Due to increasing the consumption and limited access of fossil fuels, alternative and clean energy sources

is required. On the other hand, the production of biodiesel from some plants, such as microalgae, is costly

and leading to high prices. Hence, some of the Profiteers, add cheap biodiesels such as canola and waste
cooking oil biodiesel to expensive biodiesel with various percentages. In this research, a combination of
non-destructive capacitive sensors and machine vision techniques were used to classify adulterated
microalgae biodiesel samples. To evaluate the obtained data from the dielectric system, discriminant
analysis (DA) and support vector machine (SVM) methods were used. In the next step, the color features
that were extracted from the three color spaces of RGB, HSV and CMY were evaluated by an artificial
neural network method. The fusion of the two techniques of image processing and dielectric technique

can play a significant role in the classification of different samples.