Cargando…
Non-destructive measurement of total phenolic compounds in Arabidopsis under various stress conditions
Quantifying the phenolic compounds in plants is essential for maintaining the beneficial effects of plants on human health. Existing measurement methods are destructive and/or time consuming. To overcome these issues, research was conducted to develop a non-destructive and rapid measurement of pheno...
Autores principales: | , , , , , , , |
---|---|
Formato: | Online Artículo Texto |
Lenguaje: | English |
Publicado: |
Frontiers Media S.A.
2022
|
Materias: | |
Acceso en línea: | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC9478847/ https://www.ncbi.nlm.nih.gov/pubmed/36119609 http://dx.doi.org/10.3389/fpls.2022.982247 |
_version_ | 1784790665189654528 |
---|---|
author | Jayapal, Praveen Kumar Joshi, Rahul Sathasivam, Ramaraj Van Nguyen, Bao Faqeerzada, Mohammad Akbar Park, Sang Un Sandanam, Domnic Cho, Byoung-Kwan |
author_facet | Jayapal, Praveen Kumar Joshi, Rahul Sathasivam, Ramaraj Van Nguyen, Bao Faqeerzada, Mohammad Akbar Park, Sang Un Sandanam, Domnic Cho, Byoung-Kwan |
author_sort | Jayapal, Praveen Kumar |
collection | PubMed |
description | Quantifying the phenolic compounds in plants is essential for maintaining the beneficial effects of plants on human health. Existing measurement methods are destructive and/or time consuming. To overcome these issues, research was conducted to develop a non-destructive and rapid measurement of phenolic compounds using hyperspectral imaging (HSI) and machine learning. In this study, the Arabidopsis was used since it is a model plant. They were grown in controlled and various stress conditions (LED lights and drought). Images were captured using HSI in the range of 400–1,000 nm (VIS/NIR) and 900–2,500 nm (SWIR). Initially, the plant region was segmented, and the spectra were extracted from the segmented region. These spectra were synchronized with plants’ total phenolic content reference value, which was obtained from high-performance liquid chromatography (HPLC). The partial least square regression (PLSR) model was applied for total phenolic compound prediction. The best prediction values were achieved with SWIR spectra in comparison with VIS/NIR. Hence, SWIR spectra were further used. Spectral dimensionality reduction was performed based on discrete cosine transform (DCT) coefficients and the prediction was performed. The results were better than that of obtained with original spectra. The proposed model performance yielded R(2)-values of 0.97 and 0.96 for calibration and validation, respectively. The lowest standard errors of predictions (SEP) were 0.05 and 0.07 mg/g. The proposed model out-performed different state-of-the-art methods. These demonstrate the efficiency of the model in quantifying the total phenolic compounds that are present in plants and opens a way to develop a rapid measurement system. |
format | Online Article Text |
id | pubmed-9478847 |
institution | National Center for Biotechnology Information |
language | English |
publishDate | 2022 |
publisher | Frontiers Media S.A. |
record_format | MEDLINE/PubMed |
spelling | pubmed-94788472022-09-17 Non-destructive measurement of total phenolic compounds in Arabidopsis under various stress conditions Jayapal, Praveen Kumar Joshi, Rahul Sathasivam, Ramaraj Van Nguyen, Bao Faqeerzada, Mohammad Akbar Park, Sang Un Sandanam, Domnic Cho, Byoung-Kwan Front Plant Sci Plant Science Quantifying the phenolic compounds in plants is essential for maintaining the beneficial effects of plants on human health. Existing measurement methods are destructive and/or time consuming. To overcome these issues, research was conducted to develop a non-destructive and rapid measurement of phenolic compounds using hyperspectral imaging (HSI) and machine learning. In this study, the Arabidopsis was used since it is a model plant. They were grown in controlled and various stress conditions (LED lights and drought). Images were captured using HSI in the range of 400–1,000 nm (VIS/NIR) and 900–2,500 nm (SWIR). Initially, the plant region was segmented, and the spectra were extracted from the segmented region. These spectra were synchronized with plants’ total phenolic content reference value, which was obtained from high-performance liquid chromatography (HPLC). The partial least square regression (PLSR) model was applied for total phenolic compound prediction. The best prediction values were achieved with SWIR spectra in comparison with VIS/NIR. Hence, SWIR spectra were further used. Spectral dimensionality reduction was performed based on discrete cosine transform (DCT) coefficients and the prediction was performed. The results were better than that of obtained with original spectra. The proposed model performance yielded R(2)-values of 0.97 and 0.96 for calibration and validation, respectively. The lowest standard errors of predictions (SEP) were 0.05 and 0.07 mg/g. The proposed model out-performed different state-of-the-art methods. These demonstrate the efficiency of the model in quantifying the total phenolic compounds that are present in plants and opens a way to develop a rapid measurement system. Frontiers Media S.A. 2022-09-02 /pmc/articles/PMC9478847/ /pubmed/36119609 http://dx.doi.org/10.3389/fpls.2022.982247 Text en Copyright © 2022 Jayapal, Joshi, Sathasivam, Van Nguyen, Faqeerzada, Park, Sandanam and Cho. https://creativecommons.org/licenses/by/4.0/This is an open-access article distributed under the terms of the Creative Commons Attribution License (CC BY). The use, distribution or reproduction in other forums is permitted, provided the original author(s) and the copyright owner(s) are credited and that the original publication in this journal is cited, in accordance with accepted academic practice. No use, distribution or reproduction is permitted which does not comply with these terms. |
spellingShingle | Plant Science Jayapal, Praveen Kumar Joshi, Rahul Sathasivam, Ramaraj Van Nguyen, Bao Faqeerzada, Mohammad Akbar Park, Sang Un Sandanam, Domnic Cho, Byoung-Kwan Non-destructive measurement of total phenolic compounds in Arabidopsis under various stress conditions |
title | Non-destructive measurement of total phenolic compounds in Arabidopsis under various stress conditions |
title_full | Non-destructive measurement of total phenolic compounds in Arabidopsis under various stress conditions |
title_fullStr | Non-destructive measurement of total phenolic compounds in Arabidopsis under various stress conditions |
title_full_unstemmed | Non-destructive measurement of total phenolic compounds in Arabidopsis under various stress conditions |
title_short | Non-destructive measurement of total phenolic compounds in Arabidopsis under various stress conditions |
title_sort | non-destructive measurement of total phenolic compounds in arabidopsis under various stress conditions |
topic | Plant Science |
url | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC9478847/ https://www.ncbi.nlm.nih.gov/pubmed/36119609 http://dx.doi.org/10.3389/fpls.2022.982247 |
work_keys_str_mv | AT jayapalpraveenkumar nondestructivemeasurementoftotalphenoliccompoundsinarabidopsisundervariousstressconditions AT joshirahul nondestructivemeasurementoftotalphenoliccompoundsinarabidopsisundervariousstressconditions AT sathasivamramaraj nondestructivemeasurementoftotalphenoliccompoundsinarabidopsisundervariousstressconditions AT vannguyenbao nondestructivemeasurementoftotalphenoliccompoundsinarabidopsisundervariousstressconditions AT faqeerzadamohammadakbar nondestructivemeasurementoftotalphenoliccompoundsinarabidopsisundervariousstressconditions AT parksangun nondestructivemeasurementoftotalphenoliccompoundsinarabidopsisundervariousstressconditions AT sandanamdomnic nondestructivemeasurementoftotalphenoliccompoundsinarabidopsisundervariousstressconditions AT chobyoungkwan nondestructivemeasurementoftotalphenoliccompoundsinarabidopsisundervariousstressconditions |