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In this study, the formulation of fresh low-fat yogurt containing Balangu seed gum was modeled and optimized. In this study, first the independent variables of balangu seed gum (0 to 0.1%) and fat (0 to 2%) were modeled completely randomly in the form of a central composite design and each of the response variables (syneresis, pH, flavor, color, texture, and overall acceptance) were presented in the form of a polynomial regression model as a function of independent variables.The results showed that with increasing balangu seed gum, syneresis and pH of the samples decreased and the flavor, color, texture, and overall acceptance of the samples increased. Also, the increase in fat only decreased the syneresis of the samples. Then, the obtained models were optimized by multi objective particle swarming algorithm and numerical optimization algorithm in response surface methodology, so that minimum syneresis and maximum texture, flavor, color and general acceptance scores were obtained. The results of comparing the mean of three optimal points of the two algorithms showed that in general, in optimizing the formulation of low-fat yogurt containing balangu seed gum, the multi-objective particle swarming algorithm has a better performance than the numerical optimization algorithm in response surface method. The average optimal amount of balangu seed gum and fat in the multi-objective particle swarm algorithm was 0.85 and 1%, respectively, and in the method of optimization algorithm in response surface methodology, was 0.89 and 1.94%.

In this research, the effects of three variables, Konjac Glucomannan gum (KGM) (0-0.4% w/w), Xanthan gum (XG) (0-0.4% w/w) and Butter (3-9% w/w) on firmness, flavor, spread ability, color, total acceptance and pH of spreadable processed cheese were investigated. The central composite experimental design (CCD) was used and the data were analyzed using response surface methodology (RSM). Coefficients of determination (R2) of fitted regression models for firmness, flavour, spreadability, colour, total acceptance and pH were 0.834, 0.852, 0.959, 0.886, 0.813 and 0.871, respectively. The Results of analysis of variance (ANOVA) table showed that the lack of fit was not significant for all response surface models at 95%. Therefore, the models for all response variables were highly adequate. The results showed that the effects of adding hydrocolloids and butter content were significant for sensory and chemical specifications. The optimum processing conditions for producing spreadable processed cheese with the best overall values for response variables were 0.066% (w/w) KGM, 0.024% (w/w) Xanthan gum and 8.53% (w/w) butter. The response values for such product were: firmness: 2.88, flavour: 3.99, spreadability: 4.88, colour: 4.11, total acceptance: 4.03 and pH: 5.51 and the desirability value was 93.06%.