Hyperspectral imaging captures material-specific spectral data, making it highly effective for detecting contaminants in food that are challenging to identify using conventional methods. In the food industry, the occurrence of unknown contaminants is particularly problematic due to the difficulty in obtaining training data. This highlights the need for anomaly detection algorithms that can identify previously unseen contaminants by learning from normal data. This dataset is designed to test anomaly detection performance in normal data that contains impurities.