Hyperspectral Inversion Model of Relative Heavy Metal Content in <i>Pennisetum sinese Roxb</i> via EEMD-db3 Algorithm

Ting Tang; Canming Chen; Weibin Wu; Ying Zhang; Chongyang Han; Jie Li; Ting Gao; Jiehao Li

doi:10.3390/rs15010251

Remote Sensing (Jan 2023)

Hyperspectral Inversion Model of Relative Heavy Metal Content in <i>Pennisetum sinese Roxb</i> via EEMD-db3 Algorithm

Ting Tang,
Canming Chen,
Weibin Wu,
Ying Zhang,
Chongyang Han,
Jie Li,
Ting Gao,
Jiehao Li

Affiliations

Ting Tang: College of Engineering, South China Agricultural University, Guangzhou 510642, China
Canming Chen: College of Natural Resources and Environment, South China Agricultural University, Guangzhou 510642, China
Weibin Wu: College of Engineering, South China Agricultural University, Guangzhou 510642, China
Ying Zhang: College of Engineering, South China Agricultural University, Guangzhou 510642, China
Chongyang Han: College of Engineering, South China Agricultural University, Guangzhou 510642, China
Jie Li: College of Engineering, South China Agricultural University, Guangzhou 510642, China
Ting Gao: College of Natural Resources and Environment, South China Agricultural University, Guangzhou 510642, China
Jiehao Li: College of Engineering, South China Agricultural University, Guangzhou 510642, China

DOI: https://doi.org/10.3390/rs15010251
Journal volume & issue: Vol. 15, no. 1
p. 251

Abstract

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Detection rapidity and model accuracy are the keys to hyperspectral nondestructive testing technology, especially for Pennisetum sinese Roxb (PsR) due to its extremely high adsorptive heavy metal content. The study of the resolution of PsR is conducive to the analysis of the accumulated heavy metal content in its different parts. In this paper, the contents of Cd, Cu and Zn accumulated in the old leaves, young leaves, upper stem, middle stem and lower stem, as well as the hyperspectral data of the corresponding parts, were measured simultaneously in both fresh and dry states. To begin, the spectral data of PsR were preprocessed by using Ensemble Empirical Mode Decomposition-Daubechies3 (EEMD-db3), Savitzky–Golay (SG), Symlet3 (sym3), Symlet5 (sym5), and multiplicative scatter correction (MSC). The 40 samples were divided into 32 training sets and 8 validation sets. The preprocessed spectral data were transformed by the first derivative (FD) and reciprocal logarithm (log(1/R)) to highlight the singularities using binary wavelet decomposition. After screening the significant bands from the correlation curve, the competitive adaptive reweighted sampling (CARS) and successive projection algorithm (SPA) were applied to extract the spectral characteristic variables, which were used to establish the partial least-squares (PLS) regression and multiple stepwise linear regression (MSLR) inversion models of Cd, Cu, and Zn contents. Based on EEMD-db3 pretreatment, the inversion model of Zn in the dry (fresh) state had R2 values of 0.884 (0.880), NRMSE values of 0.179 (0.253) and RPD values of 3.191 (3.221), indicating excellent stability and predictive performance. The findings of this study can not only aid in the rapid nondestructive detection of heavy metal adsorption in various parts of PsR, but can also be applied to guide the development and use of animal feed.

Published in Remote Sensing

ISSN: 2072-4292 (Online)
Publisher: MDPI AG
Country of publisher: Switzerland
LCC subjects: Science
Website: http://www.mdpi.com/journal/remotesensing/

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