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A Method to Evaluate Spectral Analysis by Spectroscopy
Visible and near infrared spectroscopy has been widely used to develop a method for rapidly determining organic carbon in soils or sediments (SOC). Most of these studies concentrated on how to establish a good spectral model but ignored how to evaluate the method, such as the use of detection range...
Autores principales: | , , , , |
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Formato: | Online Artículo Texto |
Lenguaje: | English |
Publicado: |
MDPI
2022
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Materias: | |
Acceso en línea: | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC9371028/ https://www.ncbi.nlm.nih.gov/pubmed/35957195 http://dx.doi.org/10.3390/s22155638 |
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author | Liu, Yan Fan, Pingping Qiu, Huimin Li, Xueying Hou, Guangli |
author_facet | Liu, Yan Fan, Pingping Qiu, Huimin Li, Xueying Hou, Guangli |
author_sort | Liu, Yan |
collection | PubMed |
description | Visible and near infrared spectroscopy has been widely used to develop a method for rapidly determining organic carbon in soils or sediments (SOC). Most of these studies concentrated on how to establish a good spectral model but ignored how to evaluate the method, such as the use of detection range (max and min), resolution and error for SOC spectral analysis. Here, we proposed a method to evaluate the spectral analysis of SOC. Using 96 sediments sampled in the Yellow Sea and Bohai Sea, China, we established three spectral models of SOC after collecting their spectral reflectance by Agilent Cary 5000, ASD FieldSpec 4 and Ocean Optics QEPro, respectively. For both the calibration set and validation set in each spectrometer, the predicted SOC concentrations followed a distribution curve (function), in which the x-axis was the SOC concentrations. Using these curves, we developed these four technical parameters. The detection ranges were the SOC concentrations where the curve was near to or crossing with the lateral axis, while the detection resolution was the average difference between the two neighboring SOC concentrations. The detection errors were the differences between the predicted SOC and the measured SOC. Results showed that these technical parameters were better in the bench-top spectrometer (Cary 5000) than those in the portable spectrometers when analyzing the same samples. For the portable spectrometers, QEPro had a broader detection range and more consistent detection error than FieldSpec 4, suggesting that the low-cost QEPro performed as well as the high-cost FieldSpec 4. This study provides a good example for evaluating spectral analysis by spectroscopy, which can support the development of the spectral method. |
format | Online Article Text |
id | pubmed-9371028 |
institution | National Center for Biotechnology Information |
language | English |
publishDate | 2022 |
publisher | MDPI |
record_format | MEDLINE/PubMed |
spelling | pubmed-93710282022-08-12 A Method to Evaluate Spectral Analysis by Spectroscopy Liu, Yan Fan, Pingping Qiu, Huimin Li, Xueying Hou, Guangli Sensors (Basel) Article Visible and near infrared spectroscopy has been widely used to develop a method for rapidly determining organic carbon in soils or sediments (SOC). Most of these studies concentrated on how to establish a good spectral model but ignored how to evaluate the method, such as the use of detection range (max and min), resolution and error for SOC spectral analysis. Here, we proposed a method to evaluate the spectral analysis of SOC. Using 96 sediments sampled in the Yellow Sea and Bohai Sea, China, we established three spectral models of SOC after collecting their spectral reflectance by Agilent Cary 5000, ASD FieldSpec 4 and Ocean Optics QEPro, respectively. For both the calibration set and validation set in each spectrometer, the predicted SOC concentrations followed a distribution curve (function), in which the x-axis was the SOC concentrations. Using these curves, we developed these four technical parameters. The detection ranges were the SOC concentrations where the curve was near to or crossing with the lateral axis, while the detection resolution was the average difference between the two neighboring SOC concentrations. The detection errors were the differences between the predicted SOC and the measured SOC. Results showed that these technical parameters were better in the bench-top spectrometer (Cary 5000) than those in the portable spectrometers when analyzing the same samples. For the portable spectrometers, QEPro had a broader detection range and more consistent detection error than FieldSpec 4, suggesting that the low-cost QEPro performed as well as the high-cost FieldSpec 4. This study provides a good example for evaluating spectral analysis by spectroscopy, which can support the development of the spectral method. MDPI 2022-07-28 /pmc/articles/PMC9371028/ /pubmed/35957195 http://dx.doi.org/10.3390/s22155638 Text en © 2022 by the authors. https://creativecommons.org/licenses/by/4.0/Licensee MDPI, Basel, Switzerland. This article is an open access article distributed under the terms and conditions of the Creative Commons Attribution (CC BY) license (https://creativecommons.org/licenses/by/4.0/). |
spellingShingle | Article Liu, Yan Fan, Pingping Qiu, Huimin Li, Xueying Hou, Guangli A Method to Evaluate Spectral Analysis by Spectroscopy |
title | A Method to Evaluate Spectral Analysis by Spectroscopy |
title_full | A Method to Evaluate Spectral Analysis by Spectroscopy |
title_fullStr | A Method to Evaluate Spectral Analysis by Spectroscopy |
title_full_unstemmed | A Method to Evaluate Spectral Analysis by Spectroscopy |
title_short | A Method to Evaluate Spectral Analysis by Spectroscopy |
title_sort | method to evaluate spectral analysis by spectroscopy |
topic | Article |
url | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC9371028/ https://www.ncbi.nlm.nih.gov/pubmed/35957195 http://dx.doi.org/10.3390/s22155638 |
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