Potential of spectroscopic analyses for non-destructive estimation of tea quality-related metabolites in fresh new leaves

Abstract Spectroscopic sensing provides physical and chemical information in a non-destructive and rapid manner. To develop non-destructive estimation methods of tea quality-related metabolites in fresh leaves, we estimated the contents of free amino acids, catechins, and caffeine in fresh tea leave...

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Autores principales:	Hiroto Yamashita, Rei Sonobe, Yuhei Hirono, Akio Morita, Takashi Ikka
Formato:	article
Lenguaje:	EN
Publicado:	Nature Portfolio 2021
Materias:	Medicine R Science Q
Acceso en línea:	https://doaj.org/article/26064254ec2042eaaada976da0d832ac
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spelling	oai:doaj.org-article:26064254ec2042eaaada976da0d832ac2021-12-02T12:11:52ZPotential of spectroscopic analyses for non-destructive estimation of tea quality-related metabolites in fresh new leaves10.1038/s41598-021-83847-02045-2322https://doaj.org/article/26064254ec2042eaaada976da0d832ac2021-02-01T00:00:00Zhttps://doi.org/10.1038/s41598-021-83847-0https://doaj.org/toc/2045-2322Abstract Spectroscopic sensing provides physical and chemical information in a non-destructive and rapid manner. To develop non-destructive estimation methods of tea quality-related metabolites in fresh leaves, we estimated the contents of free amino acids, catechins, and caffeine in fresh tea leaves using visible to short-wave infrared hyperspectral reflectance data and machine learning algorithms. We acquired these data from approximately 200 new leaves with various status and then constructed the regression model in the combination of six spectral patterns with pre-processing and five algorithms. In most phenotypes, the combination of de-trending pre-processing and Cubist algorithms was robustly selected as the best combination in each round over 100 repetitions that were evaluated based on the ratio of performance to deviation (RPD) values. The mean RPD values were ranged from 1.1 to 2.7 and most of them were above the acceptable or accurate threshold (RPD = 1.4 or 2.0, respectively). Data-based sensitivity analysis identified the important hyperspectral regions around 1500 and 2000 nm. Present spectroscopic approaches indicate that most tea quality-related metabolites can be estimated non-destructively, and pre-processing techniques help to improve its accuracy.Hiroto YamashitaRei SonobeYuhei HironoAkio MoritaTakashi IkkaNature PortfolioarticleMedicineRScienceQENScientific Reports, Vol 11, Iss 1, Pp 1-11 (2021)
institution	DOAJ
collection	DOAJ
language	EN
topic	Medicine R Science Q
spellingShingle	Medicine R Science Q Hiroto Yamashita Rei Sonobe Yuhei Hirono Akio Morita Takashi Ikka Potential of spectroscopic analyses for non-destructive estimation of tea quality-related metabolites in fresh new leaves
description	Abstract Spectroscopic sensing provides physical and chemical information in a non-destructive and rapid manner. To develop non-destructive estimation methods of tea quality-related metabolites in fresh leaves, we estimated the contents of free amino acids, catechins, and caffeine in fresh tea leaves using visible to short-wave infrared hyperspectral reflectance data and machine learning algorithms. We acquired these data from approximately 200 new leaves with various status and then constructed the regression model in the combination of six spectral patterns with pre-processing and five algorithms. In most phenotypes, the combination of de-trending pre-processing and Cubist algorithms was robustly selected as the best combination in each round over 100 repetitions that were evaluated based on the ratio of performance to deviation (RPD) values. The mean RPD values were ranged from 1.1 to 2.7 and most of them were above the acceptable or accurate threshold (RPD = 1.4 or 2.0, respectively). Data-based sensitivity analysis identified the important hyperspectral regions around 1500 and 2000 nm. Present spectroscopic approaches indicate that most tea quality-related metabolites can be estimated non-destructively, and pre-processing techniques help to improve its accuracy.
format	article
author	Hiroto Yamashita Rei Sonobe Yuhei Hirono Akio Morita Takashi Ikka
author_facet	Hiroto Yamashita Rei Sonobe Yuhei Hirono Akio Morita Takashi Ikka
author_sort	Hiroto Yamashita
title	Potential of spectroscopic analyses for non-destructive estimation of tea quality-related metabolites in fresh new leaves
title_short	Potential of spectroscopic analyses for non-destructive estimation of tea quality-related metabolites in fresh new leaves
title_full	Potential of spectroscopic analyses for non-destructive estimation of tea quality-related metabolites in fresh new leaves
title_fullStr	Potential of spectroscopic analyses for non-destructive estimation of tea quality-related metabolites in fresh new leaves
title_full_unstemmed	Potential of spectroscopic analyses for non-destructive estimation of tea quality-related metabolites in fresh new leaves
title_sort	potential of spectroscopic analyses for non-destructive estimation of tea quality-related metabolites in fresh new leaves
publisher	Nature Portfolio
publishDate	2021
url	https://doaj.org/article/26064254ec2042eaaada976da0d832ac
work_keys_str_mv	AT hirotoyamashita potentialofspectroscopicanalysesfornondestructiveestimationofteaqualityrelatedmetabolitesinfreshnewleaves AT reisonobe potentialofspectroscopicanalysesfornondestructiveestimationofteaqualityrelatedmetabolitesinfreshnewleaves AT yuheihirono potentialofspectroscopicanalysesfornondestructiveestimationofteaqualityrelatedmetabolitesinfreshnewleaves AT akiomorita potentialofspectroscopicanalysesfornondestructiveestimationofteaqualityrelatedmetabolitesinfreshnewleaves AT takashiikka potentialofspectroscopicanalysesfornondestructiveestimationofteaqualityrelatedmetabolitesinfreshnewleaves
_version_	1718394561309442048

Potential of spectroscopic analyses for non-destructive estimation of tea quality-related metabolites in fresh new leaves

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