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

Abstract

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.

Simultaneous Rapid Detection of Multiple Physicochemical Properties of Jet Fuel Using Near-Infrared Spectroscopy

“…In this experiment, four methods (SG smoothing, SG first D, SG second D, and MSC) were employed for preprocessing the spectral data. SG smoothing was applied to remove fluctuations caused by noise in the spectra and produced a more compact NIR spectrum (Figure a). , Other than the primary absorption peaks, the fluctuations of the miscellaneous peaks at 1150 and 1500 nm became smoother, indicating that SG smoothing effectively reduced the spectral fluctuations caused by environmental factors that affected the instrument function. SG first D and SG second D are baseline correction methods that aim to eliminate the spectral baseline shift caused by instrument and sample background factors, while enhancing the distinguishable differences between samples.…”

Hyperspectral Prediction Model of Nitrogen Content in Citrus Leaves Based on the CEEMDAN–SR Algorithm

“…The EMD algorithm is based on the scale of the data itself, but there is a mode mixing problem. As a result, many researchers have focused on improving the EMD algorithm.Tang et al [19] proposed a preprocessed algorithm with EEMD-db3 for noise reduction to process PsR spectral data. The method effectively improves the signal-to-noise ratio and enhances the relationship between the spectral data and heavy metal content.…”