Evidence on the discrimination of quinoa grains with a combination of FT-MIR and FT-NIR spectroscopy
Abstract Quinoa is considered as a valuable re-emergent crop due to its nutritional composition. In this study, five quinoa grains from different geographical origin (Real, CHEN 252, Regalona, BO25 and UDc9) were discriminated using a combination of FT-MIR and FT-NIR spectra as input for principal c...
Ausführliche Beschreibung
Gespeichert in:
| Autor*in: |
Rodríguez, Silvio D. [verfasserIn] |
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| Format: |
E-Artikel |
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| Sprache: |
Englisch |
| Erschienen: |
2019 |
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| Schlagwörter: |
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| Anmerkung: |
© Association of Food Scientists & Technologists (India) 2019 |
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| Übergeordnetes Werk: |
Enthalten in: Journal of food science and technology - Mysore, India : Springer, 2010, 56(2019), 10 vom: 23. Juli, Seite 4457-4464 |
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| Übergeordnetes Werk: |
volume:56 ; year:2019 ; number:10 ; day:23 ; month:07 ; pages:4457-4464 |
| Links: |
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| DOI / URN: |
10.1007/s13197-019-03948-7 |
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| Katalog-ID: |
SPR031156053 |
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| 100 | 1 | |a Rodríguez, Silvio D. |e verfasserin |0 (orcid)0000-0001-5559-8124 |4 aut | |
| 245 | 1 | 0 | |a Evidence on the discrimination of quinoa grains with a combination of FT-MIR and FT-NIR spectroscopy |
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| 520 | |a Abstract Quinoa is considered as a valuable re-emergent crop due to its nutritional composition. In this study, five quinoa grains from different geographical origin (Real, CHEN 252, Regalona, BO25 and UDc9) were discriminated using a combination of FT-MIR and FT-NIR spectra as input for principal component analysis (PCA), cluster analysis (CA) and soft independent modelling class analogy (SIMCA). The results obtained from PCA and CA show a great power of discrimination, with an average silhouette width value of 0.96. Moreover, SIMCA showed an error rate and accuracy values of 0 and 1 respectively with only 4% misclassified samples. A relationship between each principal component and the most important variables for the discrimination were mainly due to vibrations of several oleofins groups (C–H, C–$ H_{2} $, C–$ H_{3} $), alkene group (–CH=CH–), hydroxyl group (O–H) and Amides I and II vibrational modes. | ||
| 650 | 4 | |a Infrared spectroscopy |7 (dpeaa)DE-He213 | |
| 650 | 4 | |a Near infrared spectroscopy |7 (dpeaa)DE-He213 | |
| 650 | 4 | |a Quinoa grains |7 (dpeaa)DE-He213 | |
| 650 | 4 | |a Chemometric methods |7 (dpeaa)DE-He213 | |
| 700 | 1 | |a López-Fernández, M. P. |4 aut | |
| 700 | 1 | |a Maldonado, S. |4 aut | |
| 700 | 1 | |a Buera, M. P. |4 aut | |
| 773 | 0 | 8 | |i Enthalten in |t Journal of food science and technology |d Mysore, India : Springer, 2010 |g 56(2019), 10 vom: 23. Juli, Seite 4457-4464 |w (DE-627)618327800 |w (DE-600)2537738-3 |x 0975-8402 |7 nnns |
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10.1007/s13197-019-03948-7 doi (DE-627)SPR031156053 (SPR)s13197-019-03948-7-e DE-627 ger DE-627 rakwb eng Rodríguez, Silvio D. verfasserin (orcid)0000-0001-5559-8124 aut Evidence on the discrimination of quinoa grains with a combination of FT-MIR and FT-NIR spectroscopy 2019 Text txt rdacontent Computermedien c rdamedia Online-Ressource cr rdacarrier © Association of Food Scientists & Technologists (India) 2019 Abstract Quinoa is considered as a valuable re-emergent crop due to its nutritional composition. In this study, five quinoa grains from different geographical origin (Real, CHEN 252, Regalona, BO25 and UDc9) were discriminated using a combination of FT-MIR and FT-NIR spectra as input for principal component analysis (PCA), cluster analysis (CA) and soft independent modelling class analogy (SIMCA). The results obtained from PCA and CA show a great power of discrimination, with an average silhouette width value of 0.96. Moreover, SIMCA showed an error rate and accuracy values of 0 and 1 respectively with only 4% misclassified samples. A relationship between each principal component and the most important variables for the discrimination were mainly due to vibrations of several oleofins groups (C–H, C–$ H_{2} $, C–$ H_{3} $), alkene group (–CH=CH–), hydroxyl group (O–H) and Amides I and II vibrational modes. Infrared spectroscopy (dpeaa)DE-He213 Near infrared spectroscopy (dpeaa)DE-He213 Quinoa grains (dpeaa)DE-He213 Chemometric methods (dpeaa)DE-He213 López-Fernández, M. P. aut Maldonado, S. aut Buera, M. P. aut Enthalten in Journal of food science and technology Mysore, India : Springer, 2010 56(2019), 10 vom: 23. Juli, Seite 4457-4464 (DE-627)618327800 (DE-600)2537738-3 0975-8402 nnns volume:56 year:2019 number:10 day:23 month:07 pages:4457-4464 https://dx.doi.org/10.1007/s13197-019-03948-7 lizenzpflichtig Volltext GBV_USEFLAG_A SYSFLAG_A GBV_SPRINGER SSG-OLC-PHA 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_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_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_2472 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_4367 GBV_ILN_4393 GBV_ILN_4700 AR 56 2019 10 23 07 4457-4464 |
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10.1007/s13197-019-03948-7 doi (DE-627)SPR031156053 (SPR)s13197-019-03948-7-e DE-627 ger DE-627 rakwb eng Rodríguez, Silvio D. verfasserin (orcid)0000-0001-5559-8124 aut Evidence on the discrimination of quinoa grains with a combination of FT-MIR and FT-NIR spectroscopy 2019 Text txt rdacontent Computermedien c rdamedia Online-Ressource cr rdacarrier © Association of Food Scientists & Technologists (India) 2019 Abstract Quinoa is considered as a valuable re-emergent crop due to its nutritional composition. In this study, five quinoa grains from different geographical origin (Real, CHEN 252, Regalona, BO25 and UDc9) were discriminated using a combination of FT-MIR and FT-NIR spectra as input for principal component analysis (PCA), cluster analysis (CA) and soft independent modelling class analogy (SIMCA). The results obtained from PCA and CA show a great power of discrimination, with an average silhouette width value of 0.96. Moreover, SIMCA showed an error rate and accuracy values of 0 and 1 respectively with only 4% misclassified samples. A relationship between each principal component and the most important variables for the discrimination were mainly due to vibrations of several oleofins groups (C–H, C–$ H_{2} $, C–$ H_{3} $), alkene group (–CH=CH–), hydroxyl group (O–H) and Amides I and II vibrational modes. Infrared spectroscopy (dpeaa)DE-He213 Near infrared spectroscopy (dpeaa)DE-He213 Quinoa grains (dpeaa)DE-He213 Chemometric methods (dpeaa)DE-He213 López-Fernández, M. P. aut Maldonado, S. aut Buera, M. P. aut Enthalten in Journal of food science and technology Mysore, India : Springer, 2010 56(2019), 10 vom: 23. Juli, Seite 4457-4464 (DE-627)618327800 (DE-600)2537738-3 0975-8402 nnns volume:56 year:2019 number:10 day:23 month:07 pages:4457-4464 https://dx.doi.org/10.1007/s13197-019-03948-7 lizenzpflichtig Volltext GBV_USEFLAG_A SYSFLAG_A GBV_SPRINGER SSG-OLC-PHA 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_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_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_2472 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_4367 GBV_ILN_4393 GBV_ILN_4700 AR 56 2019 10 23 07 4457-4464 |
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10.1007/s13197-019-03948-7 doi (DE-627)SPR031156053 (SPR)s13197-019-03948-7-e DE-627 ger DE-627 rakwb eng Rodríguez, Silvio D. verfasserin (orcid)0000-0001-5559-8124 aut Evidence on the discrimination of quinoa grains with a combination of FT-MIR and FT-NIR spectroscopy 2019 Text txt rdacontent Computermedien c rdamedia Online-Ressource cr rdacarrier © Association of Food Scientists & Technologists (India) 2019 Abstract Quinoa is considered as a valuable re-emergent crop due to its nutritional composition. In this study, five quinoa grains from different geographical origin (Real, CHEN 252, Regalona, BO25 and UDc9) were discriminated using a combination of FT-MIR and FT-NIR spectra as input for principal component analysis (PCA), cluster analysis (CA) and soft independent modelling class analogy (SIMCA). The results obtained from PCA and CA show a great power of discrimination, with an average silhouette width value of 0.96. Moreover, SIMCA showed an error rate and accuracy values of 0 and 1 respectively with only 4% misclassified samples. A relationship between each principal component and the most important variables for the discrimination were mainly due to vibrations of several oleofins groups (C–H, C–$ H_{2} $, C–$ H_{3} $), alkene group (–CH=CH–), hydroxyl group (O–H) and Amides I and II vibrational modes. Infrared spectroscopy (dpeaa)DE-He213 Near infrared spectroscopy (dpeaa)DE-He213 Quinoa grains (dpeaa)DE-He213 Chemometric methods (dpeaa)DE-He213 López-Fernández, M. P. aut Maldonado, S. aut Buera, M. P. aut Enthalten in Journal of food science and technology Mysore, India : Springer, 2010 56(2019), 10 vom: 23. Juli, Seite 4457-4464 (DE-627)618327800 (DE-600)2537738-3 0975-8402 nnns volume:56 year:2019 number:10 day:23 month:07 pages:4457-4464 https://dx.doi.org/10.1007/s13197-019-03948-7 lizenzpflichtig Volltext GBV_USEFLAG_A SYSFLAG_A GBV_SPRINGER SSG-OLC-PHA 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_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_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_2472 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_4367 GBV_ILN_4393 GBV_ILN_4700 AR 56 2019 10 23 07 4457-4464 |
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10.1007/s13197-019-03948-7 doi (DE-627)SPR031156053 (SPR)s13197-019-03948-7-e DE-627 ger DE-627 rakwb eng Rodríguez, Silvio D. verfasserin (orcid)0000-0001-5559-8124 aut Evidence on the discrimination of quinoa grains with a combination of FT-MIR and FT-NIR spectroscopy 2019 Text txt rdacontent Computermedien c rdamedia Online-Ressource cr rdacarrier © Association of Food Scientists & Technologists (India) 2019 Abstract Quinoa is considered as a valuable re-emergent crop due to its nutritional composition. In this study, five quinoa grains from different geographical origin (Real, CHEN 252, Regalona, BO25 and UDc9) were discriminated using a combination of FT-MIR and FT-NIR spectra as input for principal component analysis (PCA), cluster analysis (CA) and soft independent modelling class analogy (SIMCA). The results obtained from PCA and CA show a great power of discrimination, with an average silhouette width value of 0.96. Moreover, SIMCA showed an error rate and accuracy values of 0 and 1 respectively with only 4% misclassified samples. A relationship between each principal component and the most important variables for the discrimination were mainly due to vibrations of several oleofins groups (C–H, C–$ H_{2} $, C–$ H_{3} $), alkene group (–CH=CH–), hydroxyl group (O–H) and Amides I and II vibrational modes. Infrared spectroscopy (dpeaa)DE-He213 Near infrared spectroscopy (dpeaa)DE-He213 Quinoa grains (dpeaa)DE-He213 Chemometric methods (dpeaa)DE-He213 López-Fernández, M. P. aut Maldonado, S. aut Buera, M. P. aut Enthalten in Journal of food science and technology Mysore, India : Springer, 2010 56(2019), 10 vom: 23. Juli, Seite 4457-4464 (DE-627)618327800 (DE-600)2537738-3 0975-8402 nnns volume:56 year:2019 number:10 day:23 month:07 pages:4457-4464 https://dx.doi.org/10.1007/s13197-019-03948-7 lizenzpflichtig Volltext GBV_USEFLAG_A SYSFLAG_A GBV_SPRINGER SSG-OLC-PHA 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_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_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_2472 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_4367 GBV_ILN_4393 GBV_ILN_4700 AR 56 2019 10 23 07 4457-4464 |
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10.1007/s13197-019-03948-7 doi (DE-627)SPR031156053 (SPR)s13197-019-03948-7-e DE-627 ger DE-627 rakwb eng Rodríguez, Silvio D. verfasserin (orcid)0000-0001-5559-8124 aut Evidence on the discrimination of quinoa grains with a combination of FT-MIR and FT-NIR spectroscopy 2019 Text txt rdacontent Computermedien c rdamedia Online-Ressource cr rdacarrier © Association of Food Scientists & Technologists (India) 2019 Abstract Quinoa is considered as a valuable re-emergent crop due to its nutritional composition. In this study, five quinoa grains from different geographical origin (Real, CHEN 252, Regalona, BO25 and UDc9) were discriminated using a combination of FT-MIR and FT-NIR spectra as input for principal component analysis (PCA), cluster analysis (CA) and soft independent modelling class analogy (SIMCA). The results obtained from PCA and CA show a great power of discrimination, with an average silhouette width value of 0.96. Moreover, SIMCA showed an error rate and accuracy values of 0 and 1 respectively with only 4% misclassified samples. A relationship between each principal component and the most important variables for the discrimination were mainly due to vibrations of several oleofins groups (C–H, C–$ H_{2} $, C–$ H_{3} $), alkene group (–CH=CH–), hydroxyl group (O–H) and Amides I and II vibrational modes. Infrared spectroscopy (dpeaa)DE-He213 Near infrared spectroscopy (dpeaa)DE-He213 Quinoa grains (dpeaa)DE-He213 Chemometric methods (dpeaa)DE-He213 López-Fernández, M. P. aut Maldonado, S. aut Buera, M. P. aut Enthalten in Journal of food science and technology Mysore, India : Springer, 2010 56(2019), 10 vom: 23. Juli, Seite 4457-4464 (DE-627)618327800 (DE-600)2537738-3 0975-8402 nnns volume:56 year:2019 number:10 day:23 month:07 pages:4457-4464 https://dx.doi.org/10.1007/s13197-019-03948-7 lizenzpflichtig Volltext GBV_USEFLAG_A SYSFLAG_A GBV_SPRINGER SSG-OLC-PHA 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_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_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_2472 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_4367 GBV_ILN_4393 GBV_ILN_4700 AR 56 2019 10 23 07 4457-4464 |
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Rodríguez, Silvio D. @@aut@@ López-Fernández, M. P. @@aut@@ Maldonado, S. @@aut@@ Buera, M. P. @@aut@@ |
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Rodríguez, Silvio D. |
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Rodríguez, Silvio D. misc Infrared spectroscopy misc Near infrared spectroscopy misc Quinoa grains misc Chemometric methods Evidence on the discrimination of quinoa grains with a combination of FT-MIR and FT-NIR spectroscopy |
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Evidence on the discrimination of quinoa grains with a combination of FT-MIR and FT-NIR spectroscopy Infrared spectroscopy (dpeaa)DE-He213 Near infrared spectroscopy (dpeaa)DE-He213 Quinoa grains (dpeaa)DE-He213 Chemometric methods (dpeaa)DE-He213 |
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evidence on the discrimination of quinoa grains with a combination of ft-mir and ft-nir spectroscopy |
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Evidence on the discrimination of quinoa grains with a combination of FT-MIR and FT-NIR spectroscopy |
| abstract |
Abstract Quinoa is considered as a valuable re-emergent crop due to its nutritional composition. In this study, five quinoa grains from different geographical origin (Real, CHEN 252, Regalona, BO25 and UDc9) were discriminated using a combination of FT-MIR and FT-NIR spectra as input for principal component analysis (PCA), cluster analysis (CA) and soft independent modelling class analogy (SIMCA). The results obtained from PCA and CA show a great power of discrimination, with an average silhouette width value of 0.96. Moreover, SIMCA showed an error rate and accuracy values of 0 and 1 respectively with only 4% misclassified samples. A relationship between each principal component and the most important variables for the discrimination were mainly due to vibrations of several oleofins groups (C–H, C–$ H_{2} $, C–$ H_{3} $), alkene group (–CH=CH–), hydroxyl group (O–H) and Amides I and II vibrational modes. © Association of Food Scientists & Technologists (India) 2019 |
| abstractGer |
Abstract Quinoa is considered as a valuable re-emergent crop due to its nutritional composition. In this study, five quinoa grains from different geographical origin (Real, CHEN 252, Regalona, BO25 and UDc9) were discriminated using a combination of FT-MIR and FT-NIR spectra as input for principal component analysis (PCA), cluster analysis (CA) and soft independent modelling class analogy (SIMCA). The results obtained from PCA and CA show a great power of discrimination, with an average silhouette width value of 0.96. Moreover, SIMCA showed an error rate and accuracy values of 0 and 1 respectively with only 4% misclassified samples. A relationship between each principal component and the most important variables for the discrimination were mainly due to vibrations of several oleofins groups (C–H, C–$ H_{2} $, C–$ H_{3} $), alkene group (–CH=CH–), hydroxyl group (O–H) and Amides I and II vibrational modes. © Association of Food Scientists & Technologists (India) 2019 |
| abstract_unstemmed |
Abstract Quinoa is considered as a valuable re-emergent crop due to its nutritional composition. In this study, five quinoa grains from different geographical origin (Real, CHEN 252, Regalona, BO25 and UDc9) were discriminated using a combination of FT-MIR and FT-NIR spectra as input for principal component analysis (PCA), cluster analysis (CA) and soft independent modelling class analogy (SIMCA). The results obtained from PCA and CA show a great power of discrimination, with an average silhouette width value of 0.96. Moreover, SIMCA showed an error rate and accuracy values of 0 and 1 respectively with only 4% misclassified samples. A relationship between each principal component and the most important variables for the discrimination were mainly due to vibrations of several oleofins groups (C–H, C–$ H_{2} $, C–$ H_{3} $), alkene group (–CH=CH–), hydroxyl group (O–H) and Amides I and II vibrational modes. © Association of Food Scientists & Technologists (India) 2019 |
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Evidence on the discrimination of quinoa grains with a combination of FT-MIR and FT-NIR spectroscopy |
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https://dx.doi.org/10.1007/s13197-019-03948-7 |
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López-Fernández, M. P. Maldonado, S. Buera, M. P. |
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López-Fernández, M. P. Maldonado, S. Buera, M. P. |
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10.1007/s13197-019-03948-7 |
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2024-07-03T22:18:31.937Z |
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| score |
7.397867 |