The main idea of this project is to classify any milk sample into pure or adulterated. Our method is much easier than human taste as human taste can’t always classify the milk if it’s pure or adulterated, and also to study the tastes of the tongue and classify the liquids (milk) to detect if it’s edible or not by containing adulterants and it will help in increasing the production as it saves money and effort and the machines are always faster than a human which will lead to faster production. Classification will be done by using a PH meter, conductivity, the electrodes which are used in the PH meter, and conductivity. Milk will be measured in different concentrations.

To facilitate the process of classifying liquids such as milk and black tea for the factories. To have an efficient liquid quality as an output by using deep learning. To raise the accuracy and lower the budget costs. As time passes, the tasting sense of humans decreases gradually and so we can’t depend on such a person for a critical result.

Our system aims to ease the process of classifying milk by having an easy user-friendly mobile application that could be used by different people. We aim to make the classification process faster than the other ways. Also, we aim to have an accuracy higher than 90 percent in SVM as mentioned in cite{zhang2018taste} and, 80 percent in MLP which was mentioned in cite{damarla2020classification} We used deep learning and preprocessed the data by applying normalization, encoding. Moreover, we dropped the repeated rows from our used dataset.