A Novel Method for Estimating Chlorophyll and Carotenoid Concentrations in Leaves: A Two Hyperspectral Sensor Approach
Leaf optical properties can be used to identify environmental conditions, the effect of light intensities, plant hormone levels, pigment concentrations, and cellular structures. However, the reflectance factors can affect the accuracy of predictions for chlorophyll and carotenoid concentrations. In...
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MDPI AG
2023-04-01
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Online Access: | https://www.mdpi.com/1424-8220/23/8/3843 |
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author | Renan Falcioni Werner Camargos Antunes José Alexandre Melo Demattê Marcos Rafael Nanni |
author_facet | Renan Falcioni Werner Camargos Antunes José Alexandre Melo Demattê Marcos Rafael Nanni |
author_sort | Renan Falcioni |
collection | DOAJ |
description | Leaf optical properties can be used to identify environmental conditions, the effect of light intensities, plant hormone levels, pigment concentrations, and cellular structures. However, the reflectance factors can affect the accuracy of predictions for chlorophyll and carotenoid concentrations. In this study, we tested the hypothesis that technology using two hyperspectral sensors for both reflectance and absorbance data would result in more accurate predictions of absorbance spectra. Our findings indicated that the green/yellow regions (500–600 nm) had a greater impact on photosynthetic pigment predictions, while the blue (440–485 nm) and red (626–700 nm) regions had a minor impact. Strong correlations were found between absorbance (R<sup>2</sup> = 0.87 and 0.91) and reflectance (R<sup>2</sup> = 0.80 and 0.78) for chlorophyll and carotenoids, respectively. Carotenoids showed particularly high and significant correlation coefficients using the partial least squares regression (PLSR) method (R<sup>2</sup><sub>C</sub> = 0.91, R<sup>2</sup>cv = 0.85, and R<sup>2</sup><sub>P</sub> = 0.90) when associated with hyperspectral absorbance data. Our hypothesis was supported, and these results demonstrate the effectiveness of using two hyperspectral sensors for optical leaf profile analysis and predicting the concentration of photosynthetic pigments using multivariate statistical methods. This method for two sensors is more efficient and shows better results compared to traditional single sensor techniques for measuring chloroplast changes and pigment phenotyping in plants. |
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spelling | doaj.art-803c9ad6a5b44e1fbf1a2022dbc9c28f2023-11-17T21:15:24ZengMDPI AGSensors1424-82202023-04-01238384310.3390/s23083843A Novel Method for Estimating Chlorophyll and Carotenoid Concentrations in Leaves: A Two Hyperspectral Sensor ApproachRenan Falcioni0Werner Camargos Antunes1José Alexandre Melo Demattê2Marcos Rafael Nanni3Department of Agronomy, State University of Maringa, Av. Colombo, 5790, Maringa 87020-900, Parana, BrazilDepartment of Agronomy, State University of Maringa, Av. Colombo, 5790, Maringa 87020-900, Parana, BrazilDepartment of Soil Science, Luiz de Queiroz College of Agriculture, University of Sao Paulo, Av. Padua Dias, 11, Piracicaba 13418-260, Sao Paulo, BrazilDepartment of Agronomy, State University of Maringa, Av. Colombo, 5790, Maringa 87020-900, Parana, BrazilLeaf optical properties can be used to identify environmental conditions, the effect of light intensities, plant hormone levels, pigment concentrations, and cellular structures. However, the reflectance factors can affect the accuracy of predictions for chlorophyll and carotenoid concentrations. In this study, we tested the hypothesis that technology using two hyperspectral sensors for both reflectance and absorbance data would result in more accurate predictions of absorbance spectra. Our findings indicated that the green/yellow regions (500–600 nm) had a greater impact on photosynthetic pigment predictions, while the blue (440–485 nm) and red (626–700 nm) regions had a minor impact. Strong correlations were found between absorbance (R<sup>2</sup> = 0.87 and 0.91) and reflectance (R<sup>2</sup> = 0.80 and 0.78) for chlorophyll and carotenoids, respectively. Carotenoids showed particularly high and significant correlation coefficients using the partial least squares regression (PLSR) method (R<sup>2</sup><sub>C</sub> = 0.91, R<sup>2</sup>cv = 0.85, and R<sup>2</sup><sub>P</sub> = 0.90) when associated with hyperspectral absorbance data. Our hypothesis was supported, and these results demonstrate the effectiveness of using two hyperspectral sensors for optical leaf profile analysis and predicting the concentration of photosynthetic pigments using multivariate statistical methods. This method for two sensors is more efficient and shows better results compared to traditional single sensor techniques for measuring chloroplast changes and pigment phenotyping in plants.https://www.mdpi.com/1424-8220/23/8/3843cellular structureschlorophyll and carotenoidsleaf optical propertiesleaf thicknesspartial least squares regressiontransmission electron microscopy |
spellingShingle | Renan Falcioni Werner Camargos Antunes José Alexandre Melo Demattê Marcos Rafael Nanni A Novel Method for Estimating Chlorophyll and Carotenoid Concentrations in Leaves: A Two Hyperspectral Sensor Approach Sensors cellular structures chlorophyll and carotenoids leaf optical properties leaf thickness partial least squares regression transmission electron microscopy |
title | A Novel Method for Estimating Chlorophyll and Carotenoid Concentrations in Leaves: A Two Hyperspectral Sensor Approach |
title_full | A Novel Method for Estimating Chlorophyll and Carotenoid Concentrations in Leaves: A Two Hyperspectral Sensor Approach |
title_fullStr | A Novel Method for Estimating Chlorophyll and Carotenoid Concentrations in Leaves: A Two Hyperspectral Sensor Approach |
title_full_unstemmed | A Novel Method for Estimating Chlorophyll and Carotenoid Concentrations in Leaves: A Two Hyperspectral Sensor Approach |
title_short | A Novel Method for Estimating Chlorophyll and Carotenoid Concentrations in Leaves: A Two Hyperspectral Sensor Approach |
title_sort | novel method for estimating chlorophyll and carotenoid concentrations in leaves a two hyperspectral sensor approach |
topic | cellular structures chlorophyll and carotenoids leaf optical properties leaf thickness partial least squares regression transmission electron microscopy |
url | https://www.mdpi.com/1424-8220/23/8/3843 |
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