Data integrity of food and machine learning: Strategies, advances and prospective.
Journal:
Food chemistry
PMID:
40120309
Abstract
Data integrity is an emerging concept aimed at recording real food properties in the form of data throughout the food lifecycle. However, due to the one-sided nature of current food control data, the comprehensive implementation of data integrity has not been fully achieved. Cause food data integrity realization is required to establish the connection of data-algorithm-application. Machine learning (ML) provides a possibility for the practical carrier of food data integrity. Despite ML is one of top-trend in food quality and safety, ML applications are floating on the surface. The current review does not reveal the relationships behind different algorithms and data patterns. Similarly, due to the rapid development of ML, the current advanced concepts and data explanation tools have not been systematically reviewed. This paper expounds the feasibility of machine learning to achieve data integrity and looks forward to the future vision brought about by artificial intelligence to data integrity.