Improved Cd Detection in Rice Grain Using LIBS with Husk-Based XGBoost Transfer Learning

Cadmium (Cd) is a highly toxic metal that is difficult to completely eliminate from soil, despite advancements in modern agricultural and environmental technologies that have successfully reduced Cd levels. However, rice remains a key source of Cd exposure for humans. Even small amounts of Cd absorbed by rice can pose a potential health risk to the human body. Laser-induced breakdown spectroscopy (LIBS) has the advantages of simple sample preparation and fast analysis, which, combined with the transfer learning method, is expected to realize the real-time and rapid detection of low-level heavy metals in rice. In this work, 21 groups of naturally matured rice samples from potentially Cd-contaminated environments were collected. These samples were processed into rice husk, brown rice, and polished rice groups, and the reference Cd content was measured by ICP-MS. The XGBoost algorithm, known for its excellent performance in handling high-dimensional data and nonlinear relationships, was applied to construct both the XGBoost base model and the XGBoost-based transfer learning model to predict Cd content in brown rice and polished rice. By pre-training on rice husk source data, the XGBoost-based transfer learning model can learn from the abundant information available in rice husk to improve Cd quantification in rice grain. For brown rice, the XGBoost base model achieved R C 2 of 0.9852 and R P 2 of 0.8778, which were improved to 0.9885 and 0.9743, respectively, with the XGBoost-based transfer learning model. In the case of polished rice, the base model achieved R C 2 of 0.9838 and R P 2 of 0.8683, while the transfer learning model enhanced these to 0.9883 and 0.9699, respectively. The results indicate that the transfer learning method not only improves the detection capability for low Cd content in rice but also provides new insights for food safety detection.