Rapid discrimination of Brassica napus varieties using visible and Near-infrared (Vis-NIR) spectroscopy
Brassica napus is an oilseed plant that is mostly used to produce edible oils, industrial oils, modified lipids and biofuels. The number of varieties/cultivars is high for the species, owing to their higher level of economic use. The aim of this study is to assess the use of visible-near infrared (V...
Ausführliche Beschreibung
Gespeichert in:
| Autor*in: |
Soo-In Sohn [verfasserIn] Subramani Pandian [verfasserIn] John-Lewis Zinia Zaukuu [verfasserIn] Young-Ju Oh [verfasserIn] Yong-Ho Lee [verfasserIn] Eun-Kyoung Shin [verfasserIn] Senthil Kumar Thamilarasan [verfasserIn] Hyeon-Jung Kang [verfasserIn] Tae-Hun Ryu [verfasserIn] Woo-Suk Cho [verfasserIn] |
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| Format: |
E-Artikel |
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| Sprache: |
Englisch |
| Erschienen: |
2023 |
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| Schlagwörter: |
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| Übergeordnetes Werk: |
In: Journal of King Saud University: Science - Elsevier, 2016, 35(2023), 2, Seite 102495- |
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| Übergeordnetes Werk: |
volume:35 ; year:2023 ; number:2 ; pages:102495- |
| Links: |
|---|
| DOI / URN: |
10.1016/j.jksus.2022.102495 |
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DOAJ024840831 |
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| 520 | |a Brassica napus is an oilseed plant that is mostly used to produce edible oils, industrial oils, modified lipids and biofuels. The number of varieties/cultivars is high for the species, owing to their higher level of economic use. The aim of this study is to assess the use of visible-near infrared (Vis-NIR) spectroscopy in combination with multiple chemometric methods that have been explored for the discrimination of eight Brassica napus varieties in Korea. In this study, the spectra from leaves of the eight B. napus varieties were measured in the Vis-NIR spectra in the range of 325–1075 nm with a stepping of 1.5 nm in reflectance mode. The spectral data were preprocessed with three different preprocessing methods and eight different chemometric analyses were used for effective discrimination. After the outlier detection, the samples were split into two sets, one serving as a calibration set and the remaining one as a validation set. When using multiple preprocessing and chemometric methods for the discrimination, the maximum classification accuracy was witnessed in the combination of standard normal variate and support vector machine up to 98.2 %. The use of Savitzky-Golay filter smoothing as a preprocessing method had the best and most satisfactory discrimination of all other chemometric methods. The results suggest that the use of handheld Vis-NIR spectroscopy in combination with chemometric approaches can be used as an effective tool for the discrimination of B. napus varieties in the field. | ||
| 650 | 4 | |a Visible-near infrared | |
| 650 | 4 | |a Spectroscopy | |
| 650 | 4 | |a Brassica napus | |
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| 650 | 4 | |a Deep learning | |
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10.1016/j.jksus.2022.102495 doi (DE-627)DOAJ024840831 (DE-599)DOAJ2f63f4c357d240aaacdb6688c0bf51fd DE-627 ger DE-627 rakwb eng Q1-390 Soo-In Sohn verfasserin aut Rapid discrimination of Brassica napus varieties using visible and Near-infrared (Vis-NIR) spectroscopy 2023 Text txt rdacontent Computermedien c rdamedia Online-Ressource cr rdacarrier Brassica napus is an oilseed plant that is mostly used to produce edible oils, industrial oils, modified lipids and biofuels. The number of varieties/cultivars is high for the species, owing to their higher level of economic use. The aim of this study is to assess the use of visible-near infrared (Vis-NIR) spectroscopy in combination with multiple chemometric methods that have been explored for the discrimination of eight Brassica napus varieties in Korea. In this study, the spectra from leaves of the eight B. napus varieties were measured in the Vis-NIR spectra in the range of 325–1075 nm with a stepping of 1.5 nm in reflectance mode. The spectral data were preprocessed with three different preprocessing methods and eight different chemometric analyses were used for effective discrimination. After the outlier detection, the samples were split into two sets, one serving as a calibration set and the remaining one as a validation set. When using multiple preprocessing and chemometric methods for the discrimination, the maximum classification accuracy was witnessed in the combination of standard normal variate and support vector machine up to 98.2 %. The use of Savitzky-Golay filter smoothing as a preprocessing method had the best and most satisfactory discrimination of all other chemometric methods. The results suggest that the use of handheld Vis-NIR spectroscopy in combination with chemometric approaches can be used as an effective tool for the discrimination of B. napus varieties in the field. Visible-near infrared Spectroscopy Brassica napus Chemometrics Deep learning Preprocessing Science (General) Subramani Pandian verfasserin aut John-Lewis Zinia Zaukuu verfasserin aut Young-Ju Oh verfasserin aut Yong-Ho Lee verfasserin aut Eun-Kyoung Shin verfasserin aut Senthil Kumar Thamilarasan verfasserin aut Hyeon-Jung Kang verfasserin aut Tae-Hun Ryu verfasserin aut Woo-Suk Cho verfasserin aut In Journal of King Saud University: Science Elsevier, 2016 35(2023), 2, Seite 102495- (DE-627)608942790 (DE-600)2514731-6 10183647 nnns volume:35 year:2023 number:2 pages:102495- https://doi.org/10.1016/j.jksus.2022.102495 kostenfrei https://doaj.org/article/2f63f4c357d240aaacdb6688c0bf51fd kostenfrei http://www.sciencedirect.com/science/article/pii/S1018364722006760 kostenfrei https://doaj.org/toc/1018-3647 Journal toc kostenfrei GBV_USEFLAG_A SYSFLAG_A GBV_DOAJ GBV_ILN_20 GBV_ILN_22 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_95 GBV_ILN_105 GBV_ILN_110 GBV_ILN_151 GBV_ILN_161 GBV_ILN_171 GBV_ILN_213 GBV_ILN_224 GBV_ILN_230 GBV_ILN_285 GBV_ILN_293 GBV_ILN_602 GBV_ILN_2001 GBV_ILN_2003 GBV_ILN_2004 GBV_ILN_2005 GBV_ILN_2006 GBV_ILN_2007 GBV_ILN_2008 GBV_ILN_2009 GBV_ILN_2010 GBV_ILN_2011 GBV_ILN_2014 GBV_ILN_2015 GBV_ILN_2020 GBV_ILN_2021 GBV_ILN_2025 GBV_ILN_2026 GBV_ILN_2027 GBV_ILN_2034 GBV_ILN_2038 GBV_ILN_2044 GBV_ILN_2048 GBV_ILN_2049 GBV_ILN_2050 GBV_ILN_2055 GBV_ILN_2056 GBV_ILN_2059 GBV_ILN_2061 GBV_ILN_2064 GBV_ILN_2088 GBV_ILN_2106 GBV_ILN_2110 GBV_ILN_2112 GBV_ILN_2122 GBV_ILN_2129 GBV_ILN_2143 GBV_ILN_2152 GBV_ILN_2153 GBV_ILN_2190 GBV_ILN_2232 GBV_ILN_2336 GBV_ILN_2470 GBV_ILN_2507 GBV_ILN_4012 GBV_ILN_4035 GBV_ILN_4037 GBV_ILN_4112 GBV_ILN_4125 GBV_ILN_4126 GBV_ILN_4242 GBV_ILN_4249 GBV_ILN_4251 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_4326 GBV_ILN_4333 GBV_ILN_4334 GBV_ILN_4335 GBV_ILN_4338 GBV_ILN_4367 GBV_ILN_4393 GBV_ILN_4700 AR 35 2023 2 102495- |
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10.1016/j.jksus.2022.102495 doi (DE-627)DOAJ024840831 (DE-599)DOAJ2f63f4c357d240aaacdb6688c0bf51fd DE-627 ger DE-627 rakwb eng Q1-390 Soo-In Sohn verfasserin aut Rapid discrimination of Brassica napus varieties using visible and Near-infrared (Vis-NIR) spectroscopy 2023 Text txt rdacontent Computermedien c rdamedia Online-Ressource cr rdacarrier Brassica napus is an oilseed plant that is mostly used to produce edible oils, industrial oils, modified lipids and biofuels. The number of varieties/cultivars is high for the species, owing to their higher level of economic use. The aim of this study is to assess the use of visible-near infrared (Vis-NIR) spectroscopy in combination with multiple chemometric methods that have been explored for the discrimination of eight Brassica napus varieties in Korea. In this study, the spectra from leaves of the eight B. napus varieties were measured in the Vis-NIR spectra in the range of 325–1075 nm with a stepping of 1.5 nm in reflectance mode. The spectral data were preprocessed with three different preprocessing methods and eight different chemometric analyses were used for effective discrimination. After the outlier detection, the samples were split into two sets, one serving as a calibration set and the remaining one as a validation set. When using multiple preprocessing and chemometric methods for the discrimination, the maximum classification accuracy was witnessed in the combination of standard normal variate and support vector machine up to 98.2 %. The use of Savitzky-Golay filter smoothing as a preprocessing method had the best and most satisfactory discrimination of all other chemometric methods. The results suggest that the use of handheld Vis-NIR spectroscopy in combination with chemometric approaches can be used as an effective tool for the discrimination of B. napus varieties in the field. Visible-near infrared Spectroscopy Brassica napus Chemometrics Deep learning Preprocessing Science (General) Subramani Pandian verfasserin aut John-Lewis Zinia Zaukuu verfasserin aut Young-Ju Oh verfasserin aut Yong-Ho Lee verfasserin aut Eun-Kyoung Shin verfasserin aut Senthil Kumar Thamilarasan verfasserin aut Hyeon-Jung Kang verfasserin aut Tae-Hun Ryu verfasserin aut Woo-Suk Cho verfasserin aut In Journal of King Saud University: Science Elsevier, 2016 35(2023), 2, Seite 102495- (DE-627)608942790 (DE-600)2514731-6 10183647 nnns volume:35 year:2023 number:2 pages:102495- https://doi.org/10.1016/j.jksus.2022.102495 kostenfrei https://doaj.org/article/2f63f4c357d240aaacdb6688c0bf51fd kostenfrei http://www.sciencedirect.com/science/article/pii/S1018364722006760 kostenfrei https://doaj.org/toc/1018-3647 Journal toc kostenfrei GBV_USEFLAG_A SYSFLAG_A GBV_DOAJ GBV_ILN_20 GBV_ILN_22 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_95 GBV_ILN_105 GBV_ILN_110 GBV_ILN_151 GBV_ILN_161 GBV_ILN_171 GBV_ILN_213 GBV_ILN_224 GBV_ILN_230 GBV_ILN_285 GBV_ILN_293 GBV_ILN_602 GBV_ILN_2001 GBV_ILN_2003 GBV_ILN_2004 GBV_ILN_2005 GBV_ILN_2006 GBV_ILN_2007 GBV_ILN_2008 GBV_ILN_2009 GBV_ILN_2010 GBV_ILN_2011 GBV_ILN_2014 GBV_ILN_2015 GBV_ILN_2020 GBV_ILN_2021 GBV_ILN_2025 GBV_ILN_2026 GBV_ILN_2027 GBV_ILN_2034 GBV_ILN_2038 GBV_ILN_2044 GBV_ILN_2048 GBV_ILN_2049 GBV_ILN_2050 GBV_ILN_2055 GBV_ILN_2056 GBV_ILN_2059 GBV_ILN_2061 GBV_ILN_2064 GBV_ILN_2088 GBV_ILN_2106 GBV_ILN_2110 GBV_ILN_2112 GBV_ILN_2122 GBV_ILN_2129 GBV_ILN_2143 GBV_ILN_2152 GBV_ILN_2153 GBV_ILN_2190 GBV_ILN_2232 GBV_ILN_2336 GBV_ILN_2470 GBV_ILN_2507 GBV_ILN_4012 GBV_ILN_4035 GBV_ILN_4037 GBV_ILN_4112 GBV_ILN_4125 GBV_ILN_4126 GBV_ILN_4242 GBV_ILN_4249 GBV_ILN_4251 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_4326 GBV_ILN_4333 GBV_ILN_4334 GBV_ILN_4335 GBV_ILN_4338 GBV_ILN_4367 GBV_ILN_4393 GBV_ILN_4700 AR 35 2023 2 102495- |
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10.1016/j.jksus.2022.102495 doi (DE-627)DOAJ024840831 (DE-599)DOAJ2f63f4c357d240aaacdb6688c0bf51fd DE-627 ger DE-627 rakwb eng Q1-390 Soo-In Sohn verfasserin aut Rapid discrimination of Brassica napus varieties using visible and Near-infrared (Vis-NIR) spectroscopy 2023 Text txt rdacontent Computermedien c rdamedia Online-Ressource cr rdacarrier Brassica napus is an oilseed plant that is mostly used to produce edible oils, industrial oils, modified lipids and biofuels. The number of varieties/cultivars is high for the species, owing to their higher level of economic use. The aim of this study is to assess the use of visible-near infrared (Vis-NIR) spectroscopy in combination with multiple chemometric methods that have been explored for the discrimination of eight Brassica napus varieties in Korea. In this study, the spectra from leaves of the eight B. napus varieties were measured in the Vis-NIR spectra in the range of 325–1075 nm with a stepping of 1.5 nm in reflectance mode. The spectral data were preprocessed with three different preprocessing methods and eight different chemometric analyses were used for effective discrimination. After the outlier detection, the samples were split into two sets, one serving as a calibration set and the remaining one as a validation set. When using multiple preprocessing and chemometric methods for the discrimination, the maximum classification accuracy was witnessed in the combination of standard normal variate and support vector machine up to 98.2 %. The use of Savitzky-Golay filter smoothing as a preprocessing method had the best and most satisfactory discrimination of all other chemometric methods. The results suggest that the use of handheld Vis-NIR spectroscopy in combination with chemometric approaches can be used as an effective tool for the discrimination of B. napus varieties in the field. Visible-near infrared Spectroscopy Brassica napus Chemometrics Deep learning Preprocessing Science (General) Subramani Pandian verfasserin aut John-Lewis Zinia Zaukuu verfasserin aut Young-Ju Oh verfasserin aut Yong-Ho Lee verfasserin aut Eun-Kyoung Shin verfasserin aut Senthil Kumar Thamilarasan verfasserin aut Hyeon-Jung Kang verfasserin aut Tae-Hun Ryu verfasserin aut Woo-Suk Cho verfasserin aut In Journal of King Saud University: Science Elsevier, 2016 35(2023), 2, Seite 102495- (DE-627)608942790 (DE-600)2514731-6 10183647 nnns volume:35 year:2023 number:2 pages:102495- https://doi.org/10.1016/j.jksus.2022.102495 kostenfrei https://doaj.org/article/2f63f4c357d240aaacdb6688c0bf51fd kostenfrei http://www.sciencedirect.com/science/article/pii/S1018364722006760 kostenfrei https://doaj.org/toc/1018-3647 Journal toc kostenfrei GBV_USEFLAG_A SYSFLAG_A GBV_DOAJ GBV_ILN_20 GBV_ILN_22 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_95 GBV_ILN_105 GBV_ILN_110 GBV_ILN_151 GBV_ILN_161 GBV_ILN_171 GBV_ILN_213 GBV_ILN_224 GBV_ILN_230 GBV_ILN_285 GBV_ILN_293 GBV_ILN_602 GBV_ILN_2001 GBV_ILN_2003 GBV_ILN_2004 GBV_ILN_2005 GBV_ILN_2006 GBV_ILN_2007 GBV_ILN_2008 GBV_ILN_2009 GBV_ILN_2010 GBV_ILN_2011 GBV_ILN_2014 GBV_ILN_2015 GBV_ILN_2020 GBV_ILN_2021 GBV_ILN_2025 GBV_ILN_2026 GBV_ILN_2027 GBV_ILN_2034 GBV_ILN_2038 GBV_ILN_2044 GBV_ILN_2048 GBV_ILN_2049 GBV_ILN_2050 GBV_ILN_2055 GBV_ILN_2056 GBV_ILN_2059 GBV_ILN_2061 GBV_ILN_2064 GBV_ILN_2088 GBV_ILN_2106 GBV_ILN_2110 GBV_ILN_2112 GBV_ILN_2122 GBV_ILN_2129 GBV_ILN_2143 GBV_ILN_2152 GBV_ILN_2153 GBV_ILN_2190 GBV_ILN_2232 GBV_ILN_2336 GBV_ILN_2470 GBV_ILN_2507 GBV_ILN_4012 GBV_ILN_4035 GBV_ILN_4037 GBV_ILN_4112 GBV_ILN_4125 GBV_ILN_4126 GBV_ILN_4242 GBV_ILN_4249 GBV_ILN_4251 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_4326 GBV_ILN_4333 GBV_ILN_4334 GBV_ILN_4335 GBV_ILN_4338 GBV_ILN_4367 GBV_ILN_4393 GBV_ILN_4700 AR 35 2023 2 102495- |
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10.1016/j.jksus.2022.102495 doi (DE-627)DOAJ024840831 (DE-599)DOAJ2f63f4c357d240aaacdb6688c0bf51fd DE-627 ger DE-627 rakwb eng Q1-390 Soo-In Sohn verfasserin aut Rapid discrimination of Brassica napus varieties using visible and Near-infrared (Vis-NIR) spectroscopy 2023 Text txt rdacontent Computermedien c rdamedia Online-Ressource cr rdacarrier Brassica napus is an oilseed plant that is mostly used to produce edible oils, industrial oils, modified lipids and biofuels. The number of varieties/cultivars is high for the species, owing to their higher level of economic use. The aim of this study is to assess the use of visible-near infrared (Vis-NIR) spectroscopy in combination with multiple chemometric methods that have been explored for the discrimination of eight Brassica napus varieties in Korea. In this study, the spectra from leaves of the eight B. napus varieties were measured in the Vis-NIR spectra in the range of 325–1075 nm with a stepping of 1.5 nm in reflectance mode. The spectral data were preprocessed with three different preprocessing methods and eight different chemometric analyses were used for effective discrimination. After the outlier detection, the samples were split into two sets, one serving as a calibration set and the remaining one as a validation set. When using multiple preprocessing and chemometric methods for the discrimination, the maximum classification accuracy was witnessed in the combination of standard normal variate and support vector machine up to 98.2 %. The use of Savitzky-Golay filter smoothing as a preprocessing method had the best and most satisfactory discrimination of all other chemometric methods. The results suggest that the use of handheld Vis-NIR spectroscopy in combination with chemometric approaches can be used as an effective tool for the discrimination of B. napus varieties in the field. Visible-near infrared Spectroscopy Brassica napus Chemometrics Deep learning Preprocessing Science (General) Subramani Pandian verfasserin aut John-Lewis Zinia Zaukuu verfasserin aut Young-Ju Oh verfasserin aut Yong-Ho Lee verfasserin aut Eun-Kyoung Shin verfasserin aut Senthil Kumar Thamilarasan verfasserin aut Hyeon-Jung Kang verfasserin aut Tae-Hun Ryu verfasserin aut Woo-Suk Cho verfasserin aut In Journal of King Saud University: Science Elsevier, 2016 35(2023), 2, Seite 102495- (DE-627)608942790 (DE-600)2514731-6 10183647 nnns volume:35 year:2023 number:2 pages:102495- https://doi.org/10.1016/j.jksus.2022.102495 kostenfrei https://doaj.org/article/2f63f4c357d240aaacdb6688c0bf51fd kostenfrei http://www.sciencedirect.com/science/article/pii/S1018364722006760 kostenfrei https://doaj.org/toc/1018-3647 Journal toc kostenfrei GBV_USEFLAG_A SYSFLAG_A GBV_DOAJ GBV_ILN_20 GBV_ILN_22 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_95 GBV_ILN_105 GBV_ILN_110 GBV_ILN_151 GBV_ILN_161 GBV_ILN_171 GBV_ILN_213 GBV_ILN_224 GBV_ILN_230 GBV_ILN_285 GBV_ILN_293 GBV_ILN_602 GBV_ILN_2001 GBV_ILN_2003 GBV_ILN_2004 GBV_ILN_2005 GBV_ILN_2006 GBV_ILN_2007 GBV_ILN_2008 GBV_ILN_2009 GBV_ILN_2010 GBV_ILN_2011 GBV_ILN_2014 GBV_ILN_2015 GBV_ILN_2020 GBV_ILN_2021 GBV_ILN_2025 GBV_ILN_2026 GBV_ILN_2027 GBV_ILN_2034 GBV_ILN_2038 GBV_ILN_2044 GBV_ILN_2048 GBV_ILN_2049 GBV_ILN_2050 GBV_ILN_2055 GBV_ILN_2056 GBV_ILN_2059 GBV_ILN_2061 GBV_ILN_2064 GBV_ILN_2088 GBV_ILN_2106 GBV_ILN_2110 GBV_ILN_2112 GBV_ILN_2122 GBV_ILN_2129 GBV_ILN_2143 GBV_ILN_2152 GBV_ILN_2153 GBV_ILN_2190 GBV_ILN_2232 GBV_ILN_2336 GBV_ILN_2470 GBV_ILN_2507 GBV_ILN_4012 GBV_ILN_4035 GBV_ILN_4037 GBV_ILN_4112 GBV_ILN_4125 GBV_ILN_4126 GBV_ILN_4242 GBV_ILN_4249 GBV_ILN_4251 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_4326 GBV_ILN_4333 GBV_ILN_4334 GBV_ILN_4335 GBV_ILN_4338 GBV_ILN_4367 GBV_ILN_4393 GBV_ILN_4700 AR 35 2023 2 102495- |
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10.1016/j.jksus.2022.102495 doi (DE-627)DOAJ024840831 (DE-599)DOAJ2f63f4c357d240aaacdb6688c0bf51fd DE-627 ger DE-627 rakwb eng Q1-390 Soo-In Sohn verfasserin aut Rapid discrimination of Brassica napus varieties using visible and Near-infrared (Vis-NIR) spectroscopy 2023 Text txt rdacontent Computermedien c rdamedia Online-Ressource cr rdacarrier Brassica napus is an oilseed plant that is mostly used to produce edible oils, industrial oils, modified lipids and biofuels. The number of varieties/cultivars is high for the species, owing to their higher level of economic use. The aim of this study is to assess the use of visible-near infrared (Vis-NIR) spectroscopy in combination with multiple chemometric methods that have been explored for the discrimination of eight Brassica napus varieties in Korea. In this study, the spectra from leaves of the eight B. napus varieties were measured in the Vis-NIR spectra in the range of 325–1075 nm with a stepping of 1.5 nm in reflectance mode. The spectral data were preprocessed with three different preprocessing methods and eight different chemometric analyses were used for effective discrimination. After the outlier detection, the samples were split into two sets, one serving as a calibration set and the remaining one as a validation set. When using multiple preprocessing and chemometric methods for the discrimination, the maximum classification accuracy was witnessed in the combination of standard normal variate and support vector machine up to 98.2 %. The use of Savitzky-Golay filter smoothing as a preprocessing method had the best and most satisfactory discrimination of all other chemometric methods. The results suggest that the use of handheld Vis-NIR spectroscopy in combination with chemometric approaches can be used as an effective tool for the discrimination of B. napus varieties in the field. Visible-near infrared Spectroscopy Brassica napus Chemometrics Deep learning Preprocessing Science (General) Subramani Pandian verfasserin aut John-Lewis Zinia Zaukuu verfasserin aut Young-Ju Oh verfasserin aut Yong-Ho Lee verfasserin aut Eun-Kyoung Shin verfasserin aut Senthil Kumar Thamilarasan verfasserin aut Hyeon-Jung Kang verfasserin aut Tae-Hun Ryu verfasserin aut Woo-Suk Cho verfasserin aut In Journal of King Saud University: Science Elsevier, 2016 35(2023), 2, Seite 102495- (DE-627)608942790 (DE-600)2514731-6 10183647 nnns volume:35 year:2023 number:2 pages:102495- https://doi.org/10.1016/j.jksus.2022.102495 kostenfrei https://doaj.org/article/2f63f4c357d240aaacdb6688c0bf51fd kostenfrei http://www.sciencedirect.com/science/article/pii/S1018364722006760 kostenfrei https://doaj.org/toc/1018-3647 Journal toc kostenfrei GBV_USEFLAG_A SYSFLAG_A GBV_DOAJ GBV_ILN_20 GBV_ILN_22 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_95 GBV_ILN_105 GBV_ILN_110 GBV_ILN_151 GBV_ILN_161 GBV_ILN_171 GBV_ILN_213 GBV_ILN_224 GBV_ILN_230 GBV_ILN_285 GBV_ILN_293 GBV_ILN_602 GBV_ILN_2001 GBV_ILN_2003 GBV_ILN_2004 GBV_ILN_2005 GBV_ILN_2006 GBV_ILN_2007 GBV_ILN_2008 GBV_ILN_2009 GBV_ILN_2010 GBV_ILN_2011 GBV_ILN_2014 GBV_ILN_2015 GBV_ILN_2020 GBV_ILN_2021 GBV_ILN_2025 GBV_ILN_2026 GBV_ILN_2027 GBV_ILN_2034 GBV_ILN_2038 GBV_ILN_2044 GBV_ILN_2048 GBV_ILN_2049 GBV_ILN_2050 GBV_ILN_2055 GBV_ILN_2056 GBV_ILN_2059 GBV_ILN_2061 GBV_ILN_2064 GBV_ILN_2088 GBV_ILN_2106 GBV_ILN_2110 GBV_ILN_2112 GBV_ILN_2122 GBV_ILN_2129 GBV_ILN_2143 GBV_ILN_2152 GBV_ILN_2153 GBV_ILN_2190 GBV_ILN_2232 GBV_ILN_2336 GBV_ILN_2470 GBV_ILN_2507 GBV_ILN_4012 GBV_ILN_4035 GBV_ILN_4037 GBV_ILN_4112 GBV_ILN_4125 GBV_ILN_4126 GBV_ILN_4242 GBV_ILN_4249 GBV_ILN_4251 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_4326 GBV_ILN_4333 GBV_ILN_4334 GBV_ILN_4335 GBV_ILN_4338 GBV_ILN_4367 GBV_ILN_4393 GBV_ILN_4700 AR 35 2023 2 102495- |
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Soo-In Sohn @@aut@@ Subramani Pandian @@aut@@ John-Lewis Zinia Zaukuu @@aut@@ Young-Ju Oh @@aut@@ Yong-Ho Lee @@aut@@ Eun-Kyoung Shin @@aut@@ Senthil Kumar Thamilarasan @@aut@@ Hyeon-Jung Kang @@aut@@ Tae-Hun Ryu @@aut@@ Woo-Suk Cho @@aut@@ |
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rapid discrimination of brassica napus varieties using visible and near-infrared (vis-nir) spectroscopy |
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Rapid discrimination of Brassica napus varieties using visible and Near-infrared (Vis-NIR) spectroscopy |
| abstract |
Brassica napus is an oilseed plant that is mostly used to produce edible oils, industrial oils, modified lipids and biofuels. The number of varieties/cultivars is high for the species, owing to their higher level of economic use. The aim of this study is to assess the use of visible-near infrared (Vis-NIR) spectroscopy in combination with multiple chemometric methods that have been explored for the discrimination of eight Brassica napus varieties in Korea. In this study, the spectra from leaves of the eight B. napus varieties were measured in the Vis-NIR spectra in the range of 325–1075 nm with a stepping of 1.5 nm in reflectance mode. The spectral data were preprocessed with three different preprocessing methods and eight different chemometric analyses were used for effective discrimination. After the outlier detection, the samples were split into two sets, one serving as a calibration set and the remaining one as a validation set. When using multiple preprocessing and chemometric methods for the discrimination, the maximum classification accuracy was witnessed in the combination of standard normal variate and support vector machine up to 98.2 %. The use of Savitzky-Golay filter smoothing as a preprocessing method had the best and most satisfactory discrimination of all other chemometric methods. The results suggest that the use of handheld Vis-NIR spectroscopy in combination with chemometric approaches can be used as an effective tool for the discrimination of B. napus varieties in the field. |
| abstractGer |
Brassica napus is an oilseed plant that is mostly used to produce edible oils, industrial oils, modified lipids and biofuels. The number of varieties/cultivars is high for the species, owing to their higher level of economic use. The aim of this study is to assess the use of visible-near infrared (Vis-NIR) spectroscopy in combination with multiple chemometric methods that have been explored for the discrimination of eight Brassica napus varieties in Korea. In this study, the spectra from leaves of the eight B. napus varieties were measured in the Vis-NIR spectra in the range of 325–1075 nm with a stepping of 1.5 nm in reflectance mode. The spectral data were preprocessed with three different preprocessing methods and eight different chemometric analyses were used for effective discrimination. After the outlier detection, the samples were split into two sets, one serving as a calibration set and the remaining one as a validation set. When using multiple preprocessing and chemometric methods for the discrimination, the maximum classification accuracy was witnessed in the combination of standard normal variate and support vector machine up to 98.2 %. The use of Savitzky-Golay filter smoothing as a preprocessing method had the best and most satisfactory discrimination of all other chemometric methods. The results suggest that the use of handheld Vis-NIR spectroscopy in combination with chemometric approaches can be used as an effective tool for the discrimination of B. napus varieties in the field. |
| abstract_unstemmed |
Brassica napus is an oilseed plant that is mostly used to produce edible oils, industrial oils, modified lipids and biofuels. The number of varieties/cultivars is high for the species, owing to their higher level of economic use. The aim of this study is to assess the use of visible-near infrared (Vis-NIR) spectroscopy in combination with multiple chemometric methods that have been explored for the discrimination of eight Brassica napus varieties in Korea. In this study, the spectra from leaves of the eight B. napus varieties were measured in the Vis-NIR spectra in the range of 325–1075 nm with a stepping of 1.5 nm in reflectance mode. The spectral data were preprocessed with three different preprocessing methods and eight different chemometric analyses were used for effective discrimination. After the outlier detection, the samples were split into two sets, one serving as a calibration set and the remaining one as a validation set. When using multiple preprocessing and chemometric methods for the discrimination, the maximum classification accuracy was witnessed in the combination of standard normal variate and support vector machine up to 98.2 %. The use of Savitzky-Golay filter smoothing as a preprocessing method had the best and most satisfactory discrimination of all other chemometric methods. The results suggest that the use of handheld Vis-NIR spectroscopy in combination with chemometric approaches can be used as an effective tool for the discrimination of B. napus varieties in the field. |
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