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...
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Autor*in:	He, Lei [verfasserIn] Hu, Qian [verfasserIn] Yu, Yue [verfasserIn] Yu, Yaoxian [verfasserIn] Yu, Ning [verfasserIn] Chen, Ying [verfasserIn]

Format:	E-Artikel
Sprache:	Englisch

Erschienen:	2023

Schlagwörter:	Mung bean Untargeted metabolomics Machine learning Climate zone Growing region

Übergeordnetes Werk:	Enthalten in: Food control - Amsterdam [u.a.] : Elsevier Science, 1990, 153
Übergeordnetes Werk:	volume:153

DOI / URN:	10.1016/j.foodcont.2023.109927

Katalog-ID:	ELV060813067

Internformat


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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.
650		4	\|a Mung bean
650		4	\|a Untargeted metabolomics
650		4	\|a Machine learning
650		4	\|a Climate zone
650		4	\|a Growing region
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700	1		\|a Yu, Ning \|e verfasserin \|4 aut
700	1		\|a Chen, Ying \|e verfasserin \|4 aut
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publishDate	2023
allfields	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
allfieldsSound	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
language	English
source	Enthalten in Food control 153 volume:153
sourceStr	Enthalten in Food control 153 volume:153
format_phy_str_mv	Article
bklname	Lebensmitteltechnologie
institution	findex.gbv.de
topic_facet	Mung bean Untargeted metabolomics Machine learning Climate zone Growing region
dewey-raw	630
isfreeaccess_bool	false
container_title	Food control
authorswithroles_txt_mv	He, Lei @@aut@@ Hu, Qian @@aut@@ Yu, Yue @@aut@@ Yu, Yaoxian @@aut@@ Yu, Ning @@aut@@ Chen, Ying @@aut@@
publishDateDaySort_date	2023-01-01T00:00:00Z
hierarchy_top_id	320604772
dewey-sort	3630
id	ELV060813067
language_de	englisch
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author	He, Lei
spellingShingle	He, Lei ddc 630 bkl 58.34 misc Mung bean misc Untargeted metabolomics misc Machine learning misc Climate zone misc Growing region Discrimination of mung beans according to climate and growing region by untargeted metabolomics coupled with machine learning methods
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topic_title	630 640 VZ 58.34 bkl Discrimination of mung beans according to climate and growing region by untargeted metabolomics coupled with machine learning methods Mung bean Untargeted metabolomics Machine learning Climate zone Growing region
topic	ddc 630 bkl 58.34 misc Mung bean misc Untargeted metabolomics misc Machine learning misc Climate zone misc Growing region
topic_unstemmed	ddc 630 bkl 58.34 misc Mung bean misc Untargeted metabolomics misc Machine learning misc Climate zone misc Growing region
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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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title_full	Discrimination of mung beans according to climate and growing region by untargeted metabolomics coupled with machine learning methods
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title_sort	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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title_short	Discrimination of mung beans according to climate and growing region by untargeted metabolomics coupled with machine learning methods
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author2	Hu, Qian Yu, Yue Yu, Yaoxian Yu, Ning Chen, Ying
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doi_str	10.1016/j.foodcont.2023.109927
up_date	2024-07-06T16:51:57.042Z
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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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