A novel spectral analysis method for distinguishing heavy metal stress of maize due to copper and lead: RDA and EMD-PSD

Fu, Pingjie; Zhang, Wei; Yang, Keming; Meng, Fei

doi:10.1016/j.ecoenv.2020.111211

Cited by 25 publications

(10 citation statements)

References 48 publications

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“…The results of this study demonstrate that the MEMD transform can effectively high light characteristic spectra. A possible reason for this is that MEMD, as a time-scale decom position algorithm of signals, can decompose the spectrum in the time domain, and re lated studies have also proved similar conclusions [52]. Relevant research also proves tha signal decomposition in the frequency and time domains can effectively highlight spectra information and obtain spectral characteristics [53,54].…”

Section: Spectral Data Preprocessing and Analysismentioning

confidence: 84%

The New Hyperspectral Analysis Method for Distinguishing the Types of Heavy Metal Copper and Lead Pollution Elements

Zhang

Wang

Yang

et al. 2022

IJERPH

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In recent years, the problem of heavy metal pollution in agriculture caused by industrial development has been particularly prominent, directly affecting food and ecological environmental safety. Hyperspectral remote sensing technology has the advantages of high spectral resolution and nondestructive monitoring. The physiological and biochemical parameters of crops undergo similar changes under different heavy metal stresses. Therefore, it is a great challenge to explore the use of hyperspectral technology to distinguish the types of the heavy metal copper (Cu) and lead (Pb) elements. This is also a hot topic in the current research. In this study, several models are proposed to distinguish copper and lead elements by combining multivariate empirical mode decomposition (MEMD) transformation and machine learning. First, MEMD is introduced to decompose the original spectrum, which effectively removes the noise and highlights and magnifies the weak information of the spectrum. The successive projections algorithm (SPA), competitive adaptive reweighted sampling (CARS), and iteratively retaining informative variables (IRIV) were used to screen the characteristic bands and were combined with extreme learning machine (ELM), support vector machine (SVM), and general regression neural network (GRNN) algorithms to build models to distinguish the types of Cu and Pb elements. The quality of the model was evaluated using accuracy (A), precision (P), recall (R), and F-score. The results showed that the MEMD-SPA-SVM, MEMD-CARS-SVM, MEMD-SPA-ELM, MEMD-CARS-ELM, and MEMD-IRIV-ELM models intuitively and effectively distinguished the types of Cu and Pb elements. Their accuracy and F-scores were all greater than 0.8. To verify the superiority of these models, the same model was constructed based on first derivative (FD) and second derivative (SD) transformations, and the obtained classification and recognition accuracy (A) and F-score were both lower than 0.8, which further confirmed the superiority of the model established after MEMD transformation. The model proposed in this study has great potential for applying hyperspectral technology to distinguish the types of elements contaminated by Cu and Pb in crops.

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Section: Spectral Data Preprocessing and Analysismentioning

confidence: 84%

The New Hyperspectral Analysis Method for Distinguishing the Types of Heavy Metal Copper and Lead Pollution Elements

Zhang

Wang

Yang

et al. 2022

IJERPH

Self Cite

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“…Spectral measurements were performed in a darkroom to control lighting conditions and reduce the effects of stray light ( Wang et al., 2014 ). At the same time, attention should be paid to spectral resolution and determination range ( Fu et al., 2020 ; Zhou et al., 2020b ). When collecting spectra, the surrounding environment should be stable and corrected with a standard reference plate ( Mishra, 2021 ).…”

Section: Basic Steps Of Spectroscopic Detectionmentioning

confidence: 99%

“…HMS influences the growth of plants and physiological parameters (such as chlorophyll content and enzymatic activity), leading to changes in reflection characteristics ( Zhou et al., 2019a ). Therefore, plant heavy metal levels and physiological parameters can be evaluated by analyzing spectral reflection information ( Fu et al., 2020 ; Shen et al., 2020 ). Root, stem, and leaf play an essential role in plant growth, and reflect the growing situation.…”

Section: Spectroscopy For Detecting Plants Heavy Metalsmentioning

confidence: 99%

“…Leaves are crucial indicators that reflect the biochemical composition and health status of vegetation. Getting physiological information about leaves is important to understand plant characteristics ( Fu et al., 2020 ). In particular, detecting leaf physiological information under heavy metal stress by spectral technology is helpful in researching the corresponding mechanism of plants under heavy metal stress.…”

Section: Spectroscopy For Detecting Plants Heavy Metalsmentioning

confidence: 99%

“…Fu et al. ( Fu et al., 2020 ) recognized Cu and Pb in Maize Leaves Based on HSI and analyzed the recognition characteristics of Cu and Pb in the frequency domain. The results show that the red edge and red shoulder region of the spectrum can be used as the characteristic spectrum to distinguish Cu and Pb.…”

Section: Spectroscopy For Detecting Plants Heavy Metalsmentioning

confidence: 99%

See 2 more Smart Citations

Optical imaging spectroscopy coupled with machine learning for detecting heavy metal of plants: A review

Ren

Cui

et al. 2022

Front. Plant Sci.

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Heavy metal elements, which inhibit plant development by destroying cell structure and wilting leaves, are easily absorbed by plants and eventually threaten human health via the food chain. Recently, with the increasing precision and refinement of optical instruments, optical imaging spectroscopy has gradually been applied to the detection and reaction of heavy metals in plants due to its in-situ, real-time, and simple operation compared with traditional chemical analysis methods. Moreover, the emergence of machine learning helps improve detection accuracy, making optical imaging spectroscopy comparable to conventional chemical analysis methods in some situations. This review (a): summarizes the progress of advanced optical imaging spectroscopy techniques coupled with artificial neural network algorithms for plant heavy metal detection over ten years from 2012-2022; (b) briefly describes and compares the principles and characteristics of spectroscopy and traditional chemical techniques applied to plants heavy metal detection, and the advantages of artificial neural network techniques including machine learning and deep learning techniques in combination with spectroscopy; (c) proposes the solutions such as coupling with other analytical and detection methods, portability, to address the challenges of unsatisfactory sensitivity of optical imaging spectroscopy and expensive instruments.

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Non-destructive study on identifying and monitoring of Cu-Pb pollution in corn based on near-infrared spectroscopy

Yang

et al. 2022

Environ Sci Pollut Res

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A novel spectral analysis method for distinguishing heavy metal stress of maize due to copper and lead: RDA and EMD-PSD

Cited by 25 publications

References 48 publications

The New Hyperspectral Analysis Method for Distinguishing the Types of Heavy Metal Copper and Lead Pollution Elements

The New Hyperspectral Analysis Method for Distinguishing the Types of Heavy Metal Copper and Lead Pollution Elements

Optical imaging spectroscopy coupled with machine learning for detecting heavy metal of plants: A review

Non-destructive study on identifying and monitoring of Cu-Pb pollution in corn based on near-infrared spectroscopy

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