Abstract：
Trace elements in plants primarily derive from soils, subsequently influencing human health through the food chain. Therefore, it is essential to understand the relationship of trace elements between plants and soils. Since trace elements from soils absorbed by plants is a nonlinear process, traditional multiple linear regression (MLR) models failed to provide accurate predictions. Zinc (Zn) was chosen as the objective element in this case. Using soil geochemical data, artificial neural networks (ANN) were utilized to develop predictive models that accurately estimated Zn content within wheat grains. A total of 4036 topsoil samples and 73 paired rhizosphere soil-wheat samples were collected for the simulation study. Through Pearson correlation analysis, the total content of elements (TCEs) of Fe, Mn, Zn, and P, as well as the available content of elements (ACEs) of B, Mo, N, and Fe, were significantly correlated with the Zn bioaccumulation factor (BAF). Upon comparison, ANN models outperformed MLR models in terms of prediction accuracy. Notably, the predictive performance using ACEs as input factors was better than that using TCEs. To improve the accuracy, a two-step model was established through multiple testing. Firstly, ACEs in the soil were predicted using TCEs and properties of the rhizosphere soil as input factors. Secondly, the Zn BAF in grains was predicted using ACE as input factors. Consequently, the content of Zn in wheat grains corresponding to 4036 topsoil samples was predicted. Results showed that 85.69 % of the land was suitable for cultivating Zn-rich wheat. This finding offers a more accurate method to predict the uptake of trace elements from soils to grains, which helps to warn about abnormal levels in grains and prevent potential health risks.
摘要：
植物中的微量元素主要来自土壤，通过食物链影响人类健康。因此,了解植物和土壤之间微量元素的关系至关重要。由于植物吸收土壤中的微量元素是一个非线性过程，传统的多元线性回归(MLR)模型无法提供准确的预测。在这种情况下,选择锌(Zn)作为目标元素。利用土壤地球化学数据，人工神经网络（ANN）用于开发预测模型，以准确估计小麦籽粒中的锌含量。总共收集了4036个表层土壤样品和73个配对的根际土壤-小麦样品用于模拟研究。通过皮尔逊相关分析,铁的总元素含量(TCEs)，Mn,Zn,P,以及B的元素(ACE)的可用含量，Mo,N,Fe,与锌生物富集因子(BAF)显著相关。经过比较,ANN模型在预测准确性方面优于MLR模型。值得注意的是,使用ACE作为输入因子的预测性能优于使用TCE的预测性能。为了提高准确性，通过多次测试建立了两步模型。首先,使用TCE和根际土壤的性质作为输入因子来预测土壤中的ACE。其次,用ACE作为输入因子预测谷物中的ZnBAF。因此,预测了对应4036个表层土壤样品的小麦籽粒中锌的含量。结果表明，85.69％的土地适合种植富锌小麦。这一发现提供了一种更准确的方法来预测微量元素从土壤到谷物的吸收，这有助于警告谷物中的异常水平，并防止潜在的健康风险。
