Enhanced food authenticity control using machine learning-assisted elemental analysis

Abstract

With the increasing attention being paid to the authenticity of food, efficient and accurate techniques that can solve relevant problems are crucial for improving public trust in food. This review explains two main aspects of food authenticity, namely food traceability and food quality control. More explicitly, they are the traceability of food origin and organic food, detection of food adulteration and heavy metals. It also points out the limitations of the commonly used morphology and organic compound detection methods, and highlights the advantages of combining the elements in food as detection indicators using machine learning technology to solve the problem of food authenticity. Taking elements as detection objects has the significant advantages of stability, machine learning technology can combine large data samples, ensuring both the accuracy and efficiency. In addition, the most suitable algorithm can be found by comparing their accuracy.