Machine Learning Approaches in Food Quality Enhancement for Safety – A Study

Food adulteration refers to the deliberate method of adding low-quality sub-stitutes to reduce food quality for monetary gain. It creates many health haz-ards. Many customers, producers of high-quality goods, and government agencies and authorities are concerned about ensuring food authenticity to safeguard consumers by identifying potential food fraud. In food research labs, adulteration is often found by conventional methods, which are expen-sive, and specialized expertise is needed to analyze them. The analysis of vast amounts of data generated from various analytical instruments is cum-bersome and time-consuming. This enables the development of ML models to accurately classify food samples with significantly reduced time and re-sources. In this work, a study of different ML approaches used for food safety is carried out.