A Machine-Learning-based Electronic Tongue for Classifying Milk

Electronic tongues are a novel exciting technology that has already demonstrated their utility in assessing and forecasting the flavor of beverages and other liquids. Our objective is to determine if a milk sample is pure or adulterated. Our method is much easier than human taste because the human taste cannot always classify milk as pure or adulterated, and also to study the tastes of the tongue and classify liquids (milk) to detect if they are edible or not due to the presence of adulterants. This method will aid in increasing production because it saves money and effort, the fact that machines are always faster than humans results in faster production. Classification will be accomplished with the use of a PH meter, conductivity. The electrodes are utilized in the PH meter. Milk will be analyzed at various concentrations. To determine the accuracy, various machine learning methods such as the Support Vector Machine (SVM) would be used. Additionally, Multi-layer Perceptrons (MLPs) will be utilized along with deep learning methods.