With the increasing severity of food safety problems worldwide, rapid prediction of potential toxins in foods has become critical. Traditional prediction methods, such as chemical analysis and bioassay, can provide accurate results, but they are time-consuming, costly and complicated to operate, making it difficult to meet the demand for large-scale screening. In recent years, machine learning technology, with its powerful data processing capability and pattern recognition advantages, has shown a broad application prospect in the field of food biotoxin prediction. This paper first discusses the importance of biotoxin prediction in the field of food safety. Then, the basic theory, key algorithms and models of machine learning are introduced in detail, its application in biotoxin prediction is discussed, and the practical effects of different algorithms and models are analyzed. To address the problems of machine learning in biotoxin prediction, model optimization and improvement strategies are discussed, including feature selection, hyperparameter tuning, and integrated learning. The potential challenges facing the application of machine learning in this field, such as data availability, model generalization ability, and the complexity of cross-disciplinary cooperation, are pointed out, and potential future research directions are also proposed. In the future, with the continuous progress of machine learning and the gradual expansion of food biotoxin datasets, it is expected that its application in the field of food biotoxin prediction will be further developed to provide strong support for environmental protection and human health.

The application of deep learning in food safety detection and risk early warning is becoming more and more extensive, thus providing new opportunities for improving food safety, quality control and authenticity identification. This paper first introduces the basic concept of deep learning and its current development in the field of food safety, and discusses the application of convolutional neural network (CNN), recursive neural network (RNN), transformer architecture and graph neural network (GNN) in food safety detection and risk prediction. Although deep learning performs well in improving the efficiency and accuracy of food safety detection, its practical application still faces challenges such as poor data quality, weak privacy protection capacity and lack of model interpretability. Next, this paper analyzes potential risks that could be brought about by these problems and proposes possible solutions such as promoting data standardization, strengthening privacy protection, and promoting the formulation of policies regarding artificial intelligence. In the future, the combination of deep learning with the Internet of Things (IoT) and blockchain technology and further development of generative artificial intelligence will promote the digital transformation of the food industry and enable the whole-process traceability of food safety monitoring.