Ensemble learning-assisted quantitative identifying influencing factors of cadmium and arsenic concentration in rice grain based multiplexed data.

Abstract

Rapid and accurate prediction of rice Cd (rCd) and rice As (rAs) bioaccumulation are important for assessing the safe utilization of rice. Currently, there is lack of comprehensive and systematic exploration of the factors of rCd and rAs. Herein, ensemble learning (EL) was first used to analysis the 23 factors in 8 categories (heavy metal pollution characteristics, soil properties, geographical characteristics, meteorological factors, socio-economic factors, environmental factors, rice type, and nutrient element) in typical regions of China based on the results of 193 research papers from 2000 to 2024 in Web of Science database. Three machine learning methods were used to predict rCd and rAs concentrations and identify the key factors in each region, and explored the mechanism of Cd and As uptake in rice. The results showed that there were large differences in the factors affecting rice enrichment for the same heavy metal in different regions. For Cd, rice type (48.30 %), soil characteristics (28.14 %), and environmental factors (61.30 %) were the most important factors in Central South, East China, and Southwest China, respectively. For As, soil properties (34.01 %) and geographical characteristics (50.22 %) had the greatest influence in Central South and East China, respectively. Our study provided valuable insights into the prediction of rCd and rAs, thus contributing to ensuring food safety and preventing Cd and As exposure-associated health risks.