Soil, one of the most precious natural resources on Earth, gradually accumulates heavy metals, inevitably causing significant damage to the ecological environment. Here, we introduce confocal controlled laser induced breakdown spectroscopy (CCLIBS) technology for the quantitative analysis of the heavy metal cadmium in soil for the first time. CCLIBS offers better spatial consistency and stable plasma temperature during sample ablation compared to traditional LIBS, thereby reducing matrix effects to improve the accuracy of the quantitative results. The fluctuation of the spectrum and limit of detection are reduced by 0.6 times and 0.39 times, respectively. An effective prediction model was established using the partial least squares method, with a determination coefficient increased to 0.96. The root mean square error of prediction and average relative error are reduced to 67.67 and 0.20, respectively. These results indicate that CCLIBS provides consistent ablation conditions for elemental quantification and yields reliable test results, which is significant for monitoring heavy metals in the ecological environment and effectively intervene and mitigate environmental contamination.

Rice is the primary grain crop in China, and the quality of rice is closely related to the external environment, such as soil characteristics, climate, sunshine time, and irrigation water. The high-quality rice-origin area has certain regional limitations. Therefore,the rice can be seen as an apparent geographical marker. There are often some counterfeits or branded famous high-quality rice in the market, which can damage the rice brand, reduce the rice quality guarantee of consumers, and disturb the market stability, so rapid identification technology of rice origin is needed. The rice origin identification models of five sources in Jilin Province (Daan, Gongzhuling, Qianguo, Songyuan and Taoerhe) are done by laser-induced breakdown spectroscopy and machine learning algorithms. The principal component analysis (PCA) algorithm, combined with four machine learning algorithms, Bagged Trees, Weighted KNN, Quadratic SVM, and Coaster Gaussian SVM, has been established. A total of 450 groups of LIBS data are selected. The spectral data of rice LIBS are pretreated with Savitzky-Golay smoothing (SG smoothing) is used for noise reduction and normalisation. The principal component analysis uses the rice LIBS data, which shows that the rice origins had an excellent cluster distribution of clustering spaces. Still, there is spatial overlap in some rice origins. Utilising5x cross-validation, the identification accuracy of rice origins can reachmore than 91.8% by adopting PCA-Bagged Trees, PCA-Weighted KNN, PCA-Quadratic SVM and PCA-Coarse Gaussian SVM, and the recognition accuracy of PCA-Quadratic SVM model is as high as 97.3%. The results show that the combination of LIBS technology and machine learning algorithms can identify rice origin with high precision and high efficiency.