数字农科院2.0

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

文献类型： 外文期刊

作者： Feng Gao; Yike Shen; J. Brett Sallach; Hui Li; Wei Zhang; Yuanbo Li; Cun Liu

作者机构：

关键词： Machine learning; Model interpretability; Organic contaminant; Plant uptake; Root concentration Factor

期刊名称： Journal of Hazardous Materials

ISSN： 0304-3894

年卷期： 2022 年 424 卷

页码：

收录情况： SCIE（2022版） ; EI（2022版）

摘要： 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.

分类号：

  • 相关文献

[1]Uptake kinetics and accumulation of pesticides in wheat (Triticum aestivum L.): Impact of chemical and plant properties. Qianyu Liu,Yingchao Liu,Fengshou Dong,J. Brett Sallach,Xiaohu Wu,Xingang Liu,Jun Xu,Yongquan Zheng,Yuanbo Li. 2021

[2]Distribution, Dissipation, And Metabolism Of Neonicotinoid Insecticides In The Cotton Ecosystem Under Foliar Spray And Root Irrigation. Dong, Fengshou,She, Dongmei,Ning, Jun,Mei, Xiangdong,Wu, Changcai. 2019

[3]Aluminum-Enhanced Proton Release Associated With P.lasma Membrane H+-Adenosine Triphosphatase A ctivity And Excess Cation Uptake In Tea (Camellia Sinensis) Plant Roots. Wan Qing,Li Xinghui,Mi Yuhe,Yang Yiyang,Wan Qing,Xu Renkou. 2018

[4]Natural And Synthetic Estrogens In L.eafy Vegetable And Their R isk Associated To Human Health. Zain, Muhammad,Yi, Hao,Fahad, Shah,Rizwan, Muhammad,Lee, Jie Yinn,Rui, Yukui,Fahad, Shah,Baluch, Mansoor A.,Fahad, Shah,Ameen, Asif,Adeel, Muhammad,Ameen, Asif. 2018

[5]Mass Loading Of Typical Artificial S.weeteners In A Pig F arm And Their Dissipation And Uptake By Plants In Neighboring Farmland. Ma, L, Liu, YR, Xu, JY, Sun, HW, Chen, H, Yao, YM, Zhang, P, Shen, FJ, Alder, AC. 2017

[6]Fate And Adverse Effects Of H.exabromocyclododecane Diastereoisomers (Hbcdds) In A Soil-Ryegrass Pot System. Zhu, HK, Sun, HW, Yao, YM, Wang, F, Zhang, YW, Liu, XW. 2017

[7]Impact Of Biochar Amendment On The Uptake, Fate And Bioavailability Of Pharmaceuticals In Soil-Radish Systems. Li, YB, He, JZ, Qi, HN, Li, H, Boyd, SA, Zhang, W. 2020

[8]Insight into the uptake and metabolism of a new insecticide cyetpyrafen in plants. Runan Li, Sijia Wang, Jinhe Chang, Xinglu Pan, Fengshou Dong, Zhiyuan Li, Yongquan Zheng, Yuanbo Li. 2022

[9]Evolutionarily Informed Deep Learning Methods F.or Predicting Relative Transcript A bundance From Dna Sequence. Washburn, Jacob D.,Wang, Hai,Wang, Hai,Wang, Hai,Valluru, Ravi,Ramstein, Guillaume,Mejia-Guerra, Maria Katherine,Kremling, Karl A.,Wang, Hai,Buckler, Edward S.,Buckler, Edward S.. 2019

[10]Using Machine Learning in Environmental Tax Reform Assessment for Sustainable Development: A Case Study of Hubei Province, China. Zheng, Yinger,Zheng, Yinger,Zheng, Haixia,Zheng, Haixia,Ye, Xinyue. 2016

[11]Determination of internal qualities of Newhall navel oranges based on NIR spectroscopy using machine learning. Liu, Cong,Yang, Simon X.,Liu, Cong,Deng, Lie.

[12]A comparative study for least angle regression on NIR spectra analysis to determine internal qualities of navel oranges. Liu, Cong,Yang, Simon X.,Liu, Cong,Deng, Lie. 2015

[13]Using evolutionary machine learning to characterize and optimize co-pyrolysis of biomass feedstocks and polymeric wastes. Shahbeik H.,Shafizadeh A.,Nadian M.H.,Jeddi D.,Mirjalili S.,Yang Y.,Lam S.S.,Pan J.,Tabatabaei M.,Aghbashlo M.. 2023

[14]Virtual screening strategy for anti-DPP-IV natural flavonoid derivatives based on machine learning. Lu, Gen,Pan, Fei,Li, Xiaotong,Zhu, Zehui,Zhao, Lei,Wu, Ya,Tian, Wenli,Peng, Wenjun,Liu, Jinling. 2023

[15]Effects of different floral periods and environmental factors on royal jelly identification by stable isotopes and machine learning analyses during non-migratory beekeeping. Zhaolong Liu,Xin Yin,Hongxia Li,Dong Qiao,Lanzhen Chen. 2023

[16]Several models combined with ultrasound techniques to predict breast muscle weight in broilers. Zhengda Li,Jumei Zheng,Bingxing An,Xiaochun Ma,Fan Ying,Fuli Kong,Jie Wen,Guiping Zhao. 2023

[17]Ecosystem responses dominate the trends of annual gross primary productivity over terrestrial ecosystems of China during 2000–2020. Xian Jin Zhu,Gui Rui Yu,Zhi Chen,Wei Kang Zhang,Lang Han,Qiu Feng Wang,Hua Qi,Meng Yang,Zhao Gang Liu,Xiao Jun Dou,Le Xin Ma,Shi Ping Chen,Shao Min Liu,Hui Min Wang,Jun Hua Yan,Jun Lei Tan,Fa Wei Zhang,Feng Hua Zhao,Ying Nian Li,Yi Ping Zhang,Pei Li Shi,Jiao Jun Zhu,Jia Bing Wu,Zhong Hui Zhao,Yan Bin Hao,Li Qing Sha,Yu Cui Zhang,Shi Cheng Jiang,Feng Xue Gu,Zhi Xiang Wu,Yang Jian Zhang,Li Zhou,Ya Kun Tang,Bing Rui Jia,Yu Qiang Li,Qing Hai Song,Gang Dong,Yan Hong Gao,Zheng De Jiang,Dan Sun,Jian Lin Wang,Qi Hua He,Xin Hu Li,Fei Wang,Wen Xue Wei,Zheng Miao Deng,Xiang Xiang Hao,Xiao Li Liu,Xi Feng Zhang,Zhi Lin Zhu. 2023

[18]Advances in the Study of Biochemical, Morphological and Physiological Traits of Wheat and Sorghum Crops in Australia Using Hyperspectral Data and Machine Learning. A. B. Potgieter,C. Camino,T. Poblete,X. Zhi,S. Reynolds-Massey-Reed,Y. Zhao,A. Belwalkar,J. Ruizhu,B. George-Jaeggli,S. Chapman,D. Jordan,A. Wu,G. L. Hammer,P. J. Zarco-Tejada. 2023

[19]Rapid and Non-Invasive Assessment of Texture Profile Analysis of Common Carp (Cyprinus carpio L.) Using Hyperspectral Imaging and Machine Learning. Yi Ming Cao,Yan Zhang,Shuang Ting Yu,Kai Kuo Wang,Ying Jie Chen,Zi Ming Xu,Zi Yao Ma,Hong Lu Chen,Qi Wang,Ran Zhao,Xiao Qing Sun,Jiong Tang Li. 2023

[20]Improving Genomic Prediction with Machine Learning Incorporating TPE for Hyperparameters Optimization. Liang, Mang,An, Bingxing,Li, Keanning,Du, Lili,Deng, Tianyu,Cao, Sheng,Du, Yueying,Xu, Lingyang,Gao, Xue,Zhang, Lupei,Li, Junya,Gao, Huijiang. 2022

作者其他论文 更多>>