Optimizing the sunflower seed roasting process in a cylindrical roaster using instrumental systems

Document Type : Research Paper

Authors

1 1- Department of Mechanical Engineering of Biosystems, Faculty of Agriculture, Shahrekord University, Shahrekord, Iran

2 Department of Mechanical Engineering of Biosystems, Faculty of Agriculture, Shahrekord University, Shahrekord, Iran

3 4- Department of Mechanical Engineering of Biosystems, Faculty of Agriculture, Shahrekord University, Shahrekord, Iran

4 5- Department of Mechanical Engineering of Biosystems, Faculty of Agriculture, Razi University, Kermanshah, Iran

Abstract

Roasting is a thermal process and various factors such as temperature and duration of roasting play an important role in the textural and sensory properties of the product. In this research, a roasting machine was designed and fabricated for the process of roasting sunflower seeds. Using an electronic nose, the aroma resulting from the roasting process of the samples were investigated and optimized using the response surface method (Central Compound Design) in the temperature range of 80 to 160 degrees Celsius and the time range of 10 to 30 minutes. The response of electronic nose sensors, color parameters including (L*, a*, b*, ΔE, BI) and sensory evaluation were used to develop prediction models and optimize sunflower seed roasting process. The results showed that increasing the temperature and duration of roasting has a significant effect on the response of electronic nose sensors. With increasing temperature and duration of roasting, the value of L* decreased, but a*, b*, ΔE and BI increased, and the highest overall acceptance value of the human panelist was obtained for the sample with a temperature of 120 degrees Celsius and a duration of 20 minutes. The optimum point for roasting sunflower seeds with the fabricated system was 116 degrees Celsius and a duration of 18 minutes. The results of the research showed the useful efficiency of the fabricated system and for the models presented by the response surface method for predicting the values of the dependent variables, the results were very near to the experimental findings. It was (P<0.05) in all the studied values, which indicated the suitability of the proposed models.

Keywords

Main Subjects

Optimizing the sunflower seed roasting process in a cylindrical roaster using instrumental systems

 

EXTENDED ABSTRACT

Introduction

Sunflower seeds are one of the most popular and widely consumed nuts among nut products, which have a variety of nutrients. Sunflower seeds can be used as raw or roasted. Roasting is a thermal process in which various factors such as roasting method, temperature and duration of roasting play an important role in the texture and sensory characteristics of the product. The high quality of nut products is a basic requirement for consumers. The method of checking and optimizing the process in optimal roasting of nuts and nut products is of great importance. The purpose of this study was to design and fabricate a sunflower seed roasting machine and check and optimize the roasting process with an electronic nose device, color parameters and sensory evaluation using the response surface method.

Materials and methods

In this research, a roasting machine was designed and fabricated using the heat transfer method for roasting sunflower seeds. Using an electronic nose, the aroma resulting from the roasting process of the samples were evaluated. The roasting process was optimized using the Response Surface Method (Central Composite Design) in the temperature range of 80 to 160 degrees Celsius and the time range of 10 to 30 minutes. Response of electronic nose sensors, color parameters including L* (brightness index), a* (redness index), b* (yellowness index), ΔE (overall color change), BI (browning index) and sensory evaluation were considered. The response surface method was used to develop predictive models and optimize the sunflower seed roasting process.

Results and discussion

The results showed that both the factors of temperature and duration of roasting had a significant effect on the response of the electronic nose sensors and the color parameters of the samples, but for sensory evaluation the effect of the factor of duration of roasting was not significant and the interaction of temperature and duration of roasting and their quadratic term had the greatest effect. Increasing the temperature and duration of roasting has a significant effect on the response of electronic nose sensors. With increasing temperature and duration of roasting, the value of L* decreased, but a*, b*, ΔE and BI increased, the average of each of these variables was 83.79, 6.99, 5.13, 21.58 and 2.93, respectively. For the sensory evaluation of roasted sunflower seeds, the interaction effect and the quadratic expression of temperature and roasting time had a very significant effect (P<0.001). the highest score of sensory properties was obtained for the sample at 120°C and a duration of 20 minutes. the results of variance analysis showed that the response of electronic nose sensors, color parameters and sensory evaluation of the samples can be used to check and control the sunflower seed roasting process in the roaster fabricated. The optimal point for roasting sunflower seeds was 116 degrees Celsius and a duration of 18 minutes. In optimal conditions, the roasting operation was performed in three repetitions and the results related to the responses of the dependent variables were compared with the predicted values ​​of the model. In all studied values, it was (P<0.05), which indicates the appropriate models and accuracy of optimization.

Conclusion

The results of the research showed the useful efficiency of the roasting machine. The lack of fit test for all the models obtained from the response level method for the dependent variables was not significant, which indicated the appropriateness of the presented models, and for the models presented to predict the values of the dependent variables. The result was very close to the experimental findings.

 

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Iranian Journal of Biosystems Engineering
Volume 54, Issue 4
January 2024
Pages 1-17

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History

  • Receive Date: 16 April 2024
  • Revise Date: 13 June 2024
  • Accept Date: 24 June 2024

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