Possibilities on the application of vibrational spectroscopy and data analytics in precision nutrition

The main role of precision nutrition is to personalize nutrition recommendations based not only on historical diet and phenotype data, but also on additional information that includes the genotype or gene expression, the microbiome, proteome, and metabolome of a given individual. In recent years, cu...
Ausführliche Beschreibung

Gespeichert in:

Autor*in:	Dongdong, Ni [verfasserIn] Cozzolino, Daniel [verfasserIn]

Format:	E-Artikel
Sprache:	Englisch

Erschienen:	2023

Schlagwörter:	Precision nutrition Data analytics Vibrational spectroscopy Sensors

Übergeordnetes Werk:	Enthalten in: Trends in analytical chemistry - Amsterdam : Elsevier, 1981, 163
Übergeordnetes Werk:	volume:163

DOI / URN:	10.1016/j.trac.2023.117067

Katalog-ID:	ELV009886990

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912			\|a GBV_ILN_90
912			\|a GBV_ILN_95
912			\|a GBV_ILN_100
912			\|a GBV_ILN_101
912			\|a GBV_ILN_105
912			\|a GBV_ILN_110
912			\|a GBV_ILN_150
912			\|a GBV_ILN_151
912			\|a GBV_ILN_187
912			\|a GBV_ILN_213
912			\|a GBV_ILN_224
912			\|a GBV_ILN_230
912			\|a GBV_ILN_370
912			\|a GBV_ILN_602
912			\|a GBV_ILN_702
912			\|a GBV_ILN_2001
912			\|a GBV_ILN_2003
912			\|a GBV_ILN_2004
912			\|a GBV_ILN_2005
912			\|a GBV_ILN_2007
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912			\|a GBV_ILN_2015
912			\|a GBV_ILN_2020
912			\|a GBV_ILN_2021
912			\|a GBV_ILN_2025
912			\|a GBV_ILN_2026
912			\|a GBV_ILN_2027
912			\|a GBV_ILN_2034
912			\|a GBV_ILN_2044
912			\|a GBV_ILN_2048
912			\|a GBV_ILN_2049
912			\|a GBV_ILN_2050
912			\|a GBV_ILN_2055
912			\|a GBV_ILN_2056
912			\|a GBV_ILN_2059
912			\|a GBV_ILN_2061
912			\|a GBV_ILN_2064
912			\|a GBV_ILN_2088
912			\|a GBV_ILN_2106
912			\|a GBV_ILN_2110
912			\|a GBV_ILN_2111
912			\|a GBV_ILN_2112
912			\|a GBV_ILN_2122
912			\|a GBV_ILN_2129
912			\|a GBV_ILN_2143
912			\|a GBV_ILN_2152
912			\|a GBV_ILN_2153
912			\|a GBV_ILN_2190
912			\|a GBV_ILN_2232
912			\|a GBV_ILN_2336
912			\|a GBV_ILN_2470
912			\|a GBV_ILN_2507
912			\|a GBV_ILN_4035
912			\|a GBV_ILN_4037
912			\|a GBV_ILN_4112
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allfields	10.1016/j.trac.2023.117067 doi (DE-627)ELV009886990 (ELSEVIER)S0165-9936(23)00154-1 DE-627 ger DE-627 rda eng 540 VZ 35.23 bkl Dongdong, Ni verfasserin aut Possibilities on the application of vibrational spectroscopy and data analytics in precision nutrition 2023 nicht spezifiziert zzz rdacontent Computermedien c rdamedia Online-Ressource cr rdacarrier The main role of precision nutrition is to personalize nutrition recommendations based not only on historical diet and phenotype data, but also on additional information that includes the genotype or gene expression, the microbiome, proteome, and metabolome of a given individual. In recent years, current advances in the field of digital technologies and data analytics (e.g. sensors, machine learning, mathematical modelling) have allowed for the development of practical and robust analytical tools in the field of personalized nutrition. This review discusses the application, utilization and role of non-destructive techniques based in vibrational spectroscopy combined with data analytics (e.g. machine learning techniques) as analytical tool in precision nutrition. Specifically, this paper focused on the analysis of metabolites and other biomarkers in biofluids other than blood (e.g. saliva, urine) using infrared and Raman spectroscopy in human nutrition applications. Precision nutrition Data analytics Vibrational spectroscopy Sensors Cozzolino, Daniel verfasserin (orcid)0000-0001-6247-8817 aut Enthalten in Trends in analytical chemistry Amsterdam : Elsevier, 1981 163 Online-Ressource (DE-627)320516601 (DE-600)2014041-1 (DE-576)098253344 nnns volume:163 GBV_USEFLAG_U GBV_ELV SYSFLAG_U SSG-OLC-PHA 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_95 GBV_ILN_100 GBV_ILN_101 GBV_ILN_105 GBV_ILN_110 GBV_ILN_150 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 35.23 Analytische Chemie: Allgemeines VZ AR 163
spelling	10.1016/j.trac.2023.117067 doi (DE-627)ELV009886990 (ELSEVIER)S0165-9936(23)00154-1 DE-627 ger DE-627 rda eng 540 VZ 35.23 bkl Dongdong, Ni verfasserin aut Possibilities on the application of vibrational spectroscopy and data analytics in precision nutrition 2023 nicht spezifiziert zzz rdacontent Computermedien c rdamedia Online-Ressource cr rdacarrier The main role of precision nutrition is to personalize nutrition recommendations based not only on historical diet and phenotype data, but also on additional information that includes the genotype or gene expression, the microbiome, proteome, and metabolome of a given individual. In recent years, current advances in the field of digital technologies and data analytics (e.g. sensors, machine learning, mathematical modelling) have allowed for the development of practical and robust analytical tools in the field of personalized nutrition. This review discusses the application, utilization and role of non-destructive techniques based in vibrational spectroscopy combined with data analytics (e.g. machine learning techniques) as analytical tool in precision nutrition. Specifically, this paper focused on the analysis of metabolites and other biomarkers in biofluids other than blood (e.g. saliva, urine) using infrared and Raman spectroscopy in human nutrition applications. Precision nutrition Data analytics Vibrational spectroscopy Sensors Cozzolino, Daniel verfasserin (orcid)0000-0001-6247-8817 aut Enthalten in Trends in analytical chemistry Amsterdam : Elsevier, 1981 163 Online-Ressource (DE-627)320516601 (DE-600)2014041-1 (DE-576)098253344 nnns volume:163 GBV_USEFLAG_U GBV_ELV SYSFLAG_U SSG-OLC-PHA 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_95 GBV_ILN_100 GBV_ILN_101 GBV_ILN_105 GBV_ILN_110 GBV_ILN_150 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 35.23 Analytische Chemie: Allgemeines VZ AR 163
allfields_unstemmed	10.1016/j.trac.2023.117067 doi (DE-627)ELV009886990 (ELSEVIER)S0165-9936(23)00154-1 DE-627 ger DE-627 rda eng 540 VZ 35.23 bkl Dongdong, Ni verfasserin aut Possibilities on the application of vibrational spectroscopy and data analytics in precision nutrition 2023 nicht spezifiziert zzz rdacontent Computermedien c rdamedia Online-Ressource cr rdacarrier The main role of precision nutrition is to personalize nutrition recommendations based not only on historical diet and phenotype data, but also on additional information that includes the genotype or gene expression, the microbiome, proteome, and metabolome of a given individual. In recent years, current advances in the field of digital technologies and data analytics (e.g. sensors, machine learning, mathematical modelling) have allowed for the development of practical and robust analytical tools in the field of personalized nutrition. This review discusses the application, utilization and role of non-destructive techniques based in vibrational spectroscopy combined with data analytics (e.g. machine learning techniques) as analytical tool in precision nutrition. Specifically, this paper focused on the analysis of metabolites and other biomarkers in biofluids other than blood (e.g. saliva, urine) using infrared and Raman spectroscopy in human nutrition applications. Precision nutrition Data analytics Vibrational spectroscopy Sensors Cozzolino, Daniel verfasserin (orcid)0000-0001-6247-8817 aut Enthalten in Trends in analytical chemistry Amsterdam : Elsevier, 1981 163 Online-Ressource (DE-627)320516601 (DE-600)2014041-1 (DE-576)098253344 nnns volume:163 GBV_USEFLAG_U GBV_ELV SYSFLAG_U SSG-OLC-PHA 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_95 GBV_ILN_100 GBV_ILN_101 GBV_ILN_105 GBV_ILN_110 GBV_ILN_150 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 35.23 Analytische Chemie: Allgemeines VZ AR 163
allfieldsGer	10.1016/j.trac.2023.117067 doi (DE-627)ELV009886990 (ELSEVIER)S0165-9936(23)00154-1 DE-627 ger DE-627 rda eng 540 VZ 35.23 bkl Dongdong, Ni verfasserin aut Possibilities on the application of vibrational spectroscopy and data analytics in precision nutrition 2023 nicht spezifiziert zzz rdacontent Computermedien c rdamedia Online-Ressource cr rdacarrier The main role of precision nutrition is to personalize nutrition recommendations based not only on historical diet and phenotype data, but also on additional information that includes the genotype or gene expression, the microbiome, proteome, and metabolome of a given individual. In recent years, current advances in the field of digital technologies and data analytics (e.g. sensors, machine learning, mathematical modelling) have allowed for the development of practical and robust analytical tools in the field of personalized nutrition. This review discusses the application, utilization and role of non-destructive techniques based in vibrational spectroscopy combined with data analytics (e.g. machine learning techniques) as analytical tool in precision nutrition. Specifically, this paper focused on the analysis of metabolites and other biomarkers in biofluids other than blood (e.g. saliva, urine) using infrared and Raman spectroscopy in human nutrition applications. Precision nutrition Data analytics Vibrational spectroscopy Sensors Cozzolino, Daniel verfasserin (orcid)0000-0001-6247-8817 aut Enthalten in Trends in analytical chemistry Amsterdam : Elsevier, 1981 163 Online-Ressource (DE-627)320516601 (DE-600)2014041-1 (DE-576)098253344 nnns volume:163 GBV_USEFLAG_U GBV_ELV SYSFLAG_U SSG-OLC-PHA 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_95 GBV_ILN_100 GBV_ILN_101 GBV_ILN_105 GBV_ILN_110 GBV_ILN_150 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 35.23 Analytische Chemie: Allgemeines VZ AR 163
allfieldsSound	10.1016/j.trac.2023.117067 doi (DE-627)ELV009886990 (ELSEVIER)S0165-9936(23)00154-1 DE-627 ger DE-627 rda eng 540 VZ 35.23 bkl Dongdong, Ni verfasserin aut Possibilities on the application of vibrational spectroscopy and data analytics in precision nutrition 2023 nicht spezifiziert zzz rdacontent Computermedien c rdamedia Online-Ressource cr rdacarrier The main role of precision nutrition is to personalize nutrition recommendations based not only on historical diet and phenotype data, but also on additional information that includes the genotype or gene expression, the microbiome, proteome, and metabolome of a given individual. In recent years, current advances in the field of digital technologies and data analytics (e.g. sensors, machine learning, mathematical modelling) have allowed for the development of practical and robust analytical tools in the field of personalized nutrition. This review discusses the application, utilization and role of non-destructive techniques based in vibrational spectroscopy combined with data analytics (e.g. machine learning techniques) as analytical tool in precision nutrition. Specifically, this paper focused on the analysis of metabolites and other biomarkers in biofluids other than blood (e.g. saliva, urine) using infrared and Raman spectroscopy in human nutrition applications. Precision nutrition Data analytics Vibrational spectroscopy Sensors Cozzolino, Daniel verfasserin (orcid)0000-0001-6247-8817 aut Enthalten in Trends in analytical chemistry Amsterdam : Elsevier, 1981 163 Online-Ressource (DE-627)320516601 (DE-600)2014041-1 (DE-576)098253344 nnns volume:163 GBV_USEFLAG_U GBV_ELV SYSFLAG_U SSG-OLC-PHA 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_95 GBV_ILN_100 GBV_ILN_101 GBV_ILN_105 GBV_ILN_110 GBV_ILN_150 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 35.23 Analytische Chemie: Allgemeines VZ AR 163
language	English
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sourceStr	Enthalten in Trends in analytical chemistry 163 volume:163
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container_title	Trends in analytical chemistry
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topic_title	540 VZ 35.23 bkl Possibilities on the application of vibrational spectroscopy and data analytics in precision nutrition Precision nutrition Data analytics Vibrational spectroscopy Sensors
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title_sort	possibilities on the application of vibrational spectroscopy and data analytics in precision nutrition
title_auth	Possibilities on the application of vibrational spectroscopy and data analytics in precision nutrition
abstract	The main role of precision nutrition is to personalize nutrition recommendations based not only on historical diet and phenotype data, but also on additional information that includes the genotype or gene expression, the microbiome, proteome, and metabolome of a given individual. In recent years, current advances in the field of digital technologies and data analytics (e.g. sensors, machine learning, mathematical modelling) have allowed for the development of practical and robust analytical tools in the field of personalized nutrition. This review discusses the application, utilization and role of non-destructive techniques based in vibrational spectroscopy combined with data analytics (e.g. machine learning techniques) as analytical tool in precision nutrition. Specifically, this paper focused on the analysis of metabolites and other biomarkers in biofluids other than blood (e.g. saliva, urine) using infrared and Raman spectroscopy in human nutrition applications.
abstractGer	The main role of precision nutrition is to personalize nutrition recommendations based not only on historical diet and phenotype data, but also on additional information that includes the genotype or gene expression, the microbiome, proteome, and metabolome of a given individual. In recent years, current advances in the field of digital technologies and data analytics (e.g. sensors, machine learning, mathematical modelling) have allowed for the development of practical and robust analytical tools in the field of personalized nutrition. This review discusses the application, utilization and role of non-destructive techniques based in vibrational spectroscopy combined with data analytics (e.g. machine learning techniques) as analytical tool in precision nutrition. Specifically, this paper focused on the analysis of metabolites and other biomarkers in biofluids other than blood (e.g. saliva, urine) using infrared and Raman spectroscopy in human nutrition applications.
abstract_unstemmed	The main role of precision nutrition is to personalize nutrition recommendations based not only on historical diet and phenotype data, but also on additional information that includes the genotype or gene expression, the microbiome, proteome, and metabolome of a given individual. In recent years, current advances in the field of digital technologies and data analytics (e.g. sensors, machine learning, mathematical modelling) have allowed for the development of practical and robust analytical tools in the field of personalized nutrition. This review discusses the application, utilization and role of non-destructive techniques based in vibrational spectroscopy combined with data analytics (e.g. machine learning techniques) as analytical tool in precision nutrition. Specifically, this paper focused on the analysis of metabolites and other biomarkers in biofluids other than blood (e.g. saliva, urine) using infrared and Raman spectroscopy in human nutrition applications.
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title_short	Possibilities on the application of vibrational spectroscopy and data analytics in precision nutrition
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Specifically, this paper focused on the analysis of metabolites and other biomarkers in biofluids other than blood (e.g. saliva, urine) using infrared and Raman spectroscopy in human nutrition applications.</subfield></datafield><datafield tag="650" ind1=" " ind2="4"><subfield code="a">Precision nutrition</subfield></datafield><datafield tag="650" ind1=" " ind2="4"><subfield code="a">Data analytics</subfield></datafield><datafield tag="650" ind1=" " ind2="4"><subfield code="a">Vibrational spectroscopy</subfield></datafield><datafield tag="650" ind1=" " ind2="4"><subfield code="a">Sensors</subfield></datafield><datafield tag="700" ind1="1" ind2=" "><subfield code="a">Cozzolino, Daniel</subfield><subfield code="e">verfasserin</subfield><subfield code="0">(orcid)0000-0001-6247-8817</subfield><subfield code="4">aut</subfield></datafield><datafield tag="773" ind1="0" ind2="8"><subfield code="i">Enthalten in</subfield><subfield code="t">Trends in analytical chemistry</subfield><subfield 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