Multicomposition analysis and pattern recognition of Chinese geographical indication product: vinegar
Abstract Multicomposition fingerprints with several chemical compositions containing inorganic elements and organic compounds (amino acids, polyhydric alcohols, organic acids) were measured to distinguish two geographical indication-protected vinegars (GIs) from general vinegars (nGIs). The two of G...
Ausführliche Beschreibung
Autor*in: |
Zheng, Yanjie [verfasserIn] |
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Format: |
E-Artikel |
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Sprache: |
Englisch |
Erschienen: |
2013 |
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Schlagwörter: |
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Anmerkung: |
© Springer-Verlag Berlin Heidelberg 2013 |
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Übergeordnetes Werk: |
Enthalten in: European food research and technology - Berlin : Springer, 1999, 238(2013), 2 vom: 29. Dez., Seite 337-344 |
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Übergeordnetes Werk: |
volume:238 ; year:2013 ; number:2 ; day:29 ; month:12 ; pages:337-344 |
Links: |
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DOI / URN: |
10.1007/s00217-013-2135-2 |
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Katalog-ID: |
SPR002308754 |
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245 | 1 | 0 | |a Multicomposition analysis and pattern recognition of Chinese geographical indication product: vinegar |
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520 | |a Abstract Multicomposition fingerprints with several chemical compositions containing inorganic elements and organic compounds (amino acids, polyhydric alcohols, organic acids) were measured to distinguish two geographical indication-protected vinegars (GIs) from general vinegars (nGIs). The two of GIs were named Shanxi extra aged vinegar and Zhenjiang vinegar from Shanxi and Jiangsu province, respectively. Principal component analysis and Fisher linear discriminant methods were applied for the pattern recognition and classification of GI product. It was not suitable by simply using one kind of composition to make a distinction between GIs and nGIs. However, by using multicomposition to build a classify model, the classification of SXVs was described by Co, As, Al, Mg, Ca, erythritol, arabitol, sorbitol, proline, lysine and pyruvic acid, while ZJVs classification was described by threonine, serine, glycine, lysine, Ba, erythritol, xylitol and lactic acid. The GI samples can be classified with high accuracy according to the discriminant model, of which the false rate is 3.88 % in SXVs model and 10.85 % in ZJVs model. This method can be a useful method for protecting the geographical indication vinegars from the fake or adulterate vinegar commodities. | ||
650 | 4 | |a Multicomposition analysis |7 (dpeaa)DE-He213 | |
650 | 4 | |a Chinese vinegar |7 (dpeaa)DE-He213 | |
650 | 4 | |a Geographical indication |7 (dpeaa)DE-He213 | |
650 | 4 | |a Principal component analysis |7 (dpeaa)DE-He213 | |
650 | 4 | |a Fisher linear discriminant |7 (dpeaa)DE-He213 | |
700 | 1 | |a Ruan, Guihua |4 aut | |
700 | 1 | |a Li, Bifang |4 aut | |
700 | 1 | |a Xiong, Cen |4 aut | |
700 | 1 | |a Chen, Sujuan |4 aut | |
700 | 1 | |a Luo, Meizhong |4 aut | |
700 | 1 | |a Li, Yongle |4 aut | |
700 | 1 | |a Du, Fuyou |4 aut | |
773 | 0 | 8 | |i Enthalten in |t European food research and technology |d Berlin : Springer, 1999 |g 238(2013), 2 vom: 29. Dez., Seite 337-344 |w (DE-627)27012859X |w (DE-600)1476605-X |x 1438-2385 |7 nnns |
773 | 1 | 8 | |g volume:238 |g year:2013 |g number:2 |g day:29 |g month:12 |g pages:337-344 |
856 | 4 | 0 | |u https://dx.doi.org/10.1007/s00217-013-2135-2 |z lizenzpflichtig |3 Volltext |
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10.1007/s00217-013-2135-2 doi (DE-627)SPR002308754 (SPR)s00217-013-2135-2-e DE-627 ger DE-627 rakwb eng Zheng, Yanjie verfasserin aut Multicomposition analysis and pattern recognition of Chinese geographical indication product: vinegar 2013 Text txt rdacontent Computermedien c rdamedia Online-Ressource cr rdacarrier © Springer-Verlag Berlin Heidelberg 2013 Abstract Multicomposition fingerprints with several chemical compositions containing inorganic elements and organic compounds (amino acids, polyhydric alcohols, organic acids) were measured to distinguish two geographical indication-protected vinegars (GIs) from general vinegars (nGIs). The two of GIs were named Shanxi extra aged vinegar and Zhenjiang vinegar from Shanxi and Jiangsu province, respectively. Principal component analysis and Fisher linear discriminant methods were applied for the pattern recognition and classification of GI product. It was not suitable by simply using one kind of composition to make a distinction between GIs and nGIs. However, by using multicomposition to build a classify model, the classification of SXVs was described by Co, As, Al, Mg, Ca, erythritol, arabitol, sorbitol, proline, lysine and pyruvic acid, while ZJVs classification was described by threonine, serine, glycine, lysine, Ba, erythritol, xylitol and lactic acid. The GI samples can be classified with high accuracy according to the discriminant model, of which the false rate is 3.88 % in SXVs model and 10.85 % in ZJVs model. This method can be a useful method for protecting the geographical indication vinegars from the fake or adulterate vinegar commodities. Multicomposition analysis (dpeaa)DE-He213 Chinese vinegar (dpeaa)DE-He213 Geographical indication (dpeaa)DE-He213 Principal component analysis (dpeaa)DE-He213 Fisher linear discriminant (dpeaa)DE-He213 Ruan, Guihua aut Li, Bifang aut Xiong, Cen aut Chen, Sujuan aut Luo, Meizhong aut Li, Yongle aut Du, Fuyou aut Enthalten in European food research and technology Berlin : Springer, 1999 238(2013), 2 vom: 29. Dez., Seite 337-344 (DE-627)27012859X (DE-600)1476605-X 1438-2385 nnns volume:238 year:2013 number:2 day:29 month:12 pages:337-344 https://dx.doi.org/10.1007/s00217-013-2135-2 lizenzpflichtig Volltext GBV_USEFLAG_A SYSFLAG_A GBV_SPRINGER GBV_ILN_11 GBV_ILN_20 GBV_ILN_22 GBV_ILN_23 GBV_ILN_24 GBV_ILN_31 GBV_ILN_32 GBV_ILN_39 GBV_ILN_40 GBV_ILN_60 GBV_ILN_62 GBV_ILN_63 GBV_ILN_69 GBV_ILN_70 GBV_ILN_73 GBV_ILN_74 GBV_ILN_90 GBV_ILN_95 GBV_ILN_100 GBV_ILN_105 GBV_ILN_110 GBV_ILN_120 GBV_ILN_138 GBV_ILN_150 GBV_ILN_151 GBV_ILN_152 GBV_ILN_161 GBV_ILN_170 GBV_ILN_171 GBV_ILN_187 GBV_ILN_213 GBV_ILN_224 GBV_ILN_230 GBV_ILN_250 GBV_ILN_281 GBV_ILN_285 GBV_ILN_293 GBV_ILN_370 GBV_ILN_602 GBV_ILN_636 GBV_ILN_702 GBV_ILN_2001 GBV_ILN_2003 GBV_ILN_2004 GBV_ILN_2005 GBV_ILN_2006 GBV_ILN_2007 GBV_ILN_2008 GBV_ILN_2009 GBV_ILN_2010 GBV_ILN_2011 GBV_ILN_2014 GBV_ILN_2015 GBV_ILN_2020 GBV_ILN_2021 GBV_ILN_2025 GBV_ILN_2026 GBV_ILN_2027 GBV_ILN_2031 GBV_ILN_2034 GBV_ILN_2037 GBV_ILN_2038 GBV_ILN_2039 GBV_ILN_2044 GBV_ILN_2048 GBV_ILN_2049 GBV_ILN_2050 GBV_ILN_2055 GBV_ILN_2057 GBV_ILN_2059 GBV_ILN_2061 GBV_ILN_2064 GBV_ILN_2065 GBV_ILN_2068 GBV_ILN_2070 GBV_ILN_2086 GBV_ILN_2088 GBV_ILN_2093 GBV_ILN_2106 GBV_ILN_2107 GBV_ILN_2108 GBV_ILN_2110 GBV_ILN_2111 GBV_ILN_2112 GBV_ILN_2113 GBV_ILN_2116 GBV_ILN_2118 GBV_ILN_2119 GBV_ILN_2122 GBV_ILN_2129 GBV_ILN_2143 GBV_ILN_2144 GBV_ILN_2147 GBV_ILN_2148 GBV_ILN_2152 GBV_ILN_2153 GBV_ILN_2188 GBV_ILN_2190 GBV_ILN_2232 GBV_ILN_2336 GBV_ILN_2446 GBV_ILN_2470 GBV_ILN_2507 GBV_ILN_2522 GBV_ILN_2548 GBV_ILN_4012 GBV_ILN_4035 GBV_ILN_4037 GBV_ILN_4046 GBV_ILN_4112 GBV_ILN_4125 GBV_ILN_4126 GBV_ILN_4242 GBV_ILN_4246 GBV_ILN_4249 GBV_ILN_4251 GBV_ILN_4305 GBV_ILN_4306 GBV_ILN_4307 GBV_ILN_4313 GBV_ILN_4322 GBV_ILN_4323 GBV_ILN_4324 GBV_ILN_4325 GBV_ILN_4326 GBV_ILN_4333 GBV_ILN_4334 GBV_ILN_4335 GBV_ILN_4336 GBV_ILN_4338 GBV_ILN_4393 GBV_ILN_4700 AR 238 2013 2 29 12 337-344 |
spelling |
10.1007/s00217-013-2135-2 doi (DE-627)SPR002308754 (SPR)s00217-013-2135-2-e DE-627 ger DE-627 rakwb eng Zheng, Yanjie verfasserin aut Multicomposition analysis and pattern recognition of Chinese geographical indication product: vinegar 2013 Text txt rdacontent Computermedien c rdamedia Online-Ressource cr rdacarrier © Springer-Verlag Berlin Heidelberg 2013 Abstract Multicomposition fingerprints with several chemical compositions containing inorganic elements and organic compounds (amino acids, polyhydric alcohols, organic acids) were measured to distinguish two geographical indication-protected vinegars (GIs) from general vinegars (nGIs). The two of GIs were named Shanxi extra aged vinegar and Zhenjiang vinegar from Shanxi and Jiangsu province, respectively. Principal component analysis and Fisher linear discriminant methods were applied for the pattern recognition and classification of GI product. It was not suitable by simply using one kind of composition to make a distinction between GIs and nGIs. However, by using multicomposition to build a classify model, the classification of SXVs was described by Co, As, Al, Mg, Ca, erythritol, arabitol, sorbitol, proline, lysine and pyruvic acid, while ZJVs classification was described by threonine, serine, glycine, lysine, Ba, erythritol, xylitol and lactic acid. The GI samples can be classified with high accuracy according to the discriminant model, of which the false rate is 3.88 % in SXVs model and 10.85 % in ZJVs model. This method can be a useful method for protecting the geographical indication vinegars from the fake or adulterate vinegar commodities. Multicomposition analysis (dpeaa)DE-He213 Chinese vinegar (dpeaa)DE-He213 Geographical indication (dpeaa)DE-He213 Principal component analysis (dpeaa)DE-He213 Fisher linear discriminant (dpeaa)DE-He213 Ruan, Guihua aut Li, Bifang aut Xiong, Cen aut Chen, Sujuan aut Luo, Meizhong aut Li, Yongle aut Du, Fuyou aut Enthalten in European food research and technology Berlin : Springer, 1999 238(2013), 2 vom: 29. Dez., Seite 337-344 (DE-627)27012859X (DE-600)1476605-X 1438-2385 nnns volume:238 year:2013 number:2 day:29 month:12 pages:337-344 https://dx.doi.org/10.1007/s00217-013-2135-2 lizenzpflichtig Volltext GBV_USEFLAG_A SYSFLAG_A GBV_SPRINGER GBV_ILN_11 GBV_ILN_20 GBV_ILN_22 GBV_ILN_23 GBV_ILN_24 GBV_ILN_31 GBV_ILN_32 GBV_ILN_39 GBV_ILN_40 GBV_ILN_60 GBV_ILN_62 GBV_ILN_63 GBV_ILN_69 GBV_ILN_70 GBV_ILN_73 GBV_ILN_74 GBV_ILN_90 GBV_ILN_95 GBV_ILN_100 GBV_ILN_105 GBV_ILN_110 GBV_ILN_120 GBV_ILN_138 GBV_ILN_150 GBV_ILN_151 GBV_ILN_152 GBV_ILN_161 GBV_ILN_170 GBV_ILN_171 GBV_ILN_187 GBV_ILN_213 GBV_ILN_224 GBV_ILN_230 GBV_ILN_250 GBV_ILN_281 GBV_ILN_285 GBV_ILN_293 GBV_ILN_370 GBV_ILN_602 GBV_ILN_636 GBV_ILN_702 GBV_ILN_2001 GBV_ILN_2003 GBV_ILN_2004 GBV_ILN_2005 GBV_ILN_2006 GBV_ILN_2007 GBV_ILN_2008 GBV_ILN_2009 GBV_ILN_2010 GBV_ILN_2011 GBV_ILN_2014 GBV_ILN_2015 GBV_ILN_2020 GBV_ILN_2021 GBV_ILN_2025 GBV_ILN_2026 GBV_ILN_2027 GBV_ILN_2031 GBV_ILN_2034 GBV_ILN_2037 GBV_ILN_2038 GBV_ILN_2039 GBV_ILN_2044 GBV_ILN_2048 GBV_ILN_2049 GBV_ILN_2050 GBV_ILN_2055 GBV_ILN_2057 GBV_ILN_2059 GBV_ILN_2061 GBV_ILN_2064 GBV_ILN_2065 GBV_ILN_2068 GBV_ILN_2070 GBV_ILN_2086 GBV_ILN_2088 GBV_ILN_2093 GBV_ILN_2106 GBV_ILN_2107 GBV_ILN_2108 GBV_ILN_2110 GBV_ILN_2111 GBV_ILN_2112 GBV_ILN_2113 GBV_ILN_2116 GBV_ILN_2118 GBV_ILN_2119 GBV_ILN_2122 GBV_ILN_2129 GBV_ILN_2143 GBV_ILN_2144 GBV_ILN_2147 GBV_ILN_2148 GBV_ILN_2152 GBV_ILN_2153 GBV_ILN_2188 GBV_ILN_2190 GBV_ILN_2232 GBV_ILN_2336 GBV_ILN_2446 GBV_ILN_2470 GBV_ILN_2507 GBV_ILN_2522 GBV_ILN_2548 GBV_ILN_4012 GBV_ILN_4035 GBV_ILN_4037 GBV_ILN_4046 GBV_ILN_4112 GBV_ILN_4125 GBV_ILN_4126 GBV_ILN_4242 GBV_ILN_4246 GBV_ILN_4249 GBV_ILN_4251 GBV_ILN_4305 GBV_ILN_4306 GBV_ILN_4307 GBV_ILN_4313 GBV_ILN_4322 GBV_ILN_4323 GBV_ILN_4324 GBV_ILN_4325 GBV_ILN_4326 GBV_ILN_4333 GBV_ILN_4334 GBV_ILN_4335 GBV_ILN_4336 GBV_ILN_4338 GBV_ILN_4393 GBV_ILN_4700 AR 238 2013 2 29 12 337-344 |
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10.1007/s00217-013-2135-2 doi (DE-627)SPR002308754 (SPR)s00217-013-2135-2-e DE-627 ger DE-627 rakwb eng Zheng, Yanjie verfasserin aut Multicomposition analysis and pattern recognition of Chinese geographical indication product: vinegar 2013 Text txt rdacontent Computermedien c rdamedia Online-Ressource cr rdacarrier © Springer-Verlag Berlin Heidelberg 2013 Abstract Multicomposition fingerprints with several chemical compositions containing inorganic elements and organic compounds (amino acids, polyhydric alcohols, organic acids) were measured to distinguish two geographical indication-protected vinegars (GIs) from general vinegars (nGIs). The two of GIs were named Shanxi extra aged vinegar and Zhenjiang vinegar from Shanxi and Jiangsu province, respectively. Principal component analysis and Fisher linear discriminant methods were applied for the pattern recognition and classification of GI product. It was not suitable by simply using one kind of composition to make a distinction between GIs and nGIs. However, by using multicomposition to build a classify model, the classification of SXVs was described by Co, As, Al, Mg, Ca, erythritol, arabitol, sorbitol, proline, lysine and pyruvic acid, while ZJVs classification was described by threonine, serine, glycine, lysine, Ba, erythritol, xylitol and lactic acid. The GI samples can be classified with high accuracy according to the discriminant model, of which the false rate is 3.88 % in SXVs model and 10.85 % in ZJVs model. This method can be a useful method for protecting the geographical indication vinegars from the fake or adulterate vinegar commodities. Multicomposition analysis (dpeaa)DE-He213 Chinese vinegar (dpeaa)DE-He213 Geographical indication (dpeaa)DE-He213 Principal component analysis (dpeaa)DE-He213 Fisher linear discriminant (dpeaa)DE-He213 Ruan, Guihua aut Li, Bifang aut Xiong, Cen aut Chen, Sujuan aut Luo, Meizhong aut Li, Yongle aut Du, Fuyou aut Enthalten in European food research and technology Berlin : Springer, 1999 238(2013), 2 vom: 29. Dez., Seite 337-344 (DE-627)27012859X (DE-600)1476605-X 1438-2385 nnns volume:238 year:2013 number:2 day:29 month:12 pages:337-344 https://dx.doi.org/10.1007/s00217-013-2135-2 lizenzpflichtig Volltext GBV_USEFLAG_A SYSFLAG_A GBV_SPRINGER GBV_ILN_11 GBV_ILN_20 GBV_ILN_22 GBV_ILN_23 GBV_ILN_24 GBV_ILN_31 GBV_ILN_32 GBV_ILN_39 GBV_ILN_40 GBV_ILN_60 GBV_ILN_62 GBV_ILN_63 GBV_ILN_69 GBV_ILN_70 GBV_ILN_73 GBV_ILN_74 GBV_ILN_90 GBV_ILN_95 GBV_ILN_100 GBV_ILN_105 GBV_ILN_110 GBV_ILN_120 GBV_ILN_138 GBV_ILN_150 GBV_ILN_151 GBV_ILN_152 GBV_ILN_161 GBV_ILN_170 GBV_ILN_171 GBV_ILN_187 GBV_ILN_213 GBV_ILN_224 GBV_ILN_230 GBV_ILN_250 GBV_ILN_281 GBV_ILN_285 GBV_ILN_293 GBV_ILN_370 GBV_ILN_602 GBV_ILN_636 GBV_ILN_702 GBV_ILN_2001 GBV_ILN_2003 GBV_ILN_2004 GBV_ILN_2005 GBV_ILN_2006 GBV_ILN_2007 GBV_ILN_2008 GBV_ILN_2009 GBV_ILN_2010 GBV_ILN_2011 GBV_ILN_2014 GBV_ILN_2015 GBV_ILN_2020 GBV_ILN_2021 GBV_ILN_2025 GBV_ILN_2026 GBV_ILN_2027 GBV_ILN_2031 GBV_ILN_2034 GBV_ILN_2037 GBV_ILN_2038 GBV_ILN_2039 GBV_ILN_2044 GBV_ILN_2048 GBV_ILN_2049 GBV_ILN_2050 GBV_ILN_2055 GBV_ILN_2057 GBV_ILN_2059 GBV_ILN_2061 GBV_ILN_2064 GBV_ILN_2065 GBV_ILN_2068 GBV_ILN_2070 GBV_ILN_2086 GBV_ILN_2088 GBV_ILN_2093 GBV_ILN_2106 GBV_ILN_2107 GBV_ILN_2108 GBV_ILN_2110 GBV_ILN_2111 GBV_ILN_2112 GBV_ILN_2113 GBV_ILN_2116 GBV_ILN_2118 GBV_ILN_2119 GBV_ILN_2122 GBV_ILN_2129 GBV_ILN_2143 GBV_ILN_2144 GBV_ILN_2147 GBV_ILN_2148 GBV_ILN_2152 GBV_ILN_2153 GBV_ILN_2188 GBV_ILN_2190 GBV_ILN_2232 GBV_ILN_2336 GBV_ILN_2446 GBV_ILN_2470 GBV_ILN_2507 GBV_ILN_2522 GBV_ILN_2548 GBV_ILN_4012 GBV_ILN_4035 GBV_ILN_4037 GBV_ILN_4046 GBV_ILN_4112 GBV_ILN_4125 GBV_ILN_4126 GBV_ILN_4242 GBV_ILN_4246 GBV_ILN_4249 GBV_ILN_4251 GBV_ILN_4305 GBV_ILN_4306 GBV_ILN_4307 GBV_ILN_4313 GBV_ILN_4322 GBV_ILN_4323 GBV_ILN_4324 GBV_ILN_4325 GBV_ILN_4326 GBV_ILN_4333 GBV_ILN_4334 GBV_ILN_4335 GBV_ILN_4336 GBV_ILN_4338 GBV_ILN_4393 GBV_ILN_4700 AR 238 2013 2 29 12 337-344 |
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10.1007/s00217-013-2135-2 doi (DE-627)SPR002308754 (SPR)s00217-013-2135-2-e DE-627 ger DE-627 rakwb eng Zheng, Yanjie verfasserin aut Multicomposition analysis and pattern recognition of Chinese geographical indication product: vinegar 2013 Text txt rdacontent Computermedien c rdamedia Online-Ressource cr rdacarrier © Springer-Verlag Berlin Heidelberg 2013 Abstract Multicomposition fingerprints with several chemical compositions containing inorganic elements and organic compounds (amino acids, polyhydric alcohols, organic acids) were measured to distinguish two geographical indication-protected vinegars (GIs) from general vinegars (nGIs). The two of GIs were named Shanxi extra aged vinegar and Zhenjiang vinegar from Shanxi and Jiangsu province, respectively. Principal component analysis and Fisher linear discriminant methods were applied for the pattern recognition and classification of GI product. It was not suitable by simply using one kind of composition to make a distinction between GIs and nGIs. However, by using multicomposition to build a classify model, the classification of SXVs was described by Co, As, Al, Mg, Ca, erythritol, arabitol, sorbitol, proline, lysine and pyruvic acid, while ZJVs classification was described by threonine, serine, glycine, lysine, Ba, erythritol, xylitol and lactic acid. The GI samples can be classified with high accuracy according to the discriminant model, of which the false rate is 3.88 % in SXVs model and 10.85 % in ZJVs model. This method can be a useful method for protecting the geographical indication vinegars from the fake or adulterate vinegar commodities. Multicomposition analysis (dpeaa)DE-He213 Chinese vinegar (dpeaa)DE-He213 Geographical indication (dpeaa)DE-He213 Principal component analysis (dpeaa)DE-He213 Fisher linear discriminant (dpeaa)DE-He213 Ruan, Guihua aut Li, Bifang aut Xiong, Cen aut Chen, Sujuan aut Luo, Meizhong aut Li, Yongle aut Du, Fuyou aut Enthalten in European food research and technology Berlin : Springer, 1999 238(2013), 2 vom: 29. Dez., Seite 337-344 (DE-627)27012859X (DE-600)1476605-X 1438-2385 nnns volume:238 year:2013 number:2 day:29 month:12 pages:337-344 https://dx.doi.org/10.1007/s00217-013-2135-2 lizenzpflichtig Volltext GBV_USEFLAG_A SYSFLAG_A GBV_SPRINGER GBV_ILN_11 GBV_ILN_20 GBV_ILN_22 GBV_ILN_23 GBV_ILN_24 GBV_ILN_31 GBV_ILN_32 GBV_ILN_39 GBV_ILN_40 GBV_ILN_60 GBV_ILN_62 GBV_ILN_63 GBV_ILN_69 GBV_ILN_70 GBV_ILN_73 GBV_ILN_74 GBV_ILN_90 GBV_ILN_95 GBV_ILN_100 GBV_ILN_105 GBV_ILN_110 GBV_ILN_120 GBV_ILN_138 GBV_ILN_150 GBV_ILN_151 GBV_ILN_152 GBV_ILN_161 GBV_ILN_170 GBV_ILN_171 GBV_ILN_187 GBV_ILN_213 GBV_ILN_224 GBV_ILN_230 GBV_ILN_250 GBV_ILN_281 GBV_ILN_285 GBV_ILN_293 GBV_ILN_370 GBV_ILN_602 GBV_ILN_636 GBV_ILN_702 GBV_ILN_2001 GBV_ILN_2003 GBV_ILN_2004 GBV_ILN_2005 GBV_ILN_2006 GBV_ILN_2007 GBV_ILN_2008 GBV_ILN_2009 GBV_ILN_2010 GBV_ILN_2011 GBV_ILN_2014 GBV_ILN_2015 GBV_ILN_2020 GBV_ILN_2021 GBV_ILN_2025 GBV_ILN_2026 GBV_ILN_2027 GBV_ILN_2031 GBV_ILN_2034 GBV_ILN_2037 GBV_ILN_2038 GBV_ILN_2039 GBV_ILN_2044 GBV_ILN_2048 GBV_ILN_2049 GBV_ILN_2050 GBV_ILN_2055 GBV_ILN_2057 GBV_ILN_2059 GBV_ILN_2061 GBV_ILN_2064 GBV_ILN_2065 GBV_ILN_2068 GBV_ILN_2070 GBV_ILN_2086 GBV_ILN_2088 GBV_ILN_2093 GBV_ILN_2106 GBV_ILN_2107 GBV_ILN_2108 GBV_ILN_2110 GBV_ILN_2111 GBV_ILN_2112 GBV_ILN_2113 GBV_ILN_2116 GBV_ILN_2118 GBV_ILN_2119 GBV_ILN_2122 GBV_ILN_2129 GBV_ILN_2143 GBV_ILN_2144 GBV_ILN_2147 GBV_ILN_2148 GBV_ILN_2152 GBV_ILN_2153 GBV_ILN_2188 GBV_ILN_2190 GBV_ILN_2232 GBV_ILN_2336 GBV_ILN_2446 GBV_ILN_2470 GBV_ILN_2507 GBV_ILN_2522 GBV_ILN_2548 GBV_ILN_4012 GBV_ILN_4035 GBV_ILN_4037 GBV_ILN_4046 GBV_ILN_4112 GBV_ILN_4125 GBV_ILN_4126 GBV_ILN_4242 GBV_ILN_4246 GBV_ILN_4249 GBV_ILN_4251 GBV_ILN_4305 GBV_ILN_4306 GBV_ILN_4307 GBV_ILN_4313 GBV_ILN_4322 GBV_ILN_4323 GBV_ILN_4324 GBV_ILN_4325 GBV_ILN_4326 GBV_ILN_4333 GBV_ILN_4334 GBV_ILN_4335 GBV_ILN_4336 GBV_ILN_4338 GBV_ILN_4393 GBV_ILN_4700 AR 238 2013 2 29 12 337-344 |
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10.1007/s00217-013-2135-2 doi (DE-627)SPR002308754 (SPR)s00217-013-2135-2-e DE-627 ger DE-627 rakwb eng Zheng, Yanjie verfasserin aut Multicomposition analysis and pattern recognition of Chinese geographical indication product: vinegar 2013 Text txt rdacontent Computermedien c rdamedia Online-Ressource cr rdacarrier © Springer-Verlag Berlin Heidelberg 2013 Abstract Multicomposition fingerprints with several chemical compositions containing inorganic elements and organic compounds (amino acids, polyhydric alcohols, organic acids) were measured to distinguish two geographical indication-protected vinegars (GIs) from general vinegars (nGIs). The two of GIs were named Shanxi extra aged vinegar and Zhenjiang vinegar from Shanxi and Jiangsu province, respectively. Principal component analysis and Fisher linear discriminant methods were applied for the pattern recognition and classification of GI product. It was not suitable by simply using one kind of composition to make a distinction between GIs and nGIs. However, by using multicomposition to build a classify model, the classification of SXVs was described by Co, As, Al, Mg, Ca, erythritol, arabitol, sorbitol, proline, lysine and pyruvic acid, while ZJVs classification was described by threonine, serine, glycine, lysine, Ba, erythritol, xylitol and lactic acid. The GI samples can be classified with high accuracy according to the discriminant model, of which the false rate is 3.88 % in SXVs model and 10.85 % in ZJVs model. This method can be a useful method for protecting the geographical indication vinegars from the fake or adulterate vinegar commodities. Multicomposition analysis (dpeaa)DE-He213 Chinese vinegar (dpeaa)DE-He213 Geographical indication (dpeaa)DE-He213 Principal component analysis (dpeaa)DE-He213 Fisher linear discriminant (dpeaa)DE-He213 Ruan, Guihua aut Li, Bifang aut Xiong, Cen aut Chen, Sujuan aut Luo, Meizhong aut Li, Yongle aut Du, Fuyou aut Enthalten in European food research and technology Berlin : Springer, 1999 238(2013), 2 vom: 29. Dez., Seite 337-344 (DE-627)27012859X (DE-600)1476605-X 1438-2385 nnns volume:238 year:2013 number:2 day:29 month:12 pages:337-344 https://dx.doi.org/10.1007/s00217-013-2135-2 lizenzpflichtig Volltext GBV_USEFLAG_A SYSFLAG_A GBV_SPRINGER GBV_ILN_11 GBV_ILN_20 GBV_ILN_22 GBV_ILN_23 GBV_ILN_24 GBV_ILN_31 GBV_ILN_32 GBV_ILN_39 GBV_ILN_40 GBV_ILN_60 GBV_ILN_62 GBV_ILN_63 GBV_ILN_69 GBV_ILN_70 GBV_ILN_73 GBV_ILN_74 GBV_ILN_90 GBV_ILN_95 GBV_ILN_100 GBV_ILN_105 GBV_ILN_110 GBV_ILN_120 GBV_ILN_138 GBV_ILN_150 GBV_ILN_151 GBV_ILN_152 GBV_ILN_161 GBV_ILN_170 GBV_ILN_171 GBV_ILN_187 GBV_ILN_213 GBV_ILN_224 GBV_ILN_230 GBV_ILN_250 GBV_ILN_281 GBV_ILN_285 GBV_ILN_293 GBV_ILN_370 GBV_ILN_602 GBV_ILN_636 GBV_ILN_702 GBV_ILN_2001 GBV_ILN_2003 GBV_ILN_2004 GBV_ILN_2005 GBV_ILN_2006 GBV_ILN_2007 GBV_ILN_2008 GBV_ILN_2009 GBV_ILN_2010 GBV_ILN_2011 GBV_ILN_2014 GBV_ILN_2015 GBV_ILN_2020 GBV_ILN_2021 GBV_ILN_2025 GBV_ILN_2026 GBV_ILN_2027 GBV_ILN_2031 GBV_ILN_2034 GBV_ILN_2037 GBV_ILN_2038 GBV_ILN_2039 GBV_ILN_2044 GBV_ILN_2048 GBV_ILN_2049 GBV_ILN_2050 GBV_ILN_2055 GBV_ILN_2057 GBV_ILN_2059 GBV_ILN_2061 GBV_ILN_2064 GBV_ILN_2065 GBV_ILN_2068 GBV_ILN_2070 GBV_ILN_2086 GBV_ILN_2088 GBV_ILN_2093 GBV_ILN_2106 GBV_ILN_2107 GBV_ILN_2108 GBV_ILN_2110 GBV_ILN_2111 GBV_ILN_2112 GBV_ILN_2113 GBV_ILN_2116 GBV_ILN_2118 GBV_ILN_2119 GBV_ILN_2122 GBV_ILN_2129 GBV_ILN_2143 GBV_ILN_2144 GBV_ILN_2147 GBV_ILN_2148 GBV_ILN_2152 GBV_ILN_2153 GBV_ILN_2188 GBV_ILN_2190 GBV_ILN_2232 GBV_ILN_2336 GBV_ILN_2446 GBV_ILN_2470 GBV_ILN_2507 GBV_ILN_2522 GBV_ILN_2548 GBV_ILN_4012 GBV_ILN_4035 GBV_ILN_4037 GBV_ILN_4046 GBV_ILN_4112 GBV_ILN_4125 GBV_ILN_4126 GBV_ILN_4242 GBV_ILN_4246 GBV_ILN_4249 GBV_ILN_4251 GBV_ILN_4305 GBV_ILN_4306 GBV_ILN_4307 GBV_ILN_4313 GBV_ILN_4322 GBV_ILN_4323 GBV_ILN_4324 GBV_ILN_4325 GBV_ILN_4326 GBV_ILN_4333 GBV_ILN_4334 GBV_ILN_4335 GBV_ILN_4336 GBV_ILN_4338 GBV_ILN_4393 GBV_ILN_4700 AR 238 2013 2 29 12 337-344 |
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Enthalten in European food research and technology 238(2013), 2 vom: 29. Dez., Seite 337-344 volume:238 year:2013 number:2 day:29 month:12 pages:337-344 |
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Enthalten in European food research and technology 238(2013), 2 vom: 29. Dez., Seite 337-344 volume:238 year:2013 number:2 day:29 month:12 pages:337-344 |
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Multicomposition analysis Chinese vinegar Geographical indication Principal component analysis Fisher linear discriminant |
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European food research and technology |
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Zheng, Yanjie @@aut@@ Ruan, Guihua @@aut@@ Li, Bifang @@aut@@ Xiong, Cen @@aut@@ Chen, Sujuan @@aut@@ Luo, Meizhong @@aut@@ Li, Yongle @@aut@@ Du, Fuyou @@aut@@ |
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<?xml version="1.0" encoding="UTF-8"?><collection xmlns="http://www.loc.gov/MARC21/slim"><record><leader>01000caa a22002652 4500</leader><controlfield tag="001">SPR002308754</controlfield><controlfield tag="003">DE-627</controlfield><controlfield tag="005">20230327152701.0</controlfield><controlfield tag="007">cr uuu---uuuuu</controlfield><controlfield tag="008">201001s2013 xx |||||o 00| ||eng c</controlfield><datafield tag="024" ind1="7" ind2=" "><subfield code="a">10.1007/s00217-013-2135-2</subfield><subfield code="2">doi</subfield></datafield><datafield tag="035" ind1=" " ind2=" "><subfield code="a">(DE-627)SPR002308754</subfield></datafield><datafield tag="035" ind1=" " ind2=" "><subfield code="a">(SPR)s00217-013-2135-2-e</subfield></datafield><datafield tag="040" ind1=" " ind2=" "><subfield code="a">DE-627</subfield><subfield code="b">ger</subfield><subfield code="c">DE-627</subfield><subfield code="e">rakwb</subfield></datafield><datafield tag="041" ind1=" " ind2=" "><subfield code="a">eng</subfield></datafield><datafield tag="100" ind1="1" ind2=" "><subfield code="a">Zheng, Yanjie</subfield><subfield code="e">verfasserin</subfield><subfield code="4">aut</subfield></datafield><datafield tag="245" ind1="1" ind2="0"><subfield code="a">Multicomposition analysis and pattern recognition of Chinese geographical indication product: vinegar</subfield></datafield><datafield tag="264" ind1=" " ind2="1"><subfield code="c">2013</subfield></datafield><datafield tag="336" ind1=" " ind2=" "><subfield code="a">Text</subfield><subfield code="b">txt</subfield><subfield code="2">rdacontent</subfield></datafield><datafield tag="337" ind1=" " ind2=" "><subfield code="a">Computermedien</subfield><subfield code="b">c</subfield><subfield code="2">rdamedia</subfield></datafield><datafield tag="338" ind1=" " ind2=" "><subfield code="a">Online-Ressource</subfield><subfield code="b">cr</subfield><subfield code="2">rdacarrier</subfield></datafield><datafield tag="500" ind1=" " ind2=" "><subfield code="a">© Springer-Verlag Berlin Heidelberg 2013</subfield></datafield><datafield tag="520" ind1=" " ind2=" "><subfield code="a">Abstract Multicomposition fingerprints with several chemical compositions containing inorganic elements and organic compounds (amino acids, polyhydric alcohols, organic acids) were measured to distinguish two geographical indication-protected vinegars (GIs) from general vinegars (nGIs). The two of GIs were named Shanxi extra aged vinegar and Zhenjiang vinegar from Shanxi and Jiangsu province, respectively. Principal component analysis and Fisher linear discriminant methods were applied for the pattern recognition and classification of GI product. It was not suitable by simply using one kind of composition to make a distinction between GIs and nGIs. However, by using multicomposition to build a classify model, the classification of SXVs was described by Co, As, Al, Mg, Ca, erythritol, arabitol, sorbitol, proline, lysine and pyruvic acid, while ZJVs classification was described by threonine, serine, glycine, lysine, Ba, erythritol, xylitol and lactic acid. The GI samples can be classified with high accuracy according to the discriminant model, of which the false rate is 3.88 % in SXVs model and 10.85 % in ZJVs model. 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Zheng, Yanjie |
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Zheng, Yanjie misc Multicomposition analysis misc Chinese vinegar misc Geographical indication misc Principal component analysis misc Fisher linear discriminant Multicomposition analysis and pattern recognition of Chinese geographical indication product: vinegar |
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Multicomposition analysis and pattern recognition of Chinese geographical indication product: vinegar Multicomposition analysis (dpeaa)DE-He213 Chinese vinegar (dpeaa)DE-He213 Geographical indication (dpeaa)DE-He213 Principal component analysis (dpeaa)DE-He213 Fisher linear discriminant (dpeaa)DE-He213 |
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Multicomposition analysis and pattern recognition of Chinese geographical indication product: vinegar |
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multicomposition analysis and pattern recognition of chinese geographical indication product: vinegar |
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Multicomposition analysis and pattern recognition of Chinese geographical indication product: vinegar |
abstract |
Abstract Multicomposition fingerprints with several chemical compositions containing inorganic elements and organic compounds (amino acids, polyhydric alcohols, organic acids) were measured to distinguish two geographical indication-protected vinegars (GIs) from general vinegars (nGIs). The two of GIs were named Shanxi extra aged vinegar and Zhenjiang vinegar from Shanxi and Jiangsu province, respectively. Principal component analysis and Fisher linear discriminant methods were applied for the pattern recognition and classification of GI product. It was not suitable by simply using one kind of composition to make a distinction between GIs and nGIs. However, by using multicomposition to build a classify model, the classification of SXVs was described by Co, As, Al, Mg, Ca, erythritol, arabitol, sorbitol, proline, lysine and pyruvic acid, while ZJVs classification was described by threonine, serine, glycine, lysine, Ba, erythritol, xylitol and lactic acid. The GI samples can be classified with high accuracy according to the discriminant model, of which the false rate is 3.88 % in SXVs model and 10.85 % in ZJVs model. This method can be a useful method for protecting the geographical indication vinegars from the fake or adulterate vinegar commodities. © Springer-Verlag Berlin Heidelberg 2013 |
abstractGer |
Abstract Multicomposition fingerprints with several chemical compositions containing inorganic elements and organic compounds (amino acids, polyhydric alcohols, organic acids) were measured to distinguish two geographical indication-protected vinegars (GIs) from general vinegars (nGIs). The two of GIs were named Shanxi extra aged vinegar and Zhenjiang vinegar from Shanxi and Jiangsu province, respectively. Principal component analysis and Fisher linear discriminant methods were applied for the pattern recognition and classification of GI product. It was not suitable by simply using one kind of composition to make a distinction between GIs and nGIs. However, by using multicomposition to build a classify model, the classification of SXVs was described by Co, As, Al, Mg, Ca, erythritol, arabitol, sorbitol, proline, lysine and pyruvic acid, while ZJVs classification was described by threonine, serine, glycine, lysine, Ba, erythritol, xylitol and lactic acid. The GI samples can be classified with high accuracy according to the discriminant model, of which the false rate is 3.88 % in SXVs model and 10.85 % in ZJVs model. This method can be a useful method for protecting the geographical indication vinegars from the fake or adulterate vinegar commodities. © Springer-Verlag Berlin Heidelberg 2013 |
abstract_unstemmed |
Abstract Multicomposition fingerprints with several chemical compositions containing inorganic elements and organic compounds (amino acids, polyhydric alcohols, organic acids) were measured to distinguish two geographical indication-protected vinegars (GIs) from general vinegars (nGIs). The two of GIs were named Shanxi extra aged vinegar and Zhenjiang vinegar from Shanxi and Jiangsu province, respectively. Principal component analysis and Fisher linear discriminant methods were applied for the pattern recognition and classification of GI product. It was not suitable by simply using one kind of composition to make a distinction between GIs and nGIs. However, by using multicomposition to build a classify model, the classification of SXVs was described by Co, As, Al, Mg, Ca, erythritol, arabitol, sorbitol, proline, lysine and pyruvic acid, while ZJVs classification was described by threonine, serine, glycine, lysine, Ba, erythritol, xylitol and lactic acid. The GI samples can be classified with high accuracy according to the discriminant model, of which the false rate is 3.88 % in SXVs model and 10.85 % in ZJVs model. This method can be a useful method for protecting the geographical indication vinegars from the fake or adulterate vinegar commodities. © Springer-Verlag Berlin Heidelberg 2013 |
collection_details |
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container_issue |
2 |
title_short |
Multicomposition analysis and pattern recognition of Chinese geographical indication product: vinegar |
url |
https://dx.doi.org/10.1007/s00217-013-2135-2 |
remote_bool |
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author2 |
Ruan, Guihua Li, Bifang Xiong, Cen Chen, Sujuan Luo, Meizhong Li, Yongle Du, Fuyou |
author2Str |
Ruan, Guihua Li, Bifang Xiong, Cen Chen, Sujuan Luo, Meizhong Li, Yongle Du, Fuyou |
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doi_str |
10.1007/s00217-013-2135-2 |
up_date |
2024-07-04T02:34:44.019Z |
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|
score |
7.399583 |