Examining plant uptake and translocation of emerging contaminants using machine learning: Implications to food security

Majid Bagheri; Khalid Al-Jabery; Donald Wunsch; Joel G Burken

doi:10.1016/j.scitotenv.2019.133999

Examining plant uptake and translocation of emerging contaminants using machine learning: Implications to food security

Sci Total Environ. 2020 Jan 1:698:133999. doi: 10.1016/j.scitotenv.2019.133999. Epub 2019 Aug 20.

Authors

Majid Bagheri¹, Khalid Al-Jabery², Donald Wunsch², Joel G Burken³

Affiliations

¹ Civil, Architectural and Environmental Engineering Department, Missouri University of Science and Technology, Rolla, MO, United States.
² Applied Computational Intelligence Laboratory, Electrical and Computer Engineering Department, Missouri University of Science and Technology, Rolla, MO, United States.
³ Civil, Architectural and Environmental Engineering Department, Missouri University of Science and Technology, Rolla, MO, United States. Electronic address: burken@mst.edu.

PMID: 31499345
DOI: 10.1016/j.scitotenv.2019.133999

Abstract

When water and solutes enter the plant root through the epidermis, organic contaminants in solution either cross the root membranes and transport through the vascular pathways to the aerial tissues or accumulate in the plant roots. The accumulation of contaminants in plant roots and edible tissues is measured by root concentration factor (RCF) and fruit concentration factor (FCF). In this paper, 1) a neural network (NN) was applied to model RCF based on physicochemical properties of organic compounds, 2) correlation and significance of physicochemical properties were assessed using statistical analysis, 3) fuzzy logic was used to examine the simultaneous impacts of significant compound properties on RCF and FCF, 4) a clustering algorithm (k-means) was used to identify unique groups and discover hidden relationships within contaminants in various parts of the plants. The physicochemical cutoffs achieved by fuzzy logic for the RCF and the FCF were compared versus the cutoffs for compounds that crossed the plant root membranes and found their way into transpiration stream (measured by transpiration stream concentration factor, TSCF). The NN predicted the RCF with improved accuracy compared to mechanistic models. The analysis indicated that log K_ow, molecular weight, and rotatable bonds are the most important properties for predicting the RCF. These significant compound properties are positively correlated with RCF while they are negatively correlated with TSCF. Comparing the relationships between compound properties in various plant tissues showed that compounds detected in the edible parts have physicochemical cutoffs that are more like the compounds crossing the plant root membranes (into xylem tissues) than the compounds accumulating in the plant roots, with clear relationships to food security. The cluster analysis placed the contaminants into three meaningful groups that were in agreement with the results of fuzzy logic.

Keywords: Clustering algorithm; Food security; Fuzzy logic; Neural network; Plant uptake.

MeSH terms

Cluster Analysis
Food Supply*
Fuzzy Logic
Machine Learning*
Neural Networks, Computer
Plants / metabolism*
Soil Pollutants / metabolism*
Xylem

Substances

Soil Pollutants