Predicting crop root concentration factors of organic contaminants with machine learning models

Feng Gao, Yike Shen, J. Brett Sallach, Hui Li, Wei Zhang, Yuanbo Li*, Cun Liu

*Corresponding author for this work

Research output: Contribution to journalArticlepeer-review


Accurate prediction of uptake and accumulation of organic contaminants by crops from soils is essential to assessing human exposure via the food chain. However, traditional empirical or mechanistic models frequently show variable performance due to complex interactions among contaminants, soils, and plants. Thus, in this study different machine learning algorithms were compared and applied to predict root concentration factors (RCFs) based on a dataset comprising 57 chemicals and 11 crops, followed by comparison with a traditional linear regression model as the benchmark. The RCF patterns and predictions were investigated by unsupervised t-distributed stochastic neighbor embedding and four supervised machine learning models including Random Forest, Gradient Boosting Regression Tree, Fully Connected Neural Network, and Supporting Vector Regression based on 15 property descriptors. The Fully Connected Neural Network demonstrated superior prediction performance for RCFs (R2 = 0.79, mean absolute error [MAE] = 0.22) over other machine learning models (R2 = 0.68–0.76, MAE = 0.23–0.26). All four machine learning models performed better than the traditional linear regression model (R2 = 0.62, MAE = 0.29). Four key property descriptors were identified in predicting RCFs. Specifically, increasing root lipid content and decreasing soil organic matter content increased RCFs, while increasing excess molar refractivity and molecular volume of contaminants decreased RCFs. These results show that machine learning models can improve prediction accuracy by learning nonlinear relationships between RCFs and properties of contaminants, soils, and plants.

Original languageEnglish
Article number127437
Number of pages9
JournalJournal of hazardous materials
Issue numberPart B
Early online date5 Oct 2021
Publication statusPublished - 15 Feb 2022

Bibliographical note

Funding Information:
The work was supported by the National Key Research and Development Program of China , China ( 2019YFC1604503 and 2016YFD0800403 ).

© 2021 Elsevier B.V. This is an author-produced version of the published paper. Uploaded in accordance with the publisher’s self-archiving policy.


  • Machine learning
  • Model interpretability
  • Organic contaminant
  • Plant uptake
  • Root concentration Factor

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