In recent years, counterfeit and substandard food products have become an increasing concern to consumers, and food authenticity assessment is a powerful tool to address this problem and protect public health. Under the high requirements of equipment and sample processing, modern detection technologies usually require a lot of time and money cost consumption. However, as food adulteration methods change and become more sophisticated, traditional modern food quality detection technologies have certain limitations. Therefore, it is necessary to seek new detection technology to effectively promote the efficiency and improvement of food safety quality control and provide strong scientific and technological support and protection for regulatory work-spectroscopic analysis technology, which has been used extensively in recent years for its simplicity and rapidity. As an indirect analysis technique, it needs to be combined with classification methods in data statistics to establish models and achieve rapid analysis requirements. Commonly used classification methods are ineffective in the face of the enormous variety of adulteration types in real life and the considerable variation in the number of true and false samples. One-class classification is a method that models and analyses only one class of instances, fixing the boundaries of the target sample class at a specific confidence level for classification and then using the edges of the target sample to predict the class of the new sample, distinguishing it from all other possible objects. Using this feature to effectively differentiate between samples that are different from the actual data, significantly reducing the detection effort, and has some potential for development in food adulteration detection applications. This paper reviewed the one-class classification method, which has been used in pattern recognition in recent years and described the need for spectral analysis combined with classification methods for food adulteration. The classification results of traditional -and one-class classification methods were compared in the same scenario, and the latter's characteristics were briefly introduced. Then, several common one-class classification methods were highlighted, such as data-driven class comparison soft independent modelling (DD-SIMCA), one-class partial least squares (OCPLS), and one-class support vector machines (OCSVM), and one-class random forests (OCRF). The applications of one-class classification methods in the food authenticity identification were also discussed, specifically in edible oils, dairy products, beverages, herbs, spices, and agricultural products. At last, the problems of the current one-class classification were analyzed, and the prospects for applying the technique were outlined. This paper is expected to provide some theoretical basis for food certification analysis.