Detection of Adulteration in Fruits Using Machine Learning
S. Prince Sahaya Brighty, G S Harini, N. Vishal
Abstract
Food is essential for life. The food we take should be pure, nutritious and free from any type of adulteration for proper maintenance of human health. In this paper, an IoT based food and formalin detection technique is developed to detect the presence of formalin using machine-learning approaches. Volatile compound HCHO gas sensor connected with Raspberry pi3 were used to extract the concentration of the formalin as a function of output voltage of any fruit or vegetable and different machine learning algorithms were used to classify the fruit or vegetable based on their extracted features. Supervised machine learning algorithms have been incorporated in our system to accurately predict the correct concentration of formalin at all temperatures which is also able to correctly classify between artificially added and naturally formed formalin.