Building a service-oriented Website application to manage and analyze near-infrared spectral data of food

Abstract

Artificial Intelligence (AI) is becoming pervasive very rapidly because of its robust applicability in most areas of life. Such an area of extreme importance is agriculture [1]. Besides, near-infrared spectroscopy today is widely used to determine the chemical changes and properties of food and agricultural products. For this project, I built a website application to manage and analyze near-infrared spectral data of food, which uses basic machine learning algorithms. The set goal of the application is to effectively support data storage, data processing using common techniques such as normalization, exception handling, etc. And support machine learning algorithms that were designed and implemented. The result is a website application that meets the original goals, with a user-friendly interface and familiar operations.