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Raman Spectroscopy and Machine Learning for Agricultural Applications: Chemometric Assessment of Spectroscopic Signatures of Plants as the Essential Step Toward Digital Farming
A growing body of evidence suggests that Raman spectroscopy (RS) can be used for diagnostics of plant biotic and abiotic stresses. RS can be also utilized for identification of plant species and their varieties, as well as assessment of the nutritional content and commercial values of seeds. The pow...
Autores principales: | , |
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Formato: | Online Artículo Texto |
Lenguaje: | English |
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Frontiers Media S.A.
2022
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Acceso en línea: | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC9087799/ https://www.ncbi.nlm.nih.gov/pubmed/35557733 http://dx.doi.org/10.3389/fpls.2022.887511 |
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author | Farber, Charles Kurouski, Dmitry |
author_facet | Farber, Charles Kurouski, Dmitry |
author_sort | Farber, Charles |
collection | PubMed |
description | A growing body of evidence suggests that Raman spectroscopy (RS) can be used for diagnostics of plant biotic and abiotic stresses. RS can be also utilized for identification of plant species and their varieties, as well as assessment of the nutritional content and commercial values of seeds. The power of RS in such cases to a large extent depends on chemometric analyses of spectra. In this work, we critically discuss three major approaches that can be used for advanced analyses of spectroscopic data: summary statistics, statistical testing and chemometric classification. On the example of Raman spectra collected from roses, we demonstrate the outcomes and the potential of all three types of spectral analyses. We anticipate that our findings will help to design the most optimal spectral processing and preprocessing that is required to achieved the desired results. We also expect that reported collection of results will be useful to all researchers who work on spectroscopic analyses of plant specimens. |
format | Online Article Text |
id | pubmed-9087799 |
institution | National Center for Biotechnology Information |
language | English |
publishDate | 2022 |
publisher | Frontiers Media S.A. |
record_format | MEDLINE/PubMed |
spelling | pubmed-90877992022-05-11 Raman Spectroscopy and Machine Learning for Agricultural Applications: Chemometric Assessment of Spectroscopic Signatures of Plants as the Essential Step Toward Digital Farming Farber, Charles Kurouski, Dmitry Front Plant Sci Plant Science A growing body of evidence suggests that Raman spectroscopy (RS) can be used for diagnostics of plant biotic and abiotic stresses. RS can be also utilized for identification of plant species and their varieties, as well as assessment of the nutritional content and commercial values of seeds. The power of RS in such cases to a large extent depends on chemometric analyses of spectra. In this work, we critically discuss three major approaches that can be used for advanced analyses of spectroscopic data: summary statistics, statistical testing and chemometric classification. On the example of Raman spectra collected from roses, we demonstrate the outcomes and the potential of all three types of spectral analyses. We anticipate that our findings will help to design the most optimal spectral processing and preprocessing that is required to achieved the desired results. We also expect that reported collection of results will be useful to all researchers who work on spectroscopic analyses of plant specimens. Frontiers Media S.A. 2022-04-26 /pmc/articles/PMC9087799/ /pubmed/35557733 http://dx.doi.org/10.3389/fpls.2022.887511 Text en Copyright © 2022 Farber and Kurouski. 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 Farber, Charles Kurouski, Dmitry Raman Spectroscopy and Machine Learning for Agricultural Applications: Chemometric Assessment of Spectroscopic Signatures of Plants as the Essential Step Toward Digital Farming |
title | Raman Spectroscopy and Machine Learning for Agricultural Applications: Chemometric Assessment of Spectroscopic Signatures of Plants as the Essential Step Toward Digital Farming |
title_full | Raman Spectroscopy and Machine Learning for Agricultural Applications: Chemometric Assessment of Spectroscopic Signatures of Plants as the Essential Step Toward Digital Farming |
title_fullStr | Raman Spectroscopy and Machine Learning for Agricultural Applications: Chemometric Assessment of Spectroscopic Signatures of Plants as the Essential Step Toward Digital Farming |
title_full_unstemmed | Raman Spectroscopy and Machine Learning for Agricultural Applications: Chemometric Assessment of Spectroscopic Signatures of Plants as the Essential Step Toward Digital Farming |
title_short | Raman Spectroscopy and Machine Learning for Agricultural Applications: Chemometric Assessment of Spectroscopic Signatures of Plants as the Essential Step Toward Digital Farming |
title_sort | raman spectroscopy and machine learning for agricultural applications: chemometric assessment of spectroscopic signatures of plants as the essential step toward digital farming |
topic | Plant Science |
url | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC9087799/ https://www.ncbi.nlm.nih.gov/pubmed/35557733 http://dx.doi.org/10.3389/fpls.2022.887511 |
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