Autofluorescence-spectral imaging as an innovative method for rapid, non-destructive and reliable assessing of soybean seed quality

Abstract In the agricultural industry, advances in optical imaging technologies based on rapid and non-destructive approaches have contributed to increase food production for the growing population. The present study employed autofluorescence-spectral imaging and machine learning algorithms to devel...

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Autores principales:	Clíssia Barboza da Silva, Nielsen Moreira Oliveira, Marcia Eugenia Amaral de Carvalho, André Dantas de Medeiros, Marina de Lima Nogueira, André Rodrigues dos Reis
Formato:	article
Lenguaje:	EN
Publicado:	Nature Portfolio 2021
Materias:	Medicine R Science Q
Acceso en línea:	https://doaj.org/article/a90f3172aea8441bab2fc16a922dcbab
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spelling	oai:doaj.org-article:a90f3172aea8441bab2fc16a922dcbab2021-12-02T17:41:13ZAutofluorescence-spectral imaging as an innovative method for rapid, non-destructive and reliable assessing of soybean seed quality10.1038/s41598-021-97223-52045-2322https://doaj.org/article/a90f3172aea8441bab2fc16a922dcbab2021-09-01T00:00:00Zhttps://doi.org/10.1038/s41598-021-97223-5https://doaj.org/toc/2045-2322Abstract In the agricultural industry, advances in optical imaging technologies based on rapid and non-destructive approaches have contributed to increase food production for the growing population. The present study employed autofluorescence-spectral imaging and machine learning algorithms to develop distinct models for classification of soybean seeds differing in physiological quality after artificial aging. Autofluorescence signals from the 365/400 nm excitation-emission combination (that exhibited a perfect correlation with the total phenols in the embryo) were efficiently able to segregate treatments. Furthermore, it was also possible to demonstrate a strong correlation between autofluorescence-spectral data and several quality indicators, such as early germination and seed tolerance to stressful conditions. The machine learning models developed based on artificial neural network, support vector machine or linear discriminant analysis showed high performance (0.99 accuracy) for classifying seeds with different quality levels. Taken together, our study shows that the physiological potential of soybean seeds is reduced accompanied by changes in the concentration and, probably in the structure of autofluorescent compounds. In addition, altering the autofluorescent properties in seeds impact the photosynthesis apparatus in seedlings. From the practical point of view, autofluorescence-based imaging can be used to check modifications in the optical properties of soybean seed tissues and to consistently discriminate high-and low-vigor seeds.Clíssia Barboza da SilvaNielsen Moreira OliveiraMarcia Eugenia Amaral de CarvalhoAndré Dantas de MedeirosMarina de Lima NogueiraAndré Rodrigues dos ReisNature PortfolioarticleMedicineRScienceQENScientific Reports, Vol 11, Iss 1, Pp 1-12 (2021)
institution	DOAJ
collection	DOAJ
language	EN
topic	Medicine R Science Q
spellingShingle	Medicine R Science Q Clíssia Barboza da Silva Nielsen Moreira Oliveira Marcia Eugenia Amaral de Carvalho André Dantas de Medeiros Marina de Lima Nogueira André Rodrigues dos Reis Autofluorescence-spectral imaging as an innovative method for rapid, non-destructive and reliable assessing of soybean seed quality
description	Abstract In the agricultural industry, advances in optical imaging technologies based on rapid and non-destructive approaches have contributed to increase food production for the growing population. The present study employed autofluorescence-spectral imaging and machine learning algorithms to develop distinct models for classification of soybean seeds differing in physiological quality after artificial aging. Autofluorescence signals from the 365/400 nm excitation-emission combination (that exhibited a perfect correlation with the total phenols in the embryo) were efficiently able to segregate treatments. Furthermore, it was also possible to demonstrate a strong correlation between autofluorescence-spectral data and several quality indicators, such as early germination and seed tolerance to stressful conditions. The machine learning models developed based on artificial neural network, support vector machine or linear discriminant analysis showed high performance (0.99 accuracy) for classifying seeds with different quality levels. Taken together, our study shows that the physiological potential of soybean seeds is reduced accompanied by changes in the concentration and, probably in the structure of autofluorescent compounds. In addition, altering the autofluorescent properties in seeds impact the photosynthesis apparatus in seedlings. From the practical point of view, autofluorescence-based imaging can be used to check modifications in the optical properties of soybean seed tissues and to consistently discriminate high-and low-vigor seeds.
format	article
author	Clíssia Barboza da Silva Nielsen Moreira Oliveira Marcia Eugenia Amaral de Carvalho André Dantas de Medeiros Marina de Lima Nogueira André Rodrigues dos Reis
author_facet	Clíssia Barboza da Silva Nielsen Moreira Oliveira Marcia Eugenia Amaral de Carvalho André Dantas de Medeiros Marina de Lima Nogueira André Rodrigues dos Reis
author_sort	Clíssia Barboza da Silva
title	Autofluorescence-spectral imaging as an innovative method for rapid, non-destructive and reliable assessing of soybean seed quality
title_short	Autofluorescence-spectral imaging as an innovative method for rapid, non-destructive and reliable assessing of soybean seed quality
title_full	Autofluorescence-spectral imaging as an innovative method for rapid, non-destructive and reliable assessing of soybean seed quality
title_fullStr	Autofluorescence-spectral imaging as an innovative method for rapid, non-destructive and reliable assessing of soybean seed quality
title_full_unstemmed	Autofluorescence-spectral imaging as an innovative method for rapid, non-destructive and reliable assessing of soybean seed quality
title_sort	autofluorescence-spectral imaging as an innovative method for rapid, non-destructive and reliable assessing of soybean seed quality
publisher	Nature Portfolio
publishDate	2021
url	https://doaj.org/article/a90f3172aea8441bab2fc16a922dcbab
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Autofluorescence-spectral imaging as an innovative method for rapid, non-destructive and reliable assessing of soybean seed quality

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