FT-IR, Vis spectroscopy, color and multivariate analysis for the control of ageing processes in distinctive Spanish wines
During the ageing period, diverse physicochemical changes occur affecting the quality of the final product. For this reason, it is important to study and optimize this step. Fourier transform infrared (FT-IR) and UV–visible (UV–Vis) spectroscopic techniques combined with multivariate analysis were u...
Ausführliche Beschreibung
Gespeichert in:
| Autor*in: |
Ferreiro-González, Marta [verfasserIn] Ruiz-Rodríguez, Ana [verfasserIn] Barbero, Gerardo F. [verfasserIn] Ayuso, Jesús [verfasserIn] Álvarez, José A. [verfasserIn] Palma, Miguel [verfasserIn] Barroso, Carmelo G. [verfasserIn] |
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| Format: |
E-Artikel |
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| Sprache: |
Englisch |
| Erschienen: |
2018 |
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| Schlagwörter: |
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| Übergeordnetes Werk: |
Enthalten in: Food chemistry - New York, NY [u.a.] : Elsevier, 1976, 277, Seite 6-11 |
|---|---|
| Übergeordnetes Werk: |
volume:277 ; pages:6-11 |
| DOI / URN: |
10.1016/j.foodchem.2018.10.087 |
|---|
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| 520 | |a During the ageing period, diverse physicochemical changes occur affecting the quality of the final product. For this reason, it is important to study and optimize this step. Fourier transform infrared (FT-IR) and UV–visible (UV–Vis) spectroscopic techniques combined with multivariate analysis were used to obtain regression models to correlate both spectroscopic data and chromatic parameters with the ageing level of high quality Sherry wines. Three spectral ranges were obtained that contain the highest variance: two different fingerprint ranges in FT-IR (1100–2000 cm−1 and 2300–2999 cm−1) and one range in the visible region (380–450 nm). The regression model has enabled full differentiation between the seven levels of ageing in the wine explored. A good linear regression fit (R2 above 0.95) was obtained regardless of the ranges used. The results demonstrate that both spectroscopic techniques can be used to optimize the ageing process in a simple and fast way. | ||
| 650 | 4 | |a Sherry wines | |
| 650 | 4 | |a Ageing process | |
| 650 | 4 | |a FT-IR spectroscopy | |
| 650 | 4 | |a Vis spectroscopy | |
| 650 | 4 | |a Multivariate analysis | |
| 650 | 4 | |a Partial least squares | |
| 700 | 1 | |a Ruiz-Rodríguez, Ana |e verfasserin |4 aut | |
| 700 | 1 | |a Barbero, Gerardo F. |e verfasserin |4 aut | |
| 700 | 1 | |a Ayuso, Jesús |e verfasserin |4 aut | |
| 700 | 1 | |a Álvarez, José A. |e verfasserin |4 aut | |
| 700 | 1 | |a Palma, Miguel |e verfasserin |0 (orcid)0000-0001-8509-4226 |4 aut | |
| 700 | 1 | |a Barroso, Carmelo G. |e verfasserin |4 aut | |
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10.1016/j.foodchem.2018.10.087 doi (DE-627)ELV00117052X (ELSEVIER)S0308-8146(18)31862-4 DE-627 ger DE-627 rda eng 540 660 DE-600 58.34 bkl Ferreiro-González, Marta verfasserin aut FT-IR, Vis spectroscopy, color and multivariate analysis for the control of ageing processes in distinctive Spanish wines 2018 nicht spezifiziert zzz rdacontent Computermedien c rdamedia Online-Ressource cr rdacarrier During the ageing period, diverse physicochemical changes occur affecting the quality of the final product. For this reason, it is important to study and optimize this step. Fourier transform infrared (FT-IR) and UV–visible (UV–Vis) spectroscopic techniques combined with multivariate analysis were used to obtain regression models to correlate both spectroscopic data and chromatic parameters with the ageing level of high quality Sherry wines. Three spectral ranges were obtained that contain the highest variance: two different fingerprint ranges in FT-IR (1100–2000 cm−1 and 2300–2999 cm−1) and one range in the visible region (380–450 nm). The regression model has enabled full differentiation between the seven levels of ageing in the wine explored. A good linear regression fit (R2 above 0.95) was obtained regardless of the ranges used. The results demonstrate that both spectroscopic techniques can be used to optimize the ageing process in a simple and fast way. Sherry wines Ageing process FT-IR spectroscopy Vis spectroscopy Multivariate analysis Partial least squares Ruiz-Rodríguez, Ana verfasserin aut Barbero, Gerardo F. verfasserin aut Ayuso, Jesús verfasserin aut Álvarez, José A. verfasserin aut Palma, Miguel verfasserin (orcid)0000-0001-8509-4226 aut Barroso, Carmelo G. verfasserin aut Enthalten in Food chemistry New York, NY [u.a.] : Elsevier, 1976 277, Seite 6-11 Online-Ressource (DE-627)300898509 (DE-600)1483647-6 (DE-576)098330225 1873-7072 nnns volume:277 pages:6-11 GBV_USEFLAG_U SYSFLAG_U GBV_ELV 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_63 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_224 GBV_ILN_370 GBV_ILN_602 GBV_ILN_702 GBV_ILN_2003 GBV_ILN_2004 GBV_ILN_2005 GBV_ILN_2011 GBV_ILN_2014 GBV_ILN_2015 GBV_ILN_2020 GBV_ILN_2021 GBV_ILN_2025 GBV_ILN_2027 GBV_ILN_2034 GBV_ILN_2038 GBV_ILN_2044 GBV_ILN_2048 GBV_ILN_2049 GBV_ILN_2050 GBV_ILN_2056 GBV_ILN_2059 GBV_ILN_2061 GBV_ILN_2064 GBV_ILN_2065 GBV_ILN_2068 GBV_ILN_2111 GBV_ILN_2112 GBV_ILN_2113 GBV_ILN_2118 GBV_ILN_2122 GBV_ILN_2129 GBV_ILN_2143 GBV_ILN_2147 GBV_ILN_2148 GBV_ILN_2152 GBV_ILN_2153 GBV_ILN_2190 GBV_ILN_2336 GBV_ILN_2507 GBV_ILN_2522 GBV_ILN_4035 GBV_ILN_4037 GBV_ILN_4112 GBV_ILN_4125 GBV_ILN_4126 GBV_ILN_4242 GBV_ILN_4251 GBV_ILN_4305 GBV_ILN_4313 GBV_ILN_4323 GBV_ILN_4324 GBV_ILN_4326 GBV_ILN_4333 GBV_ILN_4334 GBV_ILN_4335 GBV_ILN_4338 GBV_ILN_4393 58.34 Lebensmitteltechnologie AR 277 6-11 |
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10.1016/j.foodchem.2018.10.087 doi (DE-627)ELV00117052X (ELSEVIER)S0308-8146(18)31862-4 DE-627 ger DE-627 rda eng 540 660 DE-600 58.34 bkl Ferreiro-González, Marta verfasserin aut FT-IR, Vis spectroscopy, color and multivariate analysis for the control of ageing processes in distinctive Spanish wines 2018 nicht spezifiziert zzz rdacontent Computermedien c rdamedia Online-Ressource cr rdacarrier During the ageing period, diverse physicochemical changes occur affecting the quality of the final product. For this reason, it is important to study and optimize this step. Fourier transform infrared (FT-IR) and UV–visible (UV–Vis) spectroscopic techniques combined with multivariate analysis were used to obtain regression models to correlate both spectroscopic data and chromatic parameters with the ageing level of high quality Sherry wines. Three spectral ranges were obtained that contain the highest variance: two different fingerprint ranges in FT-IR (1100–2000 cm−1 and 2300–2999 cm−1) and one range in the visible region (380–450 nm). The regression model has enabled full differentiation between the seven levels of ageing in the wine explored. A good linear regression fit (R2 above 0.95) was obtained regardless of the ranges used. The results demonstrate that both spectroscopic techniques can be used to optimize the ageing process in a simple and fast way. Sherry wines Ageing process FT-IR spectroscopy Vis spectroscopy Multivariate analysis Partial least squares Ruiz-Rodríguez, Ana verfasserin aut Barbero, Gerardo F. verfasserin aut Ayuso, Jesús verfasserin aut Álvarez, José A. verfasserin aut Palma, Miguel verfasserin (orcid)0000-0001-8509-4226 aut Barroso, Carmelo G. verfasserin aut Enthalten in Food chemistry New York, NY [u.a.] : Elsevier, 1976 277, Seite 6-11 Online-Ressource (DE-627)300898509 (DE-600)1483647-6 (DE-576)098330225 1873-7072 nnns volume:277 pages:6-11 GBV_USEFLAG_U SYSFLAG_U GBV_ELV 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_63 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_224 GBV_ILN_370 GBV_ILN_602 GBV_ILN_702 GBV_ILN_2003 GBV_ILN_2004 GBV_ILN_2005 GBV_ILN_2011 GBV_ILN_2014 GBV_ILN_2015 GBV_ILN_2020 GBV_ILN_2021 GBV_ILN_2025 GBV_ILN_2027 GBV_ILN_2034 GBV_ILN_2038 GBV_ILN_2044 GBV_ILN_2048 GBV_ILN_2049 GBV_ILN_2050 GBV_ILN_2056 GBV_ILN_2059 GBV_ILN_2061 GBV_ILN_2064 GBV_ILN_2065 GBV_ILN_2068 GBV_ILN_2111 GBV_ILN_2112 GBV_ILN_2113 GBV_ILN_2118 GBV_ILN_2122 GBV_ILN_2129 GBV_ILN_2143 GBV_ILN_2147 GBV_ILN_2148 GBV_ILN_2152 GBV_ILN_2153 GBV_ILN_2190 GBV_ILN_2336 GBV_ILN_2507 GBV_ILN_2522 GBV_ILN_4035 GBV_ILN_4037 GBV_ILN_4112 GBV_ILN_4125 GBV_ILN_4126 GBV_ILN_4242 GBV_ILN_4251 GBV_ILN_4305 GBV_ILN_4313 GBV_ILN_4323 GBV_ILN_4324 GBV_ILN_4326 GBV_ILN_4333 GBV_ILN_4334 GBV_ILN_4335 GBV_ILN_4338 GBV_ILN_4393 58.34 Lebensmitteltechnologie AR 277 6-11 |
| allfields_unstemmed |
10.1016/j.foodchem.2018.10.087 doi (DE-627)ELV00117052X (ELSEVIER)S0308-8146(18)31862-4 DE-627 ger DE-627 rda eng 540 660 DE-600 58.34 bkl Ferreiro-González, Marta verfasserin aut FT-IR, Vis spectroscopy, color and multivariate analysis for the control of ageing processes in distinctive Spanish wines 2018 nicht spezifiziert zzz rdacontent Computermedien c rdamedia Online-Ressource cr rdacarrier During the ageing period, diverse physicochemical changes occur affecting the quality of the final product. For this reason, it is important to study and optimize this step. Fourier transform infrared (FT-IR) and UV–visible (UV–Vis) spectroscopic techniques combined with multivariate analysis were used to obtain regression models to correlate both spectroscopic data and chromatic parameters with the ageing level of high quality Sherry wines. Three spectral ranges were obtained that contain the highest variance: two different fingerprint ranges in FT-IR (1100–2000 cm−1 and 2300–2999 cm−1) and one range in the visible region (380–450 nm). The regression model has enabled full differentiation between the seven levels of ageing in the wine explored. A good linear regression fit (R2 above 0.95) was obtained regardless of the ranges used. The results demonstrate that both spectroscopic techniques can be used to optimize the ageing process in a simple and fast way. Sherry wines Ageing process FT-IR spectroscopy Vis spectroscopy Multivariate analysis Partial least squares Ruiz-Rodríguez, Ana verfasserin aut Barbero, Gerardo F. verfasserin aut Ayuso, Jesús verfasserin aut Álvarez, José A. verfasserin aut Palma, Miguel verfasserin (orcid)0000-0001-8509-4226 aut Barroso, Carmelo G. verfasserin aut Enthalten in Food chemistry New York, NY [u.a.] : Elsevier, 1976 277, Seite 6-11 Online-Ressource (DE-627)300898509 (DE-600)1483647-6 (DE-576)098330225 1873-7072 nnns volume:277 pages:6-11 GBV_USEFLAG_U SYSFLAG_U GBV_ELV 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_63 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_224 GBV_ILN_370 GBV_ILN_602 GBV_ILN_702 GBV_ILN_2003 GBV_ILN_2004 GBV_ILN_2005 GBV_ILN_2011 GBV_ILN_2014 GBV_ILN_2015 GBV_ILN_2020 GBV_ILN_2021 GBV_ILN_2025 GBV_ILN_2027 GBV_ILN_2034 GBV_ILN_2038 GBV_ILN_2044 GBV_ILN_2048 GBV_ILN_2049 GBV_ILN_2050 GBV_ILN_2056 GBV_ILN_2059 GBV_ILN_2061 GBV_ILN_2064 GBV_ILN_2065 GBV_ILN_2068 GBV_ILN_2111 GBV_ILN_2112 GBV_ILN_2113 GBV_ILN_2118 GBV_ILN_2122 GBV_ILN_2129 GBV_ILN_2143 GBV_ILN_2147 GBV_ILN_2148 GBV_ILN_2152 GBV_ILN_2153 GBV_ILN_2190 GBV_ILN_2336 GBV_ILN_2507 GBV_ILN_2522 GBV_ILN_4035 GBV_ILN_4037 GBV_ILN_4112 GBV_ILN_4125 GBV_ILN_4126 GBV_ILN_4242 GBV_ILN_4251 GBV_ILN_4305 GBV_ILN_4313 GBV_ILN_4323 GBV_ILN_4324 GBV_ILN_4326 GBV_ILN_4333 GBV_ILN_4334 GBV_ILN_4335 GBV_ILN_4338 GBV_ILN_4393 58.34 Lebensmitteltechnologie AR 277 6-11 |
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10.1016/j.foodchem.2018.10.087 doi (DE-627)ELV00117052X (ELSEVIER)S0308-8146(18)31862-4 DE-627 ger DE-627 rda eng 540 660 DE-600 58.34 bkl Ferreiro-González, Marta verfasserin aut FT-IR, Vis spectroscopy, color and multivariate analysis for the control of ageing processes in distinctive Spanish wines 2018 nicht spezifiziert zzz rdacontent Computermedien c rdamedia Online-Ressource cr rdacarrier During the ageing period, diverse physicochemical changes occur affecting the quality of the final product. For this reason, it is important to study and optimize this step. Fourier transform infrared (FT-IR) and UV–visible (UV–Vis) spectroscopic techniques combined with multivariate analysis were used to obtain regression models to correlate both spectroscopic data and chromatic parameters with the ageing level of high quality Sherry wines. Three spectral ranges were obtained that contain the highest variance: two different fingerprint ranges in FT-IR (1100–2000 cm−1 and 2300–2999 cm−1) and one range in the visible region (380–450 nm). The regression model has enabled full differentiation between the seven levels of ageing in the wine explored. A good linear regression fit (R2 above 0.95) was obtained regardless of the ranges used. The results demonstrate that both spectroscopic techniques can be used to optimize the ageing process in a simple and fast way. Sherry wines Ageing process FT-IR spectroscopy Vis spectroscopy Multivariate analysis Partial least squares Ruiz-Rodríguez, Ana verfasserin aut Barbero, Gerardo F. verfasserin aut Ayuso, Jesús verfasserin aut Álvarez, José A. verfasserin aut Palma, Miguel verfasserin (orcid)0000-0001-8509-4226 aut Barroso, Carmelo G. verfasserin aut Enthalten in Food chemistry New York, NY [u.a.] : Elsevier, 1976 277, Seite 6-11 Online-Ressource (DE-627)300898509 (DE-600)1483647-6 (DE-576)098330225 1873-7072 nnns volume:277 pages:6-11 GBV_USEFLAG_U SYSFLAG_U GBV_ELV 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_63 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_224 GBV_ILN_370 GBV_ILN_602 GBV_ILN_702 GBV_ILN_2003 GBV_ILN_2004 GBV_ILN_2005 GBV_ILN_2011 GBV_ILN_2014 GBV_ILN_2015 GBV_ILN_2020 GBV_ILN_2021 GBV_ILN_2025 GBV_ILN_2027 GBV_ILN_2034 GBV_ILN_2038 GBV_ILN_2044 GBV_ILN_2048 GBV_ILN_2049 GBV_ILN_2050 GBV_ILN_2056 GBV_ILN_2059 GBV_ILN_2061 GBV_ILN_2064 GBV_ILN_2065 GBV_ILN_2068 GBV_ILN_2111 GBV_ILN_2112 GBV_ILN_2113 GBV_ILN_2118 GBV_ILN_2122 GBV_ILN_2129 GBV_ILN_2143 GBV_ILN_2147 GBV_ILN_2148 GBV_ILN_2152 GBV_ILN_2153 GBV_ILN_2190 GBV_ILN_2336 GBV_ILN_2507 GBV_ILN_2522 GBV_ILN_4035 GBV_ILN_4037 GBV_ILN_4112 GBV_ILN_4125 GBV_ILN_4126 GBV_ILN_4242 GBV_ILN_4251 GBV_ILN_4305 GBV_ILN_4313 GBV_ILN_4323 GBV_ILN_4324 GBV_ILN_4326 GBV_ILN_4333 GBV_ILN_4334 GBV_ILN_4335 GBV_ILN_4338 GBV_ILN_4393 58.34 Lebensmitteltechnologie AR 277 6-11 |
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10.1016/j.foodchem.2018.10.087 doi (DE-627)ELV00117052X (ELSEVIER)S0308-8146(18)31862-4 DE-627 ger DE-627 rda eng 540 660 DE-600 58.34 bkl Ferreiro-González, Marta verfasserin aut FT-IR, Vis spectroscopy, color and multivariate analysis for the control of ageing processes in distinctive Spanish wines 2018 nicht spezifiziert zzz rdacontent Computermedien c rdamedia Online-Ressource cr rdacarrier During the ageing period, diverse physicochemical changes occur affecting the quality of the final product. For this reason, it is important to study and optimize this step. Fourier transform infrared (FT-IR) and UV–visible (UV–Vis) spectroscopic techniques combined with multivariate analysis were used to obtain regression models to correlate both spectroscopic data and chromatic parameters with the ageing level of high quality Sherry wines. Three spectral ranges were obtained that contain the highest variance: two different fingerprint ranges in FT-IR (1100–2000 cm−1 and 2300–2999 cm−1) and one range in the visible region (380–450 nm). The regression model has enabled full differentiation between the seven levels of ageing in the wine explored. A good linear regression fit (R2 above 0.95) was obtained regardless of the ranges used. The results demonstrate that both spectroscopic techniques can be used to optimize the ageing process in a simple and fast way. Sherry wines Ageing process FT-IR spectroscopy Vis spectroscopy Multivariate analysis Partial least squares Ruiz-Rodríguez, Ana verfasserin aut Barbero, Gerardo F. verfasserin aut Ayuso, Jesús verfasserin aut Álvarez, José A. verfasserin aut Palma, Miguel verfasserin (orcid)0000-0001-8509-4226 aut Barroso, Carmelo G. verfasserin aut Enthalten in Food chemistry New York, NY [u.a.] : Elsevier, 1976 277, Seite 6-11 Online-Ressource (DE-627)300898509 (DE-600)1483647-6 (DE-576)098330225 1873-7072 nnns volume:277 pages:6-11 GBV_USEFLAG_U SYSFLAG_U GBV_ELV 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_63 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_224 GBV_ILN_370 GBV_ILN_602 GBV_ILN_702 GBV_ILN_2003 GBV_ILN_2004 GBV_ILN_2005 GBV_ILN_2011 GBV_ILN_2014 GBV_ILN_2015 GBV_ILN_2020 GBV_ILN_2021 GBV_ILN_2025 GBV_ILN_2027 GBV_ILN_2034 GBV_ILN_2038 GBV_ILN_2044 GBV_ILN_2048 GBV_ILN_2049 GBV_ILN_2050 GBV_ILN_2056 GBV_ILN_2059 GBV_ILN_2061 GBV_ILN_2064 GBV_ILN_2065 GBV_ILN_2068 GBV_ILN_2111 GBV_ILN_2112 GBV_ILN_2113 GBV_ILN_2118 GBV_ILN_2122 GBV_ILN_2129 GBV_ILN_2143 GBV_ILN_2147 GBV_ILN_2148 GBV_ILN_2152 GBV_ILN_2153 GBV_ILN_2190 GBV_ILN_2336 GBV_ILN_2507 GBV_ILN_2522 GBV_ILN_4035 GBV_ILN_4037 GBV_ILN_4112 GBV_ILN_4125 GBV_ILN_4126 GBV_ILN_4242 GBV_ILN_4251 GBV_ILN_4305 GBV_ILN_4313 GBV_ILN_4323 GBV_ILN_4324 GBV_ILN_4326 GBV_ILN_4333 GBV_ILN_4334 GBV_ILN_4335 GBV_ILN_4338 GBV_ILN_4393 58.34 Lebensmitteltechnologie AR 277 6-11 |
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FT-IR, Vis spectroscopy, color and multivariate analysis for the control of ageing processes in distinctive Spanish wines |
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FT-IR, Vis spectroscopy, color and multivariate analysis for the control of ageing processes in distinctive Spanish wines |
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Ferreiro-González, Marta Ruiz-Rodríguez, Ana Barbero, Gerardo F. Ayuso, Jesús Álvarez, José A. Palma, Miguel Barroso, Carmelo G. |
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ft-ir, vis spectroscopy, color and multivariate analysis for the control of ageing processes in distinctive spanish wines |
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FT-IR, Vis spectroscopy, color and multivariate analysis for the control of ageing processes in distinctive Spanish wines |
| abstract |
During the ageing period, diverse physicochemical changes occur affecting the quality of the final product. For this reason, it is important to study and optimize this step. Fourier transform infrared (FT-IR) and UV–visible (UV–Vis) spectroscopic techniques combined with multivariate analysis were used to obtain regression models to correlate both spectroscopic data and chromatic parameters with the ageing level of high quality Sherry wines. Three spectral ranges were obtained that contain the highest variance: two different fingerprint ranges in FT-IR (1100–2000 cm−1 and 2300–2999 cm−1) and one range in the visible region (380–450 nm). The regression model has enabled full differentiation between the seven levels of ageing in the wine explored. A good linear regression fit (R2 above 0.95) was obtained regardless of the ranges used. The results demonstrate that both spectroscopic techniques can be used to optimize the ageing process in a simple and fast way. |
| abstractGer |
During the ageing period, diverse physicochemical changes occur affecting the quality of the final product. For this reason, it is important to study and optimize this step. Fourier transform infrared (FT-IR) and UV–visible (UV–Vis) spectroscopic techniques combined with multivariate analysis were used to obtain regression models to correlate both spectroscopic data and chromatic parameters with the ageing level of high quality Sherry wines. Three spectral ranges were obtained that contain the highest variance: two different fingerprint ranges in FT-IR (1100–2000 cm−1 and 2300–2999 cm−1) and one range in the visible region (380–450 nm). The regression model has enabled full differentiation between the seven levels of ageing in the wine explored. A good linear regression fit (R2 above 0.95) was obtained regardless of the ranges used. The results demonstrate that both spectroscopic techniques can be used to optimize the ageing process in a simple and fast way. |
| abstract_unstemmed |
During the ageing period, diverse physicochemical changes occur affecting the quality of the final product. For this reason, it is important to study and optimize this step. Fourier transform infrared (FT-IR) and UV–visible (UV–Vis) spectroscopic techniques combined with multivariate analysis were used to obtain regression models to correlate both spectroscopic data and chromatic parameters with the ageing level of high quality Sherry wines. Three spectral ranges were obtained that contain the highest variance: two different fingerprint ranges in FT-IR (1100–2000 cm−1 and 2300–2999 cm−1) and one range in the visible region (380–450 nm). The regression model has enabled full differentiation between the seven levels of ageing in the wine explored. A good linear regression fit (R2 above 0.95) was obtained regardless of the ranges used. The results demonstrate that both spectroscopic techniques can be used to optimize the ageing process in a simple and fast way. |
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FT-IR, Vis spectroscopy, color and multivariate analysis for the control of ageing processes in distinctive Spanish wines |
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Ruiz-Rodríguez, Ana Barbero, Gerardo F. Ayuso, Jesús Álvarez, José A. Palma, Miguel Barroso, Carmelo G. |
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