Discrimination of <i<Brassica juncea</i< Varieties Using Visible Near-Infrared (Vis-NIR) Spectroscopy and Chemometrics Methods

Brown mustard (<i<Brassica juncea</i< (L.) is an important oilseed crop that is mostly used to produce edible oils, industrial oils, modified lipids and biofuels in subtropical nations. Due to its higher level of commercial use, the species has a huge array of varieties/cultivars. The pu...
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Autor*in:	Soo-In Sohn [verfasserIn] Subramani Pandian [verfasserIn] Young-Ju Oh [verfasserIn] John-Lewis Zinia Zaukuu [verfasserIn] Yong-Ho Lee [verfasserIn] Eun-Kyoung Shin [verfasserIn]

Format:	E-Artikel
Sprache:	Englisch

Erschienen:	2022

Schlagwörter:	visible near-infrared spectroscopy deep learning machine learning variety discrimination

Übergeordnetes Werk:	In: International Journal of Molecular Sciences - MDPI AG, 2003, 23(2022), 21, p 12809
Übergeordnetes Werk:	volume:23 ; year:2022 ; number:21, p 12809

Links:	Link aufrufen Link aufrufen Link aufrufen Journal toc Journal toc

DOI / URN:	10.3390/ijms232112809

Katalog-ID:	DOAJ083877967

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allfields	10.3390/ijms232112809 doi (DE-627)DOAJ083877967 (DE-599)DOAJe0d5a02fe01c4943b874080004fdd2ab DE-627 ger DE-627 rakwb eng QH301-705.5 QD1-999 Soo-In Sohn verfasserin aut Discrimination of <i<Brassica juncea</i< Varieties Using Visible Near-Infrared (Vis-NIR) Spectroscopy and Chemometrics Methods 2022 Text txt rdacontent Computermedien c rdamedia Online-Ressource cr rdacarrier Brown mustard (<i<Brassica juncea</i< (L.) is an important oilseed crop that is mostly used to produce edible oils, industrial oils, modified lipids and biofuels in subtropical nations. Due to its higher level of commercial use, the species has a huge array of varieties/cultivars. The purpose of this study is to evaluate the use of visible near-infrared (Vis-NIR) spectroscopy in combination with multiple chemometric approaches for distinguishing four <i<B. juncea</i< varieties in Korea. The spectra from the leaves of four different growth stages of four <i<B. juncea</i< varieties were measured in the Vis-NIR range of 325–1075 nm with a stepping of 1.5 nm in reflectance mode. For effective discrimination, the spectral data were preprocessed using three distinct approaches, and eight different chemometric analyses were utilized. After the detection of outliers, the samples were split into two groups, one serving as a calibration set and the other as a validation set. When numerous preprocessing and chemometric approaches were applied for discriminating, the combination of standard normal variate and deep learning had the highest classification accuracy in all the growth stages achieved up to 100%. Similarly, few other chemometrics also yielded 100% classification accuracy, namely, support vector machine, generalized linear model, and the random forest. Of all the chemometric preprocessing methods, Savitzky–Golay filter smoothing provided the best and most convincing discrimination. The findings imply that chemometric methods combined with handheld Vis-NIR spectroscopy can be utilized as an efficient tool for differentiating <i<B. juncea</i< varieties in the field in all the growth stages. <i<Brassica juncea</i< visible near-infrared spectroscopy deep learning machine learning variety discrimination Biology (General) Chemistry Subramani Pandian verfasserin aut Young-Ju Oh verfasserin aut John-Lewis Zinia Zaukuu verfasserin aut Yong-Ho Lee verfasserin aut Eun-Kyoung Shin verfasserin aut In International Journal of Molecular Sciences MDPI AG, 2003 23(2022), 21, p 12809 (DE-627)316340715 (DE-600)2019364-6 14220067 nnns volume:23 year:2022 number:21, p 12809 https://doi.org/10.3390/ijms232112809 kostenfrei https://doaj.org/article/e0d5a02fe01c4943b874080004fdd2ab kostenfrei https://www.mdpi.com/1422-0067/23/21/12809 kostenfrei https://doaj.org/toc/1661-6596 Journal toc kostenfrei https://doaj.org/toc/1422-0067 Journal toc kostenfrei GBV_USEFLAG_A SYSFLAG_A GBV_DOAJ GBV_ILN_20 GBV_ILN_22 GBV_ILN_23 GBV_ILN_24 GBV_ILN_31 GBV_ILN_39 GBV_ILN_40 GBV_ILN_60 GBV_ILN_62 GBV_ILN_63 GBV_ILN_65 GBV_ILN_69 GBV_ILN_70 GBV_ILN_73 GBV_ILN_74 GBV_ILN_95 GBV_ILN_105 GBV_ILN_110 GBV_ILN_151 GBV_ILN_161 GBV_ILN_170 GBV_ILN_206 GBV_ILN_213 GBV_ILN_224 GBV_ILN_230 GBV_ILN_285 GBV_ILN_293 GBV_ILN_602 GBV_ILN_2005 GBV_ILN_2009 GBV_ILN_2011 GBV_ILN_2014 GBV_ILN_2055 GBV_ILN_2111 GBV_ILN_4012 GBV_ILN_4037 GBV_ILN_4112 GBV_ILN_4125 GBV_ILN_4126 GBV_ILN_4249 GBV_ILN_4305 GBV_ILN_4306 GBV_ILN_4307 GBV_ILN_4313 GBV_ILN_4322 GBV_ILN_4323 GBV_ILN_4324 GBV_ILN_4325 GBV_ILN_4338 GBV_ILN_4367 GBV_ILN_4700 AR 23 2022 21, p 12809
spelling	10.3390/ijms232112809 doi (DE-627)DOAJ083877967 (DE-599)DOAJe0d5a02fe01c4943b874080004fdd2ab DE-627 ger DE-627 rakwb eng QH301-705.5 QD1-999 Soo-In Sohn verfasserin aut Discrimination of <i<Brassica juncea</i< Varieties Using Visible Near-Infrared (Vis-NIR) Spectroscopy and Chemometrics Methods 2022 Text txt rdacontent Computermedien c rdamedia Online-Ressource cr rdacarrier Brown mustard (<i<Brassica juncea</i< (L.) is an important oilseed crop that is mostly used to produce edible oils, industrial oils, modified lipids and biofuels in subtropical nations. Due to its higher level of commercial use, the species has a huge array of varieties/cultivars. The purpose of this study is to evaluate the use of visible near-infrared (Vis-NIR) spectroscopy in combination with multiple chemometric approaches for distinguishing four <i<B. juncea</i< varieties in Korea. The spectra from the leaves of four different growth stages of four <i<B. juncea</i< varieties were measured in the Vis-NIR range of 325–1075 nm with a stepping of 1.5 nm in reflectance mode. For effective discrimination, the spectral data were preprocessed using three distinct approaches, and eight different chemometric analyses were utilized. After the detection of outliers, the samples were split into two groups, one serving as a calibration set and the other as a validation set. When numerous preprocessing and chemometric approaches were applied for discriminating, the combination of standard normal variate and deep learning had the highest classification accuracy in all the growth stages achieved up to 100%. Similarly, few other chemometrics also yielded 100% classification accuracy, namely, support vector machine, generalized linear model, and the random forest. Of all the chemometric preprocessing methods, Savitzky–Golay filter smoothing provided the best and most convincing discrimination. The findings imply that chemometric methods combined with handheld Vis-NIR spectroscopy can be utilized as an efficient tool for differentiating <i<B. juncea</i< varieties in the field in all the growth stages. <i<Brassica juncea</i< visible near-infrared spectroscopy deep learning machine learning variety discrimination Biology (General) Chemistry Subramani Pandian verfasserin aut Young-Ju Oh verfasserin aut John-Lewis Zinia Zaukuu verfasserin aut Yong-Ho Lee verfasserin aut Eun-Kyoung Shin verfasserin aut In International Journal of Molecular Sciences MDPI AG, 2003 23(2022), 21, p 12809 (DE-627)316340715 (DE-600)2019364-6 14220067 nnns volume:23 year:2022 number:21, p 12809 https://doi.org/10.3390/ijms232112809 kostenfrei https://doaj.org/article/e0d5a02fe01c4943b874080004fdd2ab kostenfrei https://www.mdpi.com/1422-0067/23/21/12809 kostenfrei https://doaj.org/toc/1661-6596 Journal toc kostenfrei https://doaj.org/toc/1422-0067 Journal toc kostenfrei GBV_USEFLAG_A SYSFLAG_A GBV_DOAJ GBV_ILN_20 GBV_ILN_22 GBV_ILN_23 GBV_ILN_24 GBV_ILN_31 GBV_ILN_39 GBV_ILN_40 GBV_ILN_60 GBV_ILN_62 GBV_ILN_63 GBV_ILN_65 GBV_ILN_69 GBV_ILN_70 GBV_ILN_73 GBV_ILN_74 GBV_ILN_95 GBV_ILN_105 GBV_ILN_110 GBV_ILN_151 GBV_ILN_161 GBV_ILN_170 GBV_ILN_206 GBV_ILN_213 GBV_ILN_224 GBV_ILN_230 GBV_ILN_285 GBV_ILN_293 GBV_ILN_602 GBV_ILN_2005 GBV_ILN_2009 GBV_ILN_2011 GBV_ILN_2014 GBV_ILN_2055 GBV_ILN_2111 GBV_ILN_4012 GBV_ILN_4037 GBV_ILN_4112 GBV_ILN_4125 GBV_ILN_4126 GBV_ILN_4249 GBV_ILN_4305 GBV_ILN_4306 GBV_ILN_4307 GBV_ILN_4313 GBV_ILN_4322 GBV_ILN_4323 GBV_ILN_4324 GBV_ILN_4325 GBV_ILN_4338 GBV_ILN_4367 GBV_ILN_4700 AR 23 2022 21, p 12809
allfields_unstemmed	10.3390/ijms232112809 doi (DE-627)DOAJ083877967 (DE-599)DOAJe0d5a02fe01c4943b874080004fdd2ab DE-627 ger DE-627 rakwb eng QH301-705.5 QD1-999 Soo-In Sohn verfasserin aut Discrimination of <i<Brassica juncea</i< Varieties Using Visible Near-Infrared (Vis-NIR) Spectroscopy and Chemometrics Methods 2022 Text txt rdacontent Computermedien c rdamedia Online-Ressource cr rdacarrier Brown mustard (<i<Brassica juncea</i< (L.) is an important oilseed crop that is mostly used to produce edible oils, industrial oils, modified lipids and biofuels in subtropical nations. Due to its higher level of commercial use, the species has a huge array of varieties/cultivars. The purpose of this study is to evaluate the use of visible near-infrared (Vis-NIR) spectroscopy in combination with multiple chemometric approaches for distinguishing four <i<B. juncea</i< varieties in Korea. The spectra from the leaves of four different growth stages of four <i<B. juncea</i< varieties were measured in the Vis-NIR range of 325–1075 nm with a stepping of 1.5 nm in reflectance mode. For effective discrimination, the spectral data were preprocessed using three distinct approaches, and eight different chemometric analyses were utilized. After the detection of outliers, the samples were split into two groups, one serving as a calibration set and the other as a validation set. When numerous preprocessing and chemometric approaches were applied for discriminating, the combination of standard normal variate and deep learning had the highest classification accuracy in all the growth stages achieved up to 100%. Similarly, few other chemometrics also yielded 100% classification accuracy, namely, support vector machine, generalized linear model, and the random forest. Of all the chemometric preprocessing methods, Savitzky–Golay filter smoothing provided the best and most convincing discrimination. The findings imply that chemometric methods combined with handheld Vis-NIR spectroscopy can be utilized as an efficient tool for differentiating <i<B. juncea</i< varieties in the field in all the growth stages. <i<Brassica juncea</i< visible near-infrared spectroscopy deep learning machine learning variety discrimination Biology (General) Chemistry Subramani Pandian verfasserin aut Young-Ju Oh verfasserin aut John-Lewis Zinia Zaukuu verfasserin aut Yong-Ho Lee verfasserin aut Eun-Kyoung Shin verfasserin aut In International Journal of Molecular Sciences MDPI AG, 2003 23(2022), 21, p 12809 (DE-627)316340715 (DE-600)2019364-6 14220067 nnns volume:23 year:2022 number:21, p 12809 https://doi.org/10.3390/ijms232112809 kostenfrei https://doaj.org/article/e0d5a02fe01c4943b874080004fdd2ab kostenfrei https://www.mdpi.com/1422-0067/23/21/12809 kostenfrei https://doaj.org/toc/1661-6596 Journal toc kostenfrei https://doaj.org/toc/1422-0067 Journal toc kostenfrei GBV_USEFLAG_A SYSFLAG_A GBV_DOAJ GBV_ILN_20 GBV_ILN_22 GBV_ILN_23 GBV_ILN_24 GBV_ILN_31 GBV_ILN_39 GBV_ILN_40 GBV_ILN_60 GBV_ILN_62 GBV_ILN_63 GBV_ILN_65 GBV_ILN_69 GBV_ILN_70 GBV_ILN_73 GBV_ILN_74 GBV_ILN_95 GBV_ILN_105 GBV_ILN_110 GBV_ILN_151 GBV_ILN_161 GBV_ILN_170 GBV_ILN_206 GBV_ILN_213 GBV_ILN_224 GBV_ILN_230 GBV_ILN_285 GBV_ILN_293 GBV_ILN_602 GBV_ILN_2005 GBV_ILN_2009 GBV_ILN_2011 GBV_ILN_2014 GBV_ILN_2055 GBV_ILN_2111 GBV_ILN_4012 GBV_ILN_4037 GBV_ILN_4112 GBV_ILN_4125 GBV_ILN_4126 GBV_ILN_4249 GBV_ILN_4305 GBV_ILN_4306 GBV_ILN_4307 GBV_ILN_4313 GBV_ILN_4322 GBV_ILN_4323 GBV_ILN_4324 GBV_ILN_4325 GBV_ILN_4338 GBV_ILN_4367 GBV_ILN_4700 AR 23 2022 21, p 12809
allfieldsGer	10.3390/ijms232112809 doi (DE-627)DOAJ083877967 (DE-599)DOAJe0d5a02fe01c4943b874080004fdd2ab DE-627 ger DE-627 rakwb eng QH301-705.5 QD1-999 Soo-In Sohn verfasserin aut Discrimination of <i<Brassica juncea</i< Varieties Using Visible Near-Infrared (Vis-NIR) Spectroscopy and Chemometrics Methods 2022 Text txt rdacontent Computermedien c rdamedia Online-Ressource cr rdacarrier Brown mustard (<i<Brassica juncea</i< (L.) is an important oilseed crop that is mostly used to produce edible oils, industrial oils, modified lipids and biofuels in subtropical nations. Due to its higher level of commercial use, the species has a huge array of varieties/cultivars. The purpose of this study is to evaluate the use of visible near-infrared (Vis-NIR) spectroscopy in combination with multiple chemometric approaches for distinguishing four <i<B. juncea</i< varieties in Korea. The spectra from the leaves of four different growth stages of four <i<B. juncea</i< varieties were measured in the Vis-NIR range of 325–1075 nm with a stepping of 1.5 nm in reflectance mode. For effective discrimination, the spectral data were preprocessed using three distinct approaches, and eight different chemometric analyses were utilized. After the detection of outliers, the samples were split into two groups, one serving as a calibration set and the other as a validation set. When numerous preprocessing and chemometric approaches were applied for discriminating, the combination of standard normal variate and deep learning had the highest classification accuracy in all the growth stages achieved up to 100%. Similarly, few other chemometrics also yielded 100% classification accuracy, namely, support vector machine, generalized linear model, and the random forest. Of all the chemometric preprocessing methods, Savitzky–Golay filter smoothing provided the best and most convincing discrimination. The findings imply that chemometric methods combined with handheld Vis-NIR spectroscopy can be utilized as an efficient tool for differentiating <i<B. juncea</i< varieties in the field in all the growth stages. <i<Brassica juncea</i< visible near-infrared spectroscopy deep learning machine learning variety discrimination Biology (General) Chemistry Subramani Pandian verfasserin aut Young-Ju Oh verfasserin aut John-Lewis Zinia Zaukuu verfasserin aut Yong-Ho Lee verfasserin aut Eun-Kyoung Shin verfasserin aut In International Journal of Molecular Sciences MDPI AG, 2003 23(2022), 21, p 12809 (DE-627)316340715 (DE-600)2019364-6 14220067 nnns volume:23 year:2022 number:21, p 12809 https://doi.org/10.3390/ijms232112809 kostenfrei https://doaj.org/article/e0d5a02fe01c4943b874080004fdd2ab kostenfrei https://www.mdpi.com/1422-0067/23/21/12809 kostenfrei https://doaj.org/toc/1661-6596 Journal toc kostenfrei https://doaj.org/toc/1422-0067 Journal toc kostenfrei GBV_USEFLAG_A SYSFLAG_A GBV_DOAJ GBV_ILN_20 GBV_ILN_22 GBV_ILN_23 GBV_ILN_24 GBV_ILN_31 GBV_ILN_39 GBV_ILN_40 GBV_ILN_60 GBV_ILN_62 GBV_ILN_63 GBV_ILN_65 GBV_ILN_69 GBV_ILN_70 GBV_ILN_73 GBV_ILN_74 GBV_ILN_95 GBV_ILN_105 GBV_ILN_110 GBV_ILN_151 GBV_ILN_161 GBV_ILN_170 GBV_ILN_206 GBV_ILN_213 GBV_ILN_224 GBV_ILN_230 GBV_ILN_285 GBV_ILN_293 GBV_ILN_602 GBV_ILN_2005 GBV_ILN_2009 GBV_ILN_2011 GBV_ILN_2014 GBV_ILN_2055 GBV_ILN_2111 GBV_ILN_4012 GBV_ILN_4037 GBV_ILN_4112 GBV_ILN_4125 GBV_ILN_4126 GBV_ILN_4249 GBV_ILN_4305 GBV_ILN_4306 GBV_ILN_4307 GBV_ILN_4313 GBV_ILN_4322 GBV_ILN_4323 GBV_ILN_4324 GBV_ILN_4325 GBV_ILN_4338 GBV_ILN_4367 GBV_ILN_4700 AR 23 2022 21, p 12809
allfieldsSound	10.3390/ijms232112809 doi (DE-627)DOAJ083877967 (DE-599)DOAJe0d5a02fe01c4943b874080004fdd2ab DE-627 ger DE-627 rakwb eng QH301-705.5 QD1-999 Soo-In Sohn verfasserin aut Discrimination of <i<Brassica juncea</i< Varieties Using Visible Near-Infrared (Vis-NIR) Spectroscopy and Chemometrics Methods 2022 Text txt rdacontent Computermedien c rdamedia Online-Ressource cr rdacarrier Brown mustard (<i<Brassica juncea</i< (L.) is an important oilseed crop that is mostly used to produce edible oils, industrial oils, modified lipids and biofuels in subtropical nations. Due to its higher level of commercial use, the species has a huge array of varieties/cultivars. The purpose of this study is to evaluate the use of visible near-infrared (Vis-NIR) spectroscopy in combination with multiple chemometric approaches for distinguishing four <i<B. juncea</i< varieties in Korea. The spectra from the leaves of four different growth stages of four <i<B. juncea</i< varieties were measured in the Vis-NIR range of 325–1075 nm with a stepping of 1.5 nm in reflectance mode. For effective discrimination, the spectral data were preprocessed using three distinct approaches, and eight different chemometric analyses were utilized. After the detection of outliers, the samples were split into two groups, one serving as a calibration set and the other as a validation set. When numerous preprocessing and chemometric approaches were applied for discriminating, the combination of standard normal variate and deep learning had the highest classification accuracy in all the growth stages achieved up to 100%. Similarly, few other chemometrics also yielded 100% classification accuracy, namely, support vector machine, generalized linear model, and the random forest. Of all the chemometric preprocessing methods, Savitzky–Golay filter smoothing provided the best and most convincing discrimination. The findings imply that chemometric methods combined with handheld Vis-NIR spectroscopy can be utilized as an efficient tool for differentiating <i<B. juncea</i< varieties in the field in all the growth stages. <i<Brassica juncea</i< visible near-infrared spectroscopy deep learning machine learning variety discrimination Biology (General) Chemistry Subramani Pandian verfasserin aut Young-Ju Oh verfasserin aut John-Lewis Zinia Zaukuu verfasserin aut Yong-Ho Lee verfasserin aut Eun-Kyoung Shin verfasserin aut In International Journal of Molecular Sciences MDPI AG, 2003 23(2022), 21, p 12809 (DE-627)316340715 (DE-600)2019364-6 14220067 nnns volume:23 year:2022 number:21, p 12809 https://doi.org/10.3390/ijms232112809 kostenfrei https://doaj.org/article/e0d5a02fe01c4943b874080004fdd2ab kostenfrei https://www.mdpi.com/1422-0067/23/21/12809 kostenfrei https://doaj.org/toc/1661-6596 Journal toc kostenfrei https://doaj.org/toc/1422-0067 Journal toc kostenfrei GBV_USEFLAG_A SYSFLAG_A GBV_DOAJ GBV_ILN_20 GBV_ILN_22 GBV_ILN_23 GBV_ILN_24 GBV_ILN_31 GBV_ILN_39 GBV_ILN_40 GBV_ILN_60 GBV_ILN_62 GBV_ILN_63 GBV_ILN_65 GBV_ILN_69 GBV_ILN_70 GBV_ILN_73 GBV_ILN_74 GBV_ILN_95 GBV_ILN_105 GBV_ILN_110 GBV_ILN_151 GBV_ILN_161 GBV_ILN_170 GBV_ILN_206 GBV_ILN_213 GBV_ILN_224 GBV_ILN_230 GBV_ILN_285 GBV_ILN_293 GBV_ILN_602 GBV_ILN_2005 GBV_ILN_2009 GBV_ILN_2011 GBV_ILN_2014 GBV_ILN_2055 GBV_ILN_2111 GBV_ILN_4012 GBV_ILN_4037 GBV_ILN_4112 GBV_ILN_4125 GBV_ILN_4126 GBV_ILN_4249 GBV_ILN_4305 GBV_ILN_4306 GBV_ILN_4307 GBV_ILN_4313 GBV_ILN_4322 GBV_ILN_4323 GBV_ILN_4324 GBV_ILN_4325 GBV_ILN_4338 GBV_ILN_4367 GBV_ILN_4700 AR 23 2022 21, p 12809
language	English
source	In International Journal of Molecular Sciences 23(2022), 21, p 12809 volume:23 year:2022 number:21, p 12809
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format_phy_str_mv	Article
institution	findex.gbv.de
topic_facet	<i<Brassica juncea</i< visible near-infrared spectroscopy deep learning machine learning variety discrimination Biology (General) Chemistry
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container_title	International Journal of Molecular Sciences
authorswithroles_txt_mv	Soo-In Sohn @@aut@@ Subramani Pandian @@aut@@ Young-Ju Oh @@aut@@ John-Lewis Zinia Zaukuu @@aut@@ Yong-Ho Lee @@aut@@ Eun-Kyoung Shin @@aut@@
publishDateDaySort_date	2022-01-01T00:00:00Z
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callnumber-first	Q - Science
author	Soo-In Sohn
spellingShingle	Soo-In Sohn misc QH301-705.5 misc QD1-999 misc <i<Brassica juncea</i< misc visible near-infrared misc spectroscopy misc deep learning misc machine learning misc variety discrimination misc Biology (General) misc Chemistry Discrimination of <i<Brassica juncea</i< Varieties Using Visible Near-Infrared (Vis-NIR) Spectroscopy and Chemometrics Methods
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topic_title	QH301-705.5 QD1-999 Discrimination of <i<Brassica juncea</i< Varieties Using Visible Near-Infrared (Vis-NIR) Spectroscopy and Chemometrics Methods <i<Brassica juncea</i< visible near-infrared spectroscopy deep learning machine learning variety discrimination
topic	misc QH301-705.5 misc QD1-999 misc <i<Brassica juncea</i< misc visible near-infrared misc spectroscopy misc deep learning misc machine learning misc variety discrimination misc Biology (General) misc Chemistry
topic_unstemmed	misc QH301-705.5 misc QD1-999 misc <i<Brassica juncea</i< misc visible near-infrared misc spectroscopy misc deep learning misc machine learning misc variety discrimination misc Biology (General) misc Chemistry
topic_browse	misc QH301-705.5 misc QD1-999 misc <i<Brassica juncea</i< misc visible near-infrared misc spectroscopy misc deep learning misc machine learning misc variety discrimination misc Biology (General) misc Chemistry
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title	Discrimination of <i<Brassica juncea</i< Varieties Using Visible Near-Infrared (Vis-NIR) Spectroscopy and Chemometrics Methods
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title_full	Discrimination of <i<Brassica juncea</i< Varieties Using Visible Near-Infrared (Vis-NIR) Spectroscopy and Chemometrics Methods
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author_browse	Soo-In Sohn Subramani Pandian Young-Ju Oh John-Lewis Zinia Zaukuu Yong-Ho Lee Eun-Kyoung Shin
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title_sort	discrimination of <i<brassica juncea</i< varieties using visible near-infrared (vis-nir) spectroscopy and chemometrics methods
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title_auth	Discrimination of <i<Brassica juncea</i< Varieties Using Visible Near-Infrared (Vis-NIR) Spectroscopy and Chemometrics Methods
abstract	Brown mustard (<i<Brassica juncea</i< (L.) is an important oilseed crop that is mostly used to produce edible oils, industrial oils, modified lipids and biofuels in subtropical nations. Due to its higher level of commercial use, the species has a huge array of varieties/cultivars. The purpose of this study is to evaluate the use of visible near-infrared (Vis-NIR) spectroscopy in combination with multiple chemometric approaches for distinguishing four <i<B. juncea</i< varieties in Korea. The spectra from the leaves of four different growth stages of four <i<B. juncea</i< varieties were measured in the Vis-NIR range of 325–1075 nm with a stepping of 1.5 nm in reflectance mode. For effective discrimination, the spectral data were preprocessed using three distinct approaches, and eight different chemometric analyses were utilized. After the detection of outliers, the samples were split into two groups, one serving as a calibration set and the other as a validation set. When numerous preprocessing and chemometric approaches were applied for discriminating, the combination of standard normal variate and deep learning had the highest classification accuracy in all the growth stages achieved up to 100%. Similarly, few other chemometrics also yielded 100% classification accuracy, namely, support vector machine, generalized linear model, and the random forest. Of all the chemometric preprocessing methods, Savitzky–Golay filter smoothing provided the best and most convincing discrimination. The findings imply that chemometric methods combined with handheld Vis-NIR spectroscopy can be utilized as an efficient tool for differentiating <i<B. juncea</i< varieties in the field in all the growth stages.
abstractGer	Brown mustard (<i<Brassica juncea</i< (L.) is an important oilseed crop that is mostly used to produce edible oils, industrial oils, modified lipids and biofuels in subtropical nations. Due to its higher level of commercial use, the species has a huge array of varieties/cultivars. The purpose of this study is to evaluate the use of visible near-infrared (Vis-NIR) spectroscopy in combination with multiple chemometric approaches for distinguishing four <i<B. juncea</i< varieties in Korea. The spectra from the leaves of four different growth stages of four <i<B. juncea</i< varieties were measured in the Vis-NIR range of 325–1075 nm with a stepping of 1.5 nm in reflectance mode. For effective discrimination, the spectral data were preprocessed using three distinct approaches, and eight different chemometric analyses were utilized. After the detection of outliers, the samples were split into two groups, one serving as a calibration set and the other as a validation set. When numerous preprocessing and chemometric approaches were applied for discriminating, the combination of standard normal variate and deep learning had the highest classification accuracy in all the growth stages achieved up to 100%. Similarly, few other chemometrics also yielded 100% classification accuracy, namely, support vector machine, generalized linear model, and the random forest. Of all the chemometric preprocessing methods, Savitzky–Golay filter smoothing provided the best and most convincing discrimination. The findings imply that chemometric methods combined with handheld Vis-NIR spectroscopy can be utilized as an efficient tool for differentiating <i<B. juncea</i< varieties in the field in all the growth stages.
abstract_unstemmed	Brown mustard (<i<Brassica juncea</i< (L.) is an important oilseed crop that is mostly used to produce edible oils, industrial oils, modified lipids and biofuels in subtropical nations. Due to its higher level of commercial use, the species has a huge array of varieties/cultivars. The purpose of this study is to evaluate the use of visible near-infrared (Vis-NIR) spectroscopy in combination with multiple chemometric approaches for distinguishing four <i<B. juncea</i< varieties in Korea. The spectra from the leaves of four different growth stages of four <i<B. juncea</i< varieties were measured in the Vis-NIR range of 325–1075 nm with a stepping of 1.5 nm in reflectance mode. For effective discrimination, the spectral data were preprocessed using three distinct approaches, and eight different chemometric analyses were utilized. After the detection of outliers, the samples were split into two groups, one serving as a calibration set and the other as a validation set. When numerous preprocessing and chemometric approaches were applied for discriminating, the combination of standard normal variate and deep learning had the highest classification accuracy in all the growth stages achieved up to 100%. Similarly, few other chemometrics also yielded 100% classification accuracy, namely, support vector machine, generalized linear model, and the random forest. Of all the chemometric preprocessing methods, Savitzky–Golay filter smoothing provided the best and most convincing discrimination. The findings imply that chemometric methods combined with handheld Vis-NIR spectroscopy can be utilized as an efficient tool for differentiating <i<B. juncea</i< varieties in the field in all the growth stages.
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title_short	Discrimination of <i<Brassica juncea</i< Varieties Using Visible Near-Infrared (Vis-NIR) Spectroscopy and Chemometrics Methods
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