Discrimination of mung beans according to climate and growing region by untargeted metabolomics coupled with machine learning methods
Mung bean is an important pulse in China owing to its nutritional and bioactive properties, however, the quality characteristics of mung beans from different climate zones and growing regions in China remain unknown. In the present study, an untargeted metabolomics approach based on ultra-high perfo...
Ausführliche Beschreibung
Autor*in: |
He, Lei [verfasserIn] Hu, Qian [verfasserIn] Yu, Yue [verfasserIn] Yu, Yaoxian [verfasserIn] Yu, Ning [verfasserIn] Chen, Ying [verfasserIn] |
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E-Artikel |
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Sprache: |
Englisch |
Erschienen: |
2023 |
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Übergeordnetes Werk: |
Enthalten in: Food control - Amsterdam [u.a.] : Elsevier Science, 1990, 153 |
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Übergeordnetes Werk: |
volume:153 |
DOI / URN: |
10.1016/j.foodcont.2023.109927 |
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Katalog-ID: |
ELV060813067 |
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245 | 1 | 0 | |a Discrimination of mung beans according to climate and growing region by untargeted metabolomics coupled with machine learning methods |
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520 | |a Mung bean is an important pulse in China owing to its nutritional and bioactive properties, however, the quality characteristics of mung beans from different climate zones and growing regions in China remain unknown. In the present study, an untargeted metabolomics approach based on ultra-high performance liquid chromatography-quadrupole time-of-flight mass spectrometry (UPLC-QTOF-MS) coupled with machine learning was used to compare and discriminate mung beans from different climate zones and growing regions. Based on orthogonal partial least squares-discriminant analysis (OPLS-DA), 39 and 35 differential metabolites were identified in mung bean samples among three climate zones and 11 growing regions, respectively. The differential metabolites mainly included lipids, flavonoids, soyasaponins, amino acids, peptides, indoles and their derivatives, and acids. Random forest (RF) and support vector machine (SVM) models were established, optimized, and used to discriminate mung beans from different climate zones and growing regions. The SVM models performed better than the RF models in predicting both the climate zone and growing region of mung beans, with accuracies of 100% and 98.72%, respectively. Our results demonstrate that untargeted metabolomics coupled with machine learning could be a powerful tool for discriminating of mung beans from different climate zones and growing regions. | ||
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650 | 4 | |a Untargeted metabolomics | |
650 | 4 | |a Machine learning | |
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700 | 1 | |a Yu, Yaoxian |e verfasserin |4 aut | |
700 | 1 | |a Yu, Ning |e verfasserin |4 aut | |
700 | 1 | |a Chen, Ying |e verfasserin |4 aut | |
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10.1016/j.foodcont.2023.109927 doi (DE-627)ELV060813067 (ELSEVIER)S0956-7135(23)00327-4 DE-627 ger DE-627 rda eng 630 640 VZ 58.34 bkl He, Lei verfasserin aut Discrimination of mung beans according to climate and growing region by untargeted metabolomics coupled with machine learning methods 2023 nicht spezifiziert zzz rdacontent Computermedien c rdamedia Online-Ressource cr rdacarrier Mung bean is an important pulse in China owing to its nutritional and bioactive properties, however, the quality characteristics of mung beans from different climate zones and growing regions in China remain unknown. In the present study, an untargeted metabolomics approach based on ultra-high performance liquid chromatography-quadrupole time-of-flight mass spectrometry (UPLC-QTOF-MS) coupled with machine learning was used to compare and discriminate mung beans from different climate zones and growing regions. Based on orthogonal partial least squares-discriminant analysis (OPLS-DA), 39 and 35 differential metabolites were identified in mung bean samples among three climate zones and 11 growing regions, respectively. The differential metabolites mainly included lipids, flavonoids, soyasaponins, amino acids, peptides, indoles and their derivatives, and acids. Random forest (RF) and support vector machine (SVM) models were established, optimized, and used to discriminate mung beans from different climate zones and growing regions. The SVM models performed better than the RF models in predicting both the climate zone and growing region of mung beans, with accuracies of 100% and 98.72%, respectively. Our results demonstrate that untargeted metabolomics coupled with machine learning could be a powerful tool for discriminating of mung beans from different climate zones and growing regions. Mung bean Untargeted metabolomics Machine learning Climate zone Growing region Hu, Qian verfasserin aut Yu, Yue verfasserin aut Yu, Yaoxian verfasserin aut Yu, Ning verfasserin aut Chen, Ying verfasserin aut Enthalten in Food control Amsterdam [u.a.] : Elsevier Science, 1990 153 Online-Ressource (DE-627)320604772 (DE-600)2020604-5 (DE-576)259271756 0956-7135 nnns volume:153 GBV_USEFLAG_U GBV_ELV SYSFLAG_U GBV_ILN_20 GBV_ILN_22 GBV_ILN_23 GBV_ILN_24 GBV_ILN_31 GBV_ILN_32 GBV_ILN_40 GBV_ILN_60 GBV_ILN_62 GBV_ILN_65 GBV_ILN_69 GBV_ILN_70 GBV_ILN_73 GBV_ILN_74 GBV_ILN_90 GBV_ILN_100 GBV_ILN_105 GBV_ILN_110 GBV_ILN_151 GBV_ILN_187 GBV_ILN_213 GBV_ILN_224 GBV_ILN_230 GBV_ILN_370 GBV_ILN_602 GBV_ILN_702 GBV_ILN_2001 GBV_ILN_2003 GBV_ILN_2004 GBV_ILN_2005 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_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_2111 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_4035 GBV_ILN_4037 GBV_ILN_4112 GBV_ILN_4125 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_4338 GBV_ILN_4393 GBV_ILN_4700 58.34 Lebensmitteltechnologie VZ AR 153 |
spelling |
10.1016/j.foodcont.2023.109927 doi (DE-627)ELV060813067 (ELSEVIER)S0956-7135(23)00327-4 DE-627 ger DE-627 rda eng 630 640 VZ 58.34 bkl He, Lei verfasserin aut Discrimination of mung beans according to climate and growing region by untargeted metabolomics coupled with machine learning methods 2023 nicht spezifiziert zzz rdacontent Computermedien c rdamedia Online-Ressource cr rdacarrier Mung bean is an important pulse in China owing to its nutritional and bioactive properties, however, the quality characteristics of mung beans from different climate zones and growing regions in China remain unknown. In the present study, an untargeted metabolomics approach based on ultra-high performance liquid chromatography-quadrupole time-of-flight mass spectrometry (UPLC-QTOF-MS) coupled with machine learning was used to compare and discriminate mung beans from different climate zones and growing regions. Based on orthogonal partial least squares-discriminant analysis (OPLS-DA), 39 and 35 differential metabolites were identified in mung bean samples among three climate zones and 11 growing regions, respectively. The differential metabolites mainly included lipids, flavonoids, soyasaponins, amino acids, peptides, indoles and their derivatives, and acids. Random forest (RF) and support vector machine (SVM) models were established, optimized, and used to discriminate mung beans from different climate zones and growing regions. The SVM models performed better than the RF models in predicting both the climate zone and growing region of mung beans, with accuracies of 100% and 98.72%, respectively. Our results demonstrate that untargeted metabolomics coupled with machine learning could be a powerful tool for discriminating of mung beans from different climate zones and growing regions. Mung bean Untargeted metabolomics Machine learning Climate zone Growing region Hu, Qian verfasserin aut Yu, Yue verfasserin aut Yu, Yaoxian verfasserin aut Yu, Ning verfasserin aut Chen, Ying verfasserin aut Enthalten in Food control Amsterdam [u.a.] : Elsevier Science, 1990 153 Online-Ressource (DE-627)320604772 (DE-600)2020604-5 (DE-576)259271756 0956-7135 nnns volume:153 GBV_USEFLAG_U GBV_ELV SYSFLAG_U GBV_ILN_20 GBV_ILN_22 GBV_ILN_23 GBV_ILN_24 GBV_ILN_31 GBV_ILN_32 GBV_ILN_40 GBV_ILN_60 GBV_ILN_62 GBV_ILN_65 GBV_ILN_69 GBV_ILN_70 GBV_ILN_73 GBV_ILN_74 GBV_ILN_90 GBV_ILN_100 GBV_ILN_105 GBV_ILN_110 GBV_ILN_151 GBV_ILN_187 GBV_ILN_213 GBV_ILN_224 GBV_ILN_230 GBV_ILN_370 GBV_ILN_602 GBV_ILN_702 GBV_ILN_2001 GBV_ILN_2003 GBV_ILN_2004 GBV_ILN_2005 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_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_2111 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_4035 GBV_ILN_4037 GBV_ILN_4112 GBV_ILN_4125 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_4338 GBV_ILN_4393 GBV_ILN_4700 58.34 Lebensmitteltechnologie VZ AR 153 |
allfields_unstemmed |
10.1016/j.foodcont.2023.109927 doi (DE-627)ELV060813067 (ELSEVIER)S0956-7135(23)00327-4 DE-627 ger DE-627 rda eng 630 640 VZ 58.34 bkl He, Lei verfasserin aut Discrimination of mung beans according to climate and growing region by untargeted metabolomics coupled with machine learning methods 2023 nicht spezifiziert zzz rdacontent Computermedien c rdamedia Online-Ressource cr rdacarrier Mung bean is an important pulse in China owing to its nutritional and bioactive properties, however, the quality characteristics of mung beans from different climate zones and growing regions in China remain unknown. In the present study, an untargeted metabolomics approach based on ultra-high performance liquid chromatography-quadrupole time-of-flight mass spectrometry (UPLC-QTOF-MS) coupled with machine learning was used to compare and discriminate mung beans from different climate zones and growing regions. Based on orthogonal partial least squares-discriminant analysis (OPLS-DA), 39 and 35 differential metabolites were identified in mung bean samples among three climate zones and 11 growing regions, respectively. The differential metabolites mainly included lipids, flavonoids, soyasaponins, amino acids, peptides, indoles and their derivatives, and acids. Random forest (RF) and support vector machine (SVM) models were established, optimized, and used to discriminate mung beans from different climate zones and growing regions. The SVM models performed better than the RF models in predicting both the climate zone and growing region of mung beans, with accuracies of 100% and 98.72%, respectively. Our results demonstrate that untargeted metabolomics coupled with machine learning could be a powerful tool for discriminating of mung beans from different climate zones and growing regions. Mung bean Untargeted metabolomics Machine learning Climate zone Growing region Hu, Qian verfasserin aut Yu, Yue verfasserin aut Yu, Yaoxian verfasserin aut Yu, Ning verfasserin aut Chen, Ying verfasserin aut Enthalten in Food control Amsterdam [u.a.] : Elsevier Science, 1990 153 Online-Ressource (DE-627)320604772 (DE-600)2020604-5 (DE-576)259271756 0956-7135 nnns volume:153 GBV_USEFLAG_U GBV_ELV SYSFLAG_U GBV_ILN_20 GBV_ILN_22 GBV_ILN_23 GBV_ILN_24 GBV_ILN_31 GBV_ILN_32 GBV_ILN_40 GBV_ILN_60 GBV_ILN_62 GBV_ILN_65 GBV_ILN_69 GBV_ILN_70 GBV_ILN_73 GBV_ILN_74 GBV_ILN_90 GBV_ILN_100 GBV_ILN_105 GBV_ILN_110 GBV_ILN_151 GBV_ILN_187 GBV_ILN_213 GBV_ILN_224 GBV_ILN_230 GBV_ILN_370 GBV_ILN_602 GBV_ILN_702 GBV_ILN_2001 GBV_ILN_2003 GBV_ILN_2004 GBV_ILN_2005 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_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_2111 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_4035 GBV_ILN_4037 GBV_ILN_4112 GBV_ILN_4125 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_4338 GBV_ILN_4393 GBV_ILN_4700 58.34 Lebensmitteltechnologie VZ AR 153 |
allfieldsGer |
10.1016/j.foodcont.2023.109927 doi (DE-627)ELV060813067 (ELSEVIER)S0956-7135(23)00327-4 DE-627 ger DE-627 rda eng 630 640 VZ 58.34 bkl He, Lei verfasserin aut Discrimination of mung beans according to climate and growing region by untargeted metabolomics coupled with machine learning methods 2023 nicht spezifiziert zzz rdacontent Computermedien c rdamedia Online-Ressource cr rdacarrier Mung bean is an important pulse in China owing to its nutritional and bioactive properties, however, the quality characteristics of mung beans from different climate zones and growing regions in China remain unknown. In the present study, an untargeted metabolomics approach based on ultra-high performance liquid chromatography-quadrupole time-of-flight mass spectrometry (UPLC-QTOF-MS) coupled with machine learning was used to compare and discriminate mung beans from different climate zones and growing regions. Based on orthogonal partial least squares-discriminant analysis (OPLS-DA), 39 and 35 differential metabolites were identified in mung bean samples among three climate zones and 11 growing regions, respectively. The differential metabolites mainly included lipids, flavonoids, soyasaponins, amino acids, peptides, indoles and their derivatives, and acids. Random forest (RF) and support vector machine (SVM) models were established, optimized, and used to discriminate mung beans from different climate zones and growing regions. The SVM models performed better than the RF models in predicting both the climate zone and growing region of mung beans, with accuracies of 100% and 98.72%, respectively. Our results demonstrate that untargeted metabolomics coupled with machine learning could be a powerful tool for discriminating of mung beans from different climate zones and growing regions. Mung bean Untargeted metabolomics Machine learning Climate zone Growing region Hu, Qian verfasserin aut Yu, Yue verfasserin aut Yu, Yaoxian verfasserin aut Yu, Ning verfasserin aut Chen, Ying verfasserin aut Enthalten in Food control Amsterdam [u.a.] : Elsevier Science, 1990 153 Online-Ressource (DE-627)320604772 (DE-600)2020604-5 (DE-576)259271756 0956-7135 nnns volume:153 GBV_USEFLAG_U GBV_ELV SYSFLAG_U GBV_ILN_20 GBV_ILN_22 GBV_ILN_23 GBV_ILN_24 GBV_ILN_31 GBV_ILN_32 GBV_ILN_40 GBV_ILN_60 GBV_ILN_62 GBV_ILN_65 GBV_ILN_69 GBV_ILN_70 GBV_ILN_73 GBV_ILN_74 GBV_ILN_90 GBV_ILN_100 GBV_ILN_105 GBV_ILN_110 GBV_ILN_151 GBV_ILN_187 GBV_ILN_213 GBV_ILN_224 GBV_ILN_230 GBV_ILN_370 GBV_ILN_602 GBV_ILN_702 GBV_ILN_2001 GBV_ILN_2003 GBV_ILN_2004 GBV_ILN_2005 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_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_2111 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_4035 GBV_ILN_4037 GBV_ILN_4112 GBV_ILN_4125 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_4338 GBV_ILN_4393 GBV_ILN_4700 58.34 Lebensmitteltechnologie VZ AR 153 |
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10.1016/j.foodcont.2023.109927 doi (DE-627)ELV060813067 (ELSEVIER)S0956-7135(23)00327-4 DE-627 ger DE-627 rda eng 630 640 VZ 58.34 bkl He, Lei verfasserin aut Discrimination of mung beans according to climate and growing region by untargeted metabolomics coupled with machine learning methods 2023 nicht spezifiziert zzz rdacontent Computermedien c rdamedia Online-Ressource cr rdacarrier Mung bean is an important pulse in China owing to its nutritional and bioactive properties, however, the quality characteristics of mung beans from different climate zones and growing regions in China remain unknown. In the present study, an untargeted metabolomics approach based on ultra-high performance liquid chromatography-quadrupole time-of-flight mass spectrometry (UPLC-QTOF-MS) coupled with machine learning was used to compare and discriminate mung beans from different climate zones and growing regions. Based on orthogonal partial least squares-discriminant analysis (OPLS-DA), 39 and 35 differential metabolites were identified in mung bean samples among three climate zones and 11 growing regions, respectively. The differential metabolites mainly included lipids, flavonoids, soyasaponins, amino acids, peptides, indoles and their derivatives, and acids. Random forest (RF) and support vector machine (SVM) models were established, optimized, and used to discriminate mung beans from different climate zones and growing regions. The SVM models performed better than the RF models in predicting both the climate zone and growing region of mung beans, with accuracies of 100% and 98.72%, respectively. Our results demonstrate that untargeted metabolomics coupled with machine learning could be a powerful tool for discriminating of mung beans from different climate zones and growing regions. Mung bean Untargeted metabolomics Machine learning Climate zone Growing region Hu, Qian verfasserin aut Yu, Yue verfasserin aut Yu, Yaoxian verfasserin aut Yu, Ning verfasserin aut Chen, Ying verfasserin aut Enthalten in Food control Amsterdam [u.a.] : Elsevier Science, 1990 153 Online-Ressource (DE-627)320604772 (DE-600)2020604-5 (DE-576)259271756 0956-7135 nnns volume:153 GBV_USEFLAG_U GBV_ELV SYSFLAG_U GBV_ILN_20 GBV_ILN_22 GBV_ILN_23 GBV_ILN_24 GBV_ILN_31 GBV_ILN_32 GBV_ILN_40 GBV_ILN_60 GBV_ILN_62 GBV_ILN_65 GBV_ILN_69 GBV_ILN_70 GBV_ILN_73 GBV_ILN_74 GBV_ILN_90 GBV_ILN_100 GBV_ILN_105 GBV_ILN_110 GBV_ILN_151 GBV_ILN_187 GBV_ILN_213 GBV_ILN_224 GBV_ILN_230 GBV_ILN_370 GBV_ILN_602 GBV_ILN_702 GBV_ILN_2001 GBV_ILN_2003 GBV_ILN_2004 GBV_ILN_2005 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_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_2111 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_4035 GBV_ILN_4037 GBV_ILN_4112 GBV_ILN_4125 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_4338 GBV_ILN_4393 GBV_ILN_4700 58.34 Lebensmitteltechnologie VZ AR 153 |
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title |
Discrimination of mung beans according to climate and growing region by untargeted metabolomics coupled with machine learning methods |
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Discrimination of mung beans according to climate and growing region by untargeted metabolomics coupled with machine learning methods |
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He, Lei |
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He, Lei Hu, Qian Yu, Yue Yu, Yaoxian Yu, Ning Chen, Ying |
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discrimination of mung beans according to climate and growing region by untargeted metabolomics coupled with machine learning methods |
title_auth |
Discrimination of mung beans according to climate and growing region by untargeted metabolomics coupled with machine learning methods |
abstract |
Mung bean is an important pulse in China owing to its nutritional and bioactive properties, however, the quality characteristics of mung beans from different climate zones and growing regions in China remain unknown. In the present study, an untargeted metabolomics approach based on ultra-high performance liquid chromatography-quadrupole time-of-flight mass spectrometry (UPLC-QTOF-MS) coupled with machine learning was used to compare and discriminate mung beans from different climate zones and growing regions. Based on orthogonal partial least squares-discriminant analysis (OPLS-DA), 39 and 35 differential metabolites were identified in mung bean samples among three climate zones and 11 growing regions, respectively. The differential metabolites mainly included lipids, flavonoids, soyasaponins, amino acids, peptides, indoles and their derivatives, and acids. Random forest (RF) and support vector machine (SVM) models were established, optimized, and used to discriminate mung beans from different climate zones and growing regions. The SVM models performed better than the RF models in predicting both the climate zone and growing region of mung beans, with accuracies of 100% and 98.72%, respectively. Our results demonstrate that untargeted metabolomics coupled with machine learning could be a powerful tool for discriminating of mung beans from different climate zones and growing regions. |
abstractGer |
Mung bean is an important pulse in China owing to its nutritional and bioactive properties, however, the quality characteristics of mung beans from different climate zones and growing regions in China remain unknown. In the present study, an untargeted metabolomics approach based on ultra-high performance liquid chromatography-quadrupole time-of-flight mass spectrometry (UPLC-QTOF-MS) coupled with machine learning was used to compare and discriminate mung beans from different climate zones and growing regions. Based on orthogonal partial least squares-discriminant analysis (OPLS-DA), 39 and 35 differential metabolites were identified in mung bean samples among three climate zones and 11 growing regions, respectively. The differential metabolites mainly included lipids, flavonoids, soyasaponins, amino acids, peptides, indoles and their derivatives, and acids. Random forest (RF) and support vector machine (SVM) models were established, optimized, and used to discriminate mung beans from different climate zones and growing regions. The SVM models performed better than the RF models in predicting both the climate zone and growing region of mung beans, with accuracies of 100% and 98.72%, respectively. Our results demonstrate that untargeted metabolomics coupled with machine learning could be a powerful tool for discriminating of mung beans from different climate zones and growing regions. |
abstract_unstemmed |
Mung bean is an important pulse in China owing to its nutritional and bioactive properties, however, the quality characteristics of mung beans from different climate zones and growing regions in China remain unknown. In the present study, an untargeted metabolomics approach based on ultra-high performance liquid chromatography-quadrupole time-of-flight mass spectrometry (UPLC-QTOF-MS) coupled with machine learning was used to compare and discriminate mung beans from different climate zones and growing regions. Based on orthogonal partial least squares-discriminant analysis (OPLS-DA), 39 and 35 differential metabolites were identified in mung bean samples among three climate zones and 11 growing regions, respectively. The differential metabolites mainly included lipids, flavonoids, soyasaponins, amino acids, peptides, indoles and their derivatives, and acids. Random forest (RF) and support vector machine (SVM) models were established, optimized, and used to discriminate mung beans from different climate zones and growing regions. The SVM models performed better than the RF models in predicting both the climate zone and growing region of mung beans, with accuracies of 100% and 98.72%, respectively. Our results demonstrate that untargeted metabolomics coupled with machine learning could be a powerful tool for discriminating of mung beans from different climate zones and growing regions. |
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Discrimination of mung beans according to climate and growing region by untargeted metabolomics coupled with machine learning methods |
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