Chemometric Analysis for the Prediction of Biochemical Compounds in Leaves Using UV-VIS-NIR-SWIR Hyperspectroscopy

Falcioni, Renan; Gonçalves, João Vitor Ferreira; de Oliveira, Karym Mayara; de Oliveira, Caio Almeida; Reis, Amanda Silveira; Crusiol, Luis Guilherme Teixeira; Furlanetto, Renato Herrig; Antunes, Werner Camargos; Cezar, Everson; de Oliveira, Roney Berti; Chicati, Marcelo Luiz; Demattê, José Alexandre M.; Nanni, Marcos Rafael

doi:10.3390/plants12193424

Cited by 5 publications

(12 citation statements)

References 52 publications

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“…The approach is based on the principle that heavy metals move from the soil to the plants, accumulating there [ 25 ]. Under the stress of heavy metals, the protein and chlorophyll content of the plants are affected, which leads to a discernible difference in the reflectance spectra [ 26 , 27 ]. Related research has made significant progress.…”

Section: Introductionmentioning

confidence: 99%

Indirect Estimation of Heavy Metal Contamination in Rice Soil Using Spectral Techniques

Zhong,

Yang,

Rong

et al. 2024

Plants

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The rapid growth of industrialization and urbanization in China has led to an increase in soil heavy metal pollution, which poses a serious threat to ecosystem safety and human health. The advancement of spectral technology offers a way to rapidly and non-destructively monitor soil heavy metal content. In order to explore the potential of rice leaf spectra to indirectly estimate soil heavy metal content. We collected farmland soil samples and measured rice leaf spectra in Xushe Town, Yixing City, Jiangsu Province, China. In the laboratory, the heavy metals Cd and As were determined. In order to establish an estimation model between the pre-processed spectra and the soil heavy metals Cd and As content, a genetic algorithm (GA) was used to optimise the partial least squares regression (PLSR). The model’s accuracy was evaluated and the best estimation model was obtained. The results showed that spectral pre-processing techniques can extract hidden information from the spectra. The first-order derivative of absorbance was more effective in extracting spectral sensitive information from rice leaf spectra. The GA-PLSR model selects only about 10% of the bands and has better accuracy in spectral modeling than the PLSR model. The spectral reflectance of rice leaves has the capacity to estimate Cd content in the soil (relative percent difference [RPD] = 2.09) and a good capacity to estimate As content in the soil (RPD = 2.97). Therefore, the content of the heavy metals Cd and As in the soil can be estimated indirectly from the spectral data of rice leaves. This study provides a reference for future remote sensing monitoring of soil heavy metal pollution in farmland that is quantitative, dynamic, and non-destructive over a large area.

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Section: Introductionmentioning

confidence: 99%

Indirect Estimation of Heavy Metal Contamination in Rice Soil Using Spectral Techniques

Zhong,

Yang,

Rong

et al. 2024

Plants

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“…With the intricate cellular and foliar architecture of plants, an array of chloroplastic pigments, such as chlorophylls and carotenoids, and non-chloroplastic compounds, including anthocyanins, flavonoids, and phenolic compounds, have been identified [22][23][24]. Moreover, structural components such as lignin and cellulose play critical roles in the biomechanical integrity of plant tissues.…”

Section: Introductionmentioning

confidence: 99%

“…Moreover, structural components such as lignin and cellulose play critical roles in the biomechanical integrity of plant tissues. The concentrations of these biochemical constituents can vary significantly and are influenced by factors such as the expansion of leaf area, leaf thickness, and the number and structural alterations of cells [21,23,25]. These variations are pivotal because they reflect the biophysical and biochemical dynamics within the plant and offer insights into its adaptive responses to environmental conditions.…”

Section: Introductionmentioning

confidence: 99%

“…This choice is vital because it balances the maximisation of information retention with minimising noise. The β-loadings obtained from PCA provide a clear illustration of the spectral peaks, offering "fingerprints" of the chemical and physical properties within the dataset [22,23]. Furthermore, PCA can be combined with regression coefficients obtained from various spectral curves, such as OJIP, which represent the fluorescence kinetics over the first few seconds of illumination, and hyperspectral reflectance, transmittance, and absorbance curves.…”

Section: Introductionmentioning

confidence: 99%

“…By simplifying complex spectral data into understandable indices, HVIs enable the effective large-scale surveillance and mapping of more relevant parameters. These indices can be used to systematically identify areas of specific interest wavelengths by locating regions of interest in which key biochemical and biophysical components or fingerprint indicators show significant changes [22,23]. For example, specific HVIs can be designed to detect changes in chlorophyll content, water stress, and nutrient levels [25,29,[36][37][38].…”

Section: Introductionmentioning

confidence: 99%

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Estimation of Biochemical Compounds in Tradescantia Leaves Using VIS-NIR-SWIR Hyperspectral and Chlorophyll a Fluorescence Sensors

Falcioni,

Oliveira,

Chicati

et al. 2024

Remote Sensing

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An integrated approach that utilises hyperspectral and chlorophyll a fluorescence sensors to predict biochemical and biophysical parameters represents a new generation of remote-sensing research. The main objective of this study was to obtain a detailed spectral profile that correlates with plant physiology, thereby enhancing our understanding and management of plant health, pigment profiles, and compound fingerprints. Leveraging datasets using non-imaging or passive hyperspectral and chlorophyll fluorescence sensors to collect data in Tradescantia species demonstrated significant differences in leaf characteristics with pigment concentrations and structural components. The main goal was to use principal component analysis (PCA) and partial least squares regression (PLS) methods to analyse the variations in their spectra. Our findings demonstrate a strong correlation between hyperspectral data and chlorophyll fluorescence, which is further supported by the development of hyperspectral vegetation indices (HVIs) that can accurately evaluate fingerprints and predict many compounds in variegated leaves. The higher the integrated analytical approach and its potential application in HVIs and fingerprints, the better the selection of wavelengths and sensor positions for rapid and accurate analysis of many different compounds in leaves. Nonetheless, limitations arose from the specificity of the data for the Tradescantia species, warranting further research across diverse plant types and compounds in the leaves. Overall, this study paves the way for more sustainable and informed agricultural practices through breakthroughs in the application of sensors to remote-sensing technologies.

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Integrating Spectral Sensing and Systems Biology for Precision Viticulture: Effects of Shade Nets on Grapevine Leaves

Tosin,

Portis,

Rodrigues

et al. 2024

Horticulturae

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This study investigates how grapevines (Vitis vinifera L.) respond to shading induced by artificial nets, focusing on physiological and metabolic changes. Through a multidisciplinary approach, grapevines’ adaptations to shading are presented via biochemical analyses and hyperspectral data that are then combined with systems biology techniques. In the study, conducted in a ‘Moscatel Galego Branco’ vineyard in Portugal’s Douro Wine Region during post-veraison, shading was applied and predawn leaf water potential (Ψpd) was then measured to assess water stress. Biochemical analyses and hyperspectral data were integrated to explore adaptations to shading, revealing higher chlorophyll levels (chlorophyll a-b 117.39% higher) and increased Reactive Oxygen Species (ROS) levels in unshaded vines (52.10% higher). Using a self-learning artificial intelligence algorithm (SL-AI), simulations highlighted ROS’s role in stress response and accurately predicted chlorophyll a (R2: 0.92, MAPE: 24.39%), chlorophyll b (R2: 0.96, MAPE: 17.61%), and ROS levels (R2: 0.76, MAPE: 52.17%). In silico simulations employing flux balance analysis (FBA) elucidated distinct metabolic phenotypes between shaded and unshaded vines across cellular compartments. Integrating these findings provides a systems biology approach for understanding grapevine responses to environmental stressors. The leveraging of advanced omics technologies and precise metabolic models holds immense potential for untangling grapevine metabolism and optimizing viticultural practices for enhanced productivity and quality.

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Chemometric Analysis for the Prediction of Biochemical Compounds in Leaves Using UV-VIS-NIR-SWIR Hyperspectroscopy

Cited by 5 publications

References 52 publications

Indirect Estimation of Heavy Metal Contamination in Rice Soil Using Spectral Techniques

Indirect Estimation of Heavy Metal Contamination in Rice Soil Using Spectral Techniques

Estimation of Biochemical Compounds in Tradescantia Leaves Using VIS-NIR-SWIR Hyperspectral and Chlorophyll a Fluorescence Sensors

Integrating Spectral Sensing and Systems Biology for Precision Viticulture: Effects of Shade Nets on Grapevine Leaves

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