Rapid detection of cadmium and its distribution in Miscanthus sacchariflorus based on visible and near-infrared hyperspectral imaging
文献类型： 外文期刊
作者： Feng, Xuping 1 ; Chen, Houming 3 ; Chen, Yue 4 ; Zhang, Cheng 3 ; Liu, Xiaodan 1 ; Weng, Haiyong 1 ; Xiao, Shupei 1 ; Nie 1 ;
作者机构： 1.Zhejiang Univ, Coll Biosyst Engn & Food Sci, Hangzhou 310058, Zhejiang, Peoples R China
2.Zhejiang Univ, State Key Lab Modern Opt Instruments, Hangzhou 310027, Peoples R China
3.Zhejiang Univ, Coll Life Sci, State Key Lab Plant Physiol & Biochem, Hangzhou 310058, Zhejiang, Peoples R China
4.Zhejiang Acad Agr Sci, Inst Hort, Hangzhou 310021, Zhejiang, Peoples R China
关键词： Biomass crop; Vis-NIR hyperspectral imaging; Chemometrics analysis; Cadmium; Vegetation index; Model
期刊名称：SCIENCE OF THE TOTAL ENVIRONMENT （ 影响因子：7.963； 五年影响因子：7.842 ）
ISSN： 0048-9697
年卷期： 2019 年 659 卷
页码：
收录情况： SCI
摘要： Monitoring the effectiveness of Miscanthus sacchariflorus to meet the basic requirements for environmental remediation projects is an important step in determining its use as a productive bioenergy crop for phytoremediation. Conventional chemical methods for the determination of cadmium (Cd) contents involve time-consuming, monotonous and destructive procedures and are not suitable for high-throughput screening. In the present study, visible and near-infrared hyperspectral imaging technology combined with chemometric methods was used to assess the Cd concentrations in M. sacchariflorus. The total Cd concentrations in different plant tissues were measured using an inductively coupled plasma-mass spectrometer. Partial least-squares regression and least-squares support vector machine were implemented to estimate Cd contents from spectral reflectance. Successive projections algorithm and competitive adaptive reweighted sampling (CARS) methodology were used for selecting optimal wavelength. The CARS-partial least-squares regression model resulted in the most accurate predictions of Cd contents in M. sacchariflorus leaves, with a determination coefficient (R-2) of 0.87 and a root mean square error (RMSE) value of 97.78 for the calibration set, and an R-2 value of 0.91 and a RMSE value of 75.95 for the prediction set. The CARS-least-squares support vector machine model resulted in the most satisfactory predictions of Cd contents in roots, with R-2 values of 0.95 (RMSE, 0.92 x 10(3)) for the calibration set and 0.90 (RMSE, 1.64 x 10(3)) for the prediction set. Finally, the Cd concentrations in different plant tissues were visualized on the prediction maps by predicted spectral features on each hyperspectral image pixel. Thus, visible and near-infrared imaging combined with chemometric methods produces a promising technique to evaluate M. sacchariflorus' Cd phytoremediation capability in high-throughput metal-contaminated field applications. (c) 2019 Elsevier B.V. All rights reserved.
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