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Estimation of Dielectric Properties of Cakes Based on Porosity, Moisture Content, and Formulations Using Statistical Methods and Artificial Neural Networks

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Abstract

Dielectric constant (DC) and dielectric loss factor (DLF) are the two principal parameters that determine the coupling and distribution of electromagnetic energy during radiofrequency and microwave processing. In this study, chemometric methods [classical least square (CLS), principle component regression (PCR), partial least square (PLS), and artificial neural networks (ANN)] were investigated for estimation of DC and DLF values of cakes by using porosity, moisture content and main formulation components, fat content, emulsifier type (Purawave™, Lecigran™), and fat replacer type (maltodextrin, Simplesse). Chemometric methods were calibrated firstly using training data set, and then they were tested using test data set to determine estimation capability of the method. Although statistical methods (CLS, PCR and PLS) were not successful for estimation of DC and DLF values, ANN estimated the dielectric properties accurately (R 2, 0.940 for DC and 0.953 for DLF). The variation of DC and DLF of the cakes when the porosity value, moisture content, and formulation components were changed were also visualized using the data predicted by trained network.

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Abbreviations

A :

Input matrix

ANN:

Artificial neural networks

b n :

Coefficient matrix

CLS:

Classical least-square

DC:

Dielectric constant

DLF:

Dielectric loss factor

MSE:

Mean square error

n −1 :

Inverse of matrix n

n T :

Transpose of matrix n

P :

Eigenvectors matrix

PCR:

Principle component regression

PLS:

Partial least square

R 2 :

Coefficient of determination values

T :

Score matrix

W n :

Weight vectors

X 1 :

Porosity, %

X 2 :

Moisture content, % (w/w)

X 3 :

Fat content, % (on flour-weight basis)

X 4 :

Purawave, % (on flour-weight basis)

X 5 :

Lecigran, % (on flour-weight basis)

X 6 :

Maltodextrin, % (on flour-weight basis)

X 7 :

Simplesse, % (on flour-weight basis)

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Authors and Affiliations

  1. Faculty of Engineering, Department of Food Engineering, Hacettepe University, Beytepe Campus, Ankara, 06532 , Turkey

    İsmail Hakkı Boyacı

  2. Department of Food Engineering, Middle East Technical University, Ankara, 06531, Turkey

    Gulum Sumnu & Ozge Sakiyan

  3. Department of Food Engineering, Selcuk University, Konya, Turkey

    Ozge Sakiyan

Authors
  1. İsmail Hakkı Boyacı
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  2. Gulum Sumnu
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  3. Ozge Sakiyan
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Correspondence to İsmail Hakkı Boyacı.

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Boyacı, İ.H., Sumnu, G. & Sakiyan, O. Estimation of Dielectric Properties of Cakes Based on Porosity, Moisture Content, and Formulations Using Statistical Methods and Artificial Neural Networks. Food Bioprocess Technol 2, 353–360 (2009). https://doi.org/10.1007/s11947-008-0064-z

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  • DOI: https://doi.org/10.1007/s11947-008-0064-z

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