Rancidity Analysis Management System Based on Machine Learning Using IoT Rancidity Sensors

Abstract

Rancidity data can be used in various fields such as the quality analysis of food and raw materials used for construction. The rancidity of raw materials used in road pavement asphalt is currently only at the level determined by the temperature or visual factors. Although construction workers are managed individually and subjectively, such as by visual methods, they cannot be managed in practice. In this paper, we propose a system combining a rancidity sensor with an Internet of Things (IoT) communication module that collects and predicts rancidity measurements in real time at a site. The values measured by the sensor are periodically transferred to the Cloud through the IoT communication module, the validity of the data set is established, and the systematic management of the data is performed using machine-learning-based data analysis techniques. The results of an experiment showed a high classification prediction accuracy of 91.3% and a short-term pattern prediction accuracy of 96.6% (weighted scaling), confirming its excellent potential for raw material quality management. The results of this paper will be applied as a road pavement quality management system.