Modeling Honey Adulteration by Processing Microscopic Images Using Artificial Intelligence Methods

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 ۰.۹ were used to make honey adulteration. The level of adulterated honey was based on the weight percentages of ۲.۵, ۵, ۷.۵, ۱۰, ۲۰, ۳۰, ۴۰, ۵۰, ۶۰, ۷۰, ۸۰, ۹۰ and ۱۰۰ by stirring. Different samples were imaged under a microscope. Each image was processed in ۳۳ monochrome color spaces and ۱۵ 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 ۰.۹۷۴ and ۰.۰۰۲۴, 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 ۰.۹۴۸.

کلیدواژه ها:

Adaptive fuzzy neural inference system ، Artificial neural network ، Desirability function ، Response surface methodology.