Pirmoradi, M., Mostafaei, M., Naderloo, L., Javadikia, H. (2022). Modeling Honey Adulteration by Processing Microscopic Images Using Artificial Intelligence Methods. , 24(2), 365-378.
M. Pirmoradi; M. Mostafaei; L. Naderloo; H. Javadikia. "Modeling Honey Adulteration by Processing Microscopic Images Using Artificial Intelligence Methods". , 24, 2, 2022, 365-378.
Pirmoradi, M., Mostafaei, M., Naderloo, L., Javadikia, H. (2022). 'Modeling Honey Adulteration by Processing Microscopic Images Using Artificial Intelligence Methods', , 24(2), pp. 365-378.
Pirmoradi, M., Mostafaei, M., Naderloo, L., Javadikia, H. Modeling Honey Adulteration by Processing Microscopic Images Using Artificial Intelligence Methods. , 2022; 24(2): 365-378.

Modeling Honey Adulteration by Processing Microscopic Images Using Artificial Intelligence Methods

Journal of Agricultural Science and Technology
Article 9, Volume 24, Issue 2, 2022, Pages 365-378    PDF (1 M)
Document Type: Original Research
Authors
M. Pirmoradi; M. Mostafaei* ; L. Naderloo; H. Javadikia
Department of Mechanics of Biosystems Engineering, Razi University, Kermanshah, Islamic Republic of Iran.
Abstract
The aim of this study was to determine the authenticity of honey by processing microscopic images and obtaining an algorithm for classifying various honey frauds. In this study, sucrose, fructose, and fructose-glucose solution at a ratio of 0.9 were used to make honey adulteration. The level of adulterated honey was based on the weight percentages of 2.5, 5, 7.5, 10, 20, 30, 40, 50, 60, 70, 80, 90 and 100 by stirring. Different samples were imaged under a microscope. Each image was processed in 33 monochrome color spaces and 15 parameters were extracted from it. The three main and effective parameters of various color spaces were selected using sensitivity analysis for modeling honey fraud by adaptive Fuzzy Neural Inference System (ANFIS), Artificial Neural Network (ANN), and response surface methodology. Various criteria were used to evaluate the performance of the models such as coefficient of determination, mean square error, sum of squared estimate of errors, and mean absolute errors. The results showed that the determination coefficient and the mean square error of the artificial neural network model was 0.974 and 0.0024, respectively. Finally, using the desirability function, the artificial neural network model was selected as the best model due to less prediction error values and desirability of 0.948.
Keywords
Adaptive fuzzy neural inference system; artificial neural network; Desirability function; response surface methodology
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