Data acquisition and analysis system for near-infrared spectrum of food

Abstract

The project aims to develop an Internet of things (IoT) system which acquires and analyzes near-infrared spectral data for the further purpose of food quality assessment. The system includes a scan nano device collecting data from our environment and a mobile application interacting with both the nano device and users. The Bluetooth low energy is the communication bridge between the nano device and mobile application because it has a much smaller energy consumption. There are three types of primary users: guest, inspector, and expert. The guests can use this application to view spectrum plots and their parameters by scanning any samples. The inspectors have required authentication to access the system and they can load the Machine Learning models for predicting the results of the scanned sample. And finally, the experts can use this system to create and train machine learning models based on the existing datasets in the database. The data after scanning will be sent to the server and stored in the database for increasing dataset size to increase the accuracy of the predictive results. In addition, I experimented the results of machine learning models in this application based on a case study with fruit type and fruit freshness recognition.