Tandoori Chicken: Processing Optimized by Response Surface Methodology

Abstract

Processing of Tandoori Chicken, a marinated and oven baked traditional product, was optimized using response surface methodology. The central composite rotatable design used involved three factors: marination duration, oven temperature, and baking time, each factor had five levels that encompassed a suflciently wide range across the samples. Quadractic models were fitted to the measured responses of Warner-Bratzler shear, internal temperature, sensory properties, and microbiological parameters. Values for the three factors were optimized by minimizing W. B. Shear and maximizing sensory quality by using the flexible polyhedron search method. The optimal process was to bake chicken for 20 min at 280C after marinating for 120 min. The fitted models for each of the responses were calculated and graphed. Microbiological data provided extra strength in supporting the optimum conditions and in allaying apprehension about the safety of marinated products under ambient conditions.

Quality changes during the storage of dehydrated chicken kebab mix

“…However, a significant lack of fit for colour and firmness indicates that there is still some statistically significant variability left that cannot be accounted for by the factors and their interactions. Nair et al. (1995) had reported the usefulness of response surface methodology for the optimisation of processing conditions and for predicting the sensory quality of tandoori chicken as affected by processing variables.…”

“…However, a significant lack of fit for colour and firmness indicates that there is still some statistically significant variability left that cannot be accounted for by the factors and their interactions. Nair et al (1995) had reported the usefulness of response surface methodology for the optimisation of processing conditions and for predicting the sensory quality of tandoori chicken as affected by processing variables. In the present study, the narrow range of residuals/deviations (Table 5) between observed and predicted values for texture (±0.02), flavour (±0.07), juiciness (±0.06) and overall quality (±0.05) indicates the closeness of the two values.…”

Optimisation of Germination Conditions by Response Surface Methodology of a High Amylose Rice ( ) Cultivar

“…Germination is a simple operation but interactions between different variables do affect the quality of the germinated cereal flour. A central composite design is one of the most useful approaches in determining optimum conditions of many processes (11)(12)(13)(14). It explores the response surfaces covered in the experimental design, thus making the optimisation process efficient and effective.…”