Development of a non-destructive method to determine the textural characteristics of baguette bread using a Doppler laser vibrometer sensor

Document Type : Research Article

Authors

1 Associate professor of Agricultural Sciences and Natural Resources University of Khuzestan

2 2. MSc Student Department of Mechanics of Biosystems Engineering, Faculty of Agricultural Engineering and Rural Development, Agricultural Sciences and Natural Resources University of Khuzestan

Abstract

Finding a nondestructive method, for rapid evaluation of bread crumb changes during storage, helps researchers to assess the effect of different additives on quality properties and shelf life of bread. Therefore, in this study the changes of bread free vibrational parameters including natural frequency, damping ration, zero-crossing rate, average, standard deviation, peak, energy and entropy were evaluated in comparison with bread textural and sensorial properties on 0, 2 and 4 storage days. Finally, a predictive model was developed to determine the textural characteristics of bread (firmness, chewiness, cohesiveness and springiness) using support vector regression with three kernels linear, quadratic and radial basis function. Results of correlation coefficient of evaluation showed strong relationship between vibrational parameters with textural variables. In addition, in general, considering the error of three kernels as well as the correlation coefficient, the linear kernel performed better than the other two kernels in predicting textural properties. Therefore, the results of this study demonstrated the suitability of vibrational properties for determination of textural Characteristics of bread crumb during storage.

Graphical Abstract

Development of a non-destructive method to determine the textural characteristics of baguette bread using a Doppler laser vibrometer sensor

Highlights

  • In this research a non-destructive method was developed to determine the textural characteristics of baguette bread using a Doppler laser vibrometer sensor.
  • Nine vibrational parameters (natural frequency, damping ration, zero-crossing rate, average, standard deviation, peak, energy and entropy) were extracted.
  • Results of correlation coefficient of evaluation showed strong relationship between vibrational parameters with textural variables
  • A predictive model was developed to determine the textural characteristics of bread using support vector regression.
  • The prediction error of bread textural characteristics by linear kernel was lower than the other two kernels (quadratic and radial basis function kernel).

Keywords

Main Subjects

References

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Innovative Food Technologies
Volume 10, Issue 1
November 2022
Pages 69-85

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History

  • Receive Date: 12 July 2022
  • Revise Date: 02 September 2022
  • Accept Date: 28 October 2022

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