Study on effects of airborne Pb pollution on quality indicators and accumulation in tea plants using Vis-NIR spectroscopy coupled with radial basis function neural network

• Published in: Ecotoxicology and environmental safety Vol. 229; p. 113056
• Main Authors: Sanaeifar, Alireza, Zhang, Wenkai, Chen, Haitian, Zhang, Dongyi, Li, Xiaoli, He, Yong
• Format: Journal Article
• Language: English
• Published: Netherlands Elsevier Inc 01.01.2022; Elsevier
• Subjects: Airborne Pb pollution; Chemometrics; Lead; Least-Squares Analysis; Neural Networks, Computer; Quality indicators; Quality Indicators, Health Care; Quantitative determination model; Spectroscopy, Near-Infrared; Tea; Tea plant; Vis-NIR spectroscopy; Quantitative determination model; Quality indicators; Vis-NIR spectroscopy; Airborne Pb pollution; Tea plant
• DOI: 10.1016/j.ecoenv.2021.113056
• ISSN: 0147-6513; 1090-2414

Tea plants that have a large leaf area mainly suffer from heavy metal accumulation in the above-ground parts through foliar uptake. With the world rapid industrialization, this pollution in tea is considered a crucial challenge due to its potential health risks. The present study proposes an innovative approach based on visible and near-infrared (Vis-NIR) spectroscopy coupled with chemometrics for the characterization of tea chemical indicators under airborne lead stress, which can be performed fast and in situ. The effects of lead stress on chemical indicators and accumulation in leaves of the two tea varieties at different time intervals and levels of treatment were investigated. In addition, changes in cell structure and leaf stomata were monitored during foliar uptake of aerosol particles by transmission electron microscopy (TEM) and scanning electron microscopy (SEM). The spectral variation was able to classify the tea samples into the Pb treatment groups through the linear discriminant analysis (LDA) model. Two machine learning techniques, namely, partial least squares (PLS) and radial basis function neural network (RBFNN), were evaluated and compared for building the quantitative determination models. The RBFNN models combined with correlation-based feature selection (CFS) and PLS data compression methods were used to optimize the prediction performance. The results demonstrated that the PLS–RBFNN as a non-linear model outperformed the PLS model and provided the R-value of 0.944, 0.952, 0.881, 0.937, and 0.930 for prediction of MDA, starch, sucrose, fructose, glucose, respectively. It can be concluded that the proposed approach has strong application potential in monitoring the quality and safety of plants under airborne heavy metal stress. [Display omitted] •Effects of Pb pollution on tea quality and safety were explored by Vis-NIR spectra.•Leaf microstructure changes during uptake of atmospheric aerosols were studied.•Spectral differences can distinguish samples into airborne Pb treatment groups.•PLS–RBFNN performed better than PLS in predicting the chemical indicators of tea.•The spectra coupled with nonlinear models yielded high accuracy for Pb monitoring.