Anand, Gujarat.Baking powder and maida used for the preparation were procured from the local market.

Preliminary trials
Ghee residue, maida and sweet cream buttermilk powder were added to khoa at levels ranging from 5 to 20% (Ranu et al. 2012), 5 to 30% (Yawale, 2012) and 0 to 50% (Chaudhari, 2017), respectively.Baking powder was added at different levels (0.5% by weight of the product) to prepare the dough.Minimum and maximum levels of the incorporation were selected by verifying the levels of each ingredient within the range specified (by varying the levels of one ingredient keeping the other ingredients constant in the dough).The selection of the levels of addition of all the ingredients were based on sensory attributes and the scores obtained were then statistically analysed by Kruskal Wallis test.

Statistical analysis
Gulab Jamun was optimised by Central Composite Rotatable Design (CCRD) of RSM software.Based on the different treatment combinations as suggested in RSM software, samples were prepared and sensory evaluation was conducted by an expert panel of 5 judges using 9-point hedonic scale.The sensory scores thus obtained were fed to the RSM software as responses to optimise the level of addition of ingredients.The optimised levels of all the ingredients and magnitude of the responses were then validated.

Manufacturing of Gulab Jamun
Gulab Jamun was prepared as per the procedure standardised by Joshi et al. (2009) with minor modifications (Fig. 1).

Results and discussion
Gulab Jamun was prepared and optimised using RSM, in two stages i.e., preliminary trial and optimisation trials.A three factor CCRD was adopted employing a quadratic model.The levels of three factors were optimised by maximization of the sensory responses through fitting of quadratic models by numerical optimisation command of Design Expert software.

Selected levels after preliminary trials
Preliminary trials were conducted to select the minimum and maximum level of ingredients that can be incorporated in to the product.The selection of the levels was based on sensory responses which were further analyzed by Kruskal Wallis test.The addition of ghee residue was done at the levels between 5 to 20% and the best textural properties were obtained with the levels between 10% and 20%.Higher levels of addition of ghee residue caused a pronounced sour flavour in the product which was also reported by Sojan et al. (2021) with increased rate of addition of ghee residue in cakes and muffins.The range of addition of maida was restricted to 8 to 16% as higher rates of addition were found to impart a gummy texture to the final product.This is in agreement with the observations made by Joshi et al. (2009) who studied the influence of different ingredients, including maida, in Gulab Jamun.Level of sweet cream buttermilk powder was fixed at 12.5 to 37.5% beyond which there was significant lowering of textural properties.Similar effects were reported by Zhao et al. (2020) in his work in yoghurt.

Optimisation of quantity of ghee residue, maida and sweet cream buttermilk powder by response surface methodology
From the preliminary trials, the minimum and maximum levels of ghee residue, maida and SCBMP were selected as 10% and 20%, 8% and 20%, 12.5% and 37.5%, respectively.When these levels were given to CCRD of RSM, the output showing upper and lower limits of the ingredients (Table 1) and the CCRD of three factors containing 20 runs along with their responses of the sensory attributes are displayed in Table 2.
The design matrix of the three factors in CCRD of RSM along with the sensory scores corresponding to the parameters (flavour, body and texture, colour and appearance, sweetness and overall acceptability) is presented in Table 2.In the recommended quadratic model, F values for all the characteristics were greater than tabled F-value (p<0.01)showing that the developed model is significant.The average flavour scores of Gulab Jamun ranged from 6.50 to 9.00 (Table 2).The determination coefficient (R 2 ) was 0.94 indicating that 94% of the variability in the response could be explained by the design.The adequate precision of 11.24 firmly suggests the adoption of this response viz.Flavour to guide this design.Since the lack of fit test resulted in a F value that is non-significant, it was evident that the model is authentic enough for forecasting the flavour of Gulab Jamun.The p-value of the flavour model showed that the impact of ghee residue, maida and SCBMP was significant (p<0.05) on the flavour score of Gulab Jamun.The SCMBP positively affected the flavour score at quadratic levels whereas both ghee residue and maida negatively affected the flavour scores (Fig 2a,2b and 2c).From these figures, it can be inferred that the selected levels of ghee residue and maida did not contribute to any off flavour in the product yet they had reduced the flavour scores.The interaction effect of ghee residue and SCBMP had a negative effect and was found to be non-significant.In conformity to the present findings, El-Kholy et al. (2014) had also reported that addition of sweet buttermilk to ice cream had improved its flavour intensity.Similarly, improved flavor intensity by replacing the 10 to 40 % of refined wheat flour with ghee residue in cake and muffin was reported by Ranjan et al. (2020).

Colour and appearance
The following response surface equation was generated to forecast the variation in colour and appearance with various amounts The sensory scores for colour as well as appearance of Gulab Jamun ranged from 6.50 to 8.83 (Table 2).The determination coefficient (R 2 ) of 0.94 with required precision of 13.10 firmly suggests the adoption of this response viz.colour and appearance to guide this design.Lack of fit test resulted in a non-significant F value hence the model is accurate enough for forecasting the colour and appearance of Gulab Jamun.The p-value of the colour and appearance model showed that the impact of ghee residue, maida and SCBMP was significant (p<0.05) on the colour and appearance score of Gulab Jamun.Only SCBMP positively affected the colour and appearance score at quadratic levels (Fig 3a,3b and 3c).All the three factors were significant (p>0.05) at squared level.The interaction effect of ghee residue and SCBMP had a negative impact on the colour score whereas effect of interaction of ghee residue and maida showed a positive effect.Borawake and Bhosale (1996) also reported that an increase in the amount of GR decreased the colour and appearance scores in nankhatai type cookies and sponge cakes.

Body and texture
The acceptability of any food is affected by its body and texture.The given The sensory scores for body and texture of Gulab Jamun ranged from 6.93 to 8.68 (Table 2).The coefficient of determination (R 2 ) of 0.92 with adequate precision of 10.05 firmly suggests the adoption of this response viz.body and texture to guide this design.Since the lack of fit test resulted in a non-significant F value, it was evident that the model is accurate enough for forecasting the body and texture of Gulab Jamun.The p-value of the body and texture model showed that ghee residue and SCBMP had significant (p<0.05)effect, whereas maida had a non-significant (p>0.05)effect on the body and texture score of Gulab Jamun.The SCBMP positively affected the body and texture score at quadratic levels whereas all other ingredients showed a negative effect (Fig 4a ,  4b and 4c).The interaction of ghee residue and SCBMP had a significant negative effect on the score.Interactions of ghee residue and maida, and maida and SCBMP showed a significant positive effect.Improved textural properties in cheese and ice cream, on addition of buttermilk were reported by Hickey et al. (2018) and Shibu et al. (2000), respectively.Dua et al. (2018) found that increasing the level of addition of GR in burfi had a negative effect on its body and texture.The sensory scores for sweetness of Gulab Jamun ranged from 6.87 to 9 (Table 2).The coefficient of determination (R 2 ) of 0.93 with adequate precision of 9.28 firmly suggests the use of this response viz.sweetness to guide the design.Since the lack of fit test resulted in a non-significant F value, it was evident that the model is accurate enough for forecasting the sweetness of Gulab Jamun.The p-value of the sweetness model showed that all the factors had a non-significant (p>0.05)effect on the sweetness score of Gulab Jamun.All the factors negatively affected the sweetness score at quadratic levels (Fig 5a , 5b and 5c).The interaction effect of maida and SCBMP had a negative effect on the scores.Similar inferences on sweetness were reported by Kumari (2013) on khoa based sweets prepared with low calorie sweetener.

Overall acceptability
The acceptability of any food is affected by its overall acceptability.The sensory scores for overall acceptability of Gulab Jamun ranged from 6.25 to 9 (Table 2).The coefficient of determination (R 2 ) of 0.92 with adequate precision of 10.14 firmly suggests the use of this response viz.overall acceptability to guide the design.Since the lack of fit test resulted in a non-significant F value, it was evident that the model is accurate enough for forecasting the overall acceptability of Gulab Jamun.The p-value of the overall acceptability model showed that maida and SCBMP had non-significant (p>0.05)effect, whereas ghee residue had a significant (p<0.05)effect on the overall acceptability score of Gulab Jamun.The ghee residue and maida negatively affected the overall acceptability score at quadratic levels (Fig 6a,6b and 6c).The interaction of ghee residue and maida had a significant negative effect on the overall acceptability score whereas the effect of interactions of maida and SCBMP 684 Optimisation of Gulab Jamun by utilising dairy by-products _____________________________________ showed a non-significant positive effect.Similar inferences on overall acceptability scores were reported by Madenci and Bilgiçli (2014) on bread with 8% buttermilk supplementation.
The levels of three factors were optimised by maximizing the sensory responses through fitting of quadratic models by numerical optimisation.Table 4 presents the suggested solutions for the preparation of Gulab Jamun.The solution that got a desirability of 0.991 was selected.The optimum values selected were at 12.98% for ghee residue, 10.21% for maida and 25.45% for SCBMP.

Verification of optimum solution
The Gulab Jamun prepared at desired optimum level of ingredients were statistically analyzed for sensory attributes (Table 5) and it is evident that the observed values were not significantly (p<0.05)different from the predicted values.conclusion Gulab Jamun with optimum levels of maida and dairy by-products like ghee residue and sweet cream buttermilk powder was identified by RSM.The models developed were found highly suitable for predicting the ingredient formulation by assuring an optimal sensory score of Gulab Jamun.Out of suggested formulations, the formulation no. 1 had maximum desirability index of 0.991 and all the responses fitted well into the quadratic equation with R 2 > 0.90, hence, selected for further analysis.Therefore, the formulation with ghee residue (12.98%), maida (10.21%) and SCBMP (25.451%) was considered most suitable treatment combination for manufacturing of by-product based Gulab Jamun.This could also guide to product development leading to an effective way for utilising dairy by-products which are otherwise considered as dairy waste.

acknowledgment
We place on record our gratitude to Kerala Veterinary and Animal Sciences University for the financial assistance provided during for the study.

table 1 .
The coded and actual levels of ghee residue (GR), maida and sweet cream buttermilk powder (SCBMP) 680 Optimisation of Gulab Jamun by utilising dairy by-products _____________________________________

table 2 .
The Central Composite Rotatable Design (CCRD) for three factors and their sensory responses

table 5 .
Verification of the optimum formulation