Abstract: 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.

Key words: machine learning, biotoxin, food safety, model optimization