Mycotoxins often contaminate rice, which are the secondary metabolites of fungi. Ustiloxins, a type of mycotoxin that has often been overlooked, pose a significant risk to human health. Therefore, identifying and controlling the pollution of ustiloxins in rice is required. In this study, we examined the natural contamination of rice with ustiloxins and their link to climate conditions. A total of 300 paddy samples were collected from six regions in southern China, and concentrations of ustiloxins A, B, C, D, and F were analyzed by liquid chromatography–tandem mass spectrometry (LC-MS/MS). Results showed that the occurrence of ustiloxins A, B, C, D, and F in paddies was found to be 55.7%, 41.3%, 29.0%, 93.7%, and 96.7%, respectively. Ustiloxin A had the highest mean (177.8 µg/kg) and maximum (3620.9 µg/kg) concentration, followed, in order, by ustiloxins C, B, D, and F. Furthermore, ustiloxin levels were significantly different depending on their origin, with the highest concentration in rice from Changde and Yueyang. And the regional difference in ustiloxins was related to the diversity of climate. A positive correlation between ustiloxin occurrence and mean humidity and precipitation was found in July and August of different regions, while mean temperature indicated a negative dependence. This is an essential survey of the contamination of rice with ustiloxins throughout southern China. The influence of climatic conditions on ustiloxins contamination was evaluated for the first time in our study. Overall, the rice samples examined in this study exhibited a high distribution of ustiloxins, suggesting that regulatory limits and the establishment of maximum allowable levels of ustiloxins in rice are necessary. This study provides a basis and guidance for the pollution situation and control strategy of ustiloxins in China.

Routine monitoring of cadmium (Cd) contamination in rice is essential for public health protection and agricultural trade security. Conventional chemical detection methods are environmentally unfriendly, labor-intensive, and slow. This study presents a rapid, accurate classification approach based on near-infrared reflectance spectroscopy (NIRS) for discriminating Cd-contaminated rice from uncontaminated rice. Five spectral preprocessing methods and three variable selection algorithms were systematically evaluated for their influence on model performance. Classification models were developed using partial least squares discriminant analysis (PLS-DA), K-nearest neighbors (KNN), and support vector machines (SVM). Second derivative (2D) preprocessing yielded the greatest performance gains, raising KNN and SVM test-set accuracy from 73% and 88% to 93% and 91%, respectively. Among the variable selection strategies, the successive projections algorithm (SPA) proved most effective. Under optimized conditions, PLS-DA achieved the best overall performance, attaining 92% accuracy, 89% specificity, and 95% sensitivity on the test set. These results demonstrate the strong potential of NIRS coupled with machine learning for rapid, large-scale Cd surveillance in rice, providing robust technical support for grain quality monitoring and low-cadmium variety breeding programs.