Optimization of Hot Air Roasting of Peanut Kernels Using Response Surface Methodology

Subject Areas : Microbiology

H. Bagheri ¹ , M. Kashainejad ² , M. Aalami ³ , A. M. Ziaiifar ⁴

1 - Ph. D. Graduate of the Faculty of Food Science & Technology, Gorgan University of Agricultural Sciences and Natural Resources, Gorgan, Iran.
2 - Professor of the Faculty of Food Science and Technology, Gorgan University of Agricultural Sciences and Natural Resources, Gorgan, Iran.
3 - Associate Professor of the Faculty of Food Science and Technology, Gorgan University of Agricultural Sciences and Natural Resources, Gorgan, Iran.
4 - Assistant Professor of the Faculty of Food Science and Technology, Gorgan University of Agricultural Sciences and Natural Resources, Gorgan, Iran.

Received: 2019-02-07 Accepted : 2019-02-07 Published : 2019-03-21

Keywords: Energy consumption, Peanut, Quality Characteristics, Roasting, Response Surface Method,

Abstract :

Introduction: Roasting is a high temperature short time (HTST) heat treatment process and enhances the flavor of product and improves the textural and organoleptic properties of the nuts. Materials and Methods: In this study, a hot-air roasting process for the production of peanut snack was optimized by response surface methodology (RSM) over a range of air temperatures (140–180°C) for various times (10-30 min). The color parameters including lightness (L*), redness (a*), yellowness (b*) and total color differences (ΔE), textural characteristics (hardness and compressive energy), sensory properties, moisture content of the peanuts and energy consumption were used as response parameters to develop predictive models and optimize the roasting process.Results: The results showed that by increasing the by temperature and time of roasting, the L*, b*, moisture content, hardness and compressive energy were decreased and ΔE* and energy consumption were increased. The result of RSM analysis showed that quality parameters could be used to control the roasting of peanut kernels in a hot-air roaster. In order to obtain the desired quality parameter, the optimum roasting for production of peanut snack was determined at 162°C for 29 min. Conclusion: This study revealed that RSM could be used to develop adequate prediction models for describing color and texture changes in peanut kernels during hot-air roasting. The changes in the quality parameters were adequately described by quadratic model. Successful optimization for the peanut kernels roasting process can also be made using desirability functions in RSM.

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