This study explores the transformative role of artificial intelligence(AI)in food testing,focusing on its applications in food safety,quality assessment,and authenticity verification.Addressing the limitations of trad...This study explores the transformative role of artificial intelligence(AI)in food testing,focusing on its applications in food safety,quality assessment,and authenticity verification.Addressing the limitations of traditional detection methods in efficiency and cost-effectiveness,the research systematically analyzes how machine learning(ML)and deep learning(DL)technologies synergize with advanced measurement techniques such as sensor detection,spectral imaging,and molecular analysis to achieve rapid,non-destructive testing.The paper emphasizes the critical role of data preprocessing and feature engineering in optimizing model performance,while comparing the advantages of supervised,unsupervised,and semi-supervised learning algorithms across diverse detection scenarios.It highlights the necessity of Explainable Artificial Intelligence(XAI)to enhance system transparency and trustworthiness.Future directions are proposed,including the integration of multimodal data,development of adaptive AI systems,and establishment of predictive safety indicators.The study provides a theoretical framework and technical roadmap for advancing AI applications in food testing,offering significant insights for driving intelligent transformation in the food industry.展开更多
基金supported by the National Key Research and Development Program of China(2024YFF0199500).
文摘This study explores the transformative role of artificial intelligence(AI)in food testing,focusing on its applications in food safety,quality assessment,and authenticity verification.Addressing the limitations of traditional detection methods in efficiency and cost-effectiveness,the research systematically analyzes how machine learning(ML)and deep learning(DL)technologies synergize with advanced measurement techniques such as sensor detection,spectral imaging,and molecular analysis to achieve rapid,non-destructive testing.The paper emphasizes the critical role of data preprocessing and feature engineering in optimizing model performance,while comparing the advantages of supervised,unsupervised,and semi-supervised learning algorithms across diverse detection scenarios.It highlights the necessity of Explainable Artificial Intelligence(XAI)to enhance system transparency and trustworthiness.Future directions are proposed,including the integration of multimodal data,development of adaptive AI systems,and establishment of predictive safety indicators.The study provides a theoretical framework and technical roadmap for advancing AI applications in food testing,offering significant insights for driving intelligent transformation in the food industry.
