Recent advances of artificial intelligence in quantitative analysis of food quality and safety indicators: A review

Food quality and safety (FQS) are crucial aspects of everyone's life and health. With the rapidly advancing field of analytical sciences, there is a growing demand for intuitive, accurate, and swift control of FQS. In recent years, artificial intelligence (AI) has emerged as a great opportunity, offering unparalleled opportunities for extracting information and making decisions from complex or large datasets in areas like chromatography, mass spectrometry, and spectroscopy for the identification of FQS indicators. This review provides a comprehensive overview of AI-based technology's general algorithms for FQS indicator analysis. Additionally, it surveys AI-based methods that are at the forefront of analytical techniques and hold significant potential for enhancing the smart control of FQS indicators. Finally, we highlight key challenges and offer recommendations to accelerate progress towards intelligent FQS control.