Near infrared spectroscopy for the pre-cure freezing discrimination of Montanera Iberian dry-cured lomito
Abstract The time lapse between the moment Montanera dry-cured products become available in the market and the consumers purchase could be overcome by freezing the raw meat prior to its curing technological process, i.e. the pre-cure freezing. This study is an attempt to assess the suitability of us...
Ausführliche Beschreibung
Gespeichert in:
| Autor*in: |
León, Lucía [verfasserIn] |
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| Format: |
E-Artikel |
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| Sprache: |
Englisch |
| Erschienen: |
2022 |
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| Schlagwörter: |
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| Anmerkung: |
© Association of Food Scientists & Technologists (India) 2022 |
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| Übergeordnetes Werk: |
Enthalten in: Journal of food science and technology - Mysore, India : Springer, 2010, 59(2022), 11 vom: 23. Juni, Seite 4499-4509 |
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| Übergeordnetes Werk: |
volume:59 ; year:2022 ; number:11 ; day:23 ; month:06 ; pages:4499-4509 |
| Links: |
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| DOI / URN: |
10.1007/s13197-022-05530-0 |
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| Katalog-ID: |
SPR048262633 |
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| 245 | 1 | 0 | |a Near infrared spectroscopy for the pre-cure freezing discrimination of Montanera Iberian dry-cured lomito |
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| 520 | |a Abstract The time lapse between the moment Montanera dry-cured products become available in the market and the consumers purchase could be overcome by freezing the raw meat prior to its curing technological process, i.e. the pre-cure freezing. This study is an attempt to assess the suitability of using Near Infrared Spectroscopy (NIRS) for the pre-cure freezing discrimination in Montanera Iberian dry-cured lomitos (the commercial name given to the Iberian dry-cured presa, Serratus ventralis muscle). The best fitting models developed through Partial Least Square-Discriminant Analysis (PLS-DA) offered a highly-discriminatory capacity, with sensitivity and specificity over 85%. The classification performance decreased in Soft Independent Modelling of Class Analogies (SIMCA) models due to the decrease of the specificity. These findings suggest that NIRS technology in combination with PLS-DA, may be useful for the control of the pre-cure freezing practice. | ||
| 650 | 4 | |a seasonality |7 (dpeaa)DE-He213 | |
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| 650 | 4 | |a Pre-frozen dry-cured |7 (dpeaa)DE-He213 | |
| 650 | 4 | |a Dry-cured product authentication |7 (dpeaa)DE-He213 | |
| 700 | 1 | |a Ortiz, Alberto |0 (orcid)0000-0003-2432-2373 |4 aut | |
| 700 | 1 | |a Tejerina, David |4 aut | |
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10.1007/s13197-022-05530-0 doi (DE-627)SPR048262633 (SPR)s13197-022-05530-0-e DE-627 ger DE-627 rakwb eng León, Lucía verfasserin aut Near infrared spectroscopy for the pre-cure freezing discrimination of Montanera Iberian dry-cured lomito 2022 Text txt rdacontent Computermedien c rdamedia Online-Ressource cr rdacarrier © Association of Food Scientists & Technologists (India) 2022 Abstract The time lapse between the moment Montanera dry-cured products become available in the market and the consumers purchase could be overcome by freezing the raw meat prior to its curing technological process, i.e. the pre-cure freezing. This study is an attempt to assess the suitability of using Near Infrared Spectroscopy (NIRS) for the pre-cure freezing discrimination in Montanera Iberian dry-cured lomitos (the commercial name given to the Iberian dry-cured presa, Serratus ventralis muscle). The best fitting models developed through Partial Least Square-Discriminant Analysis (PLS-DA) offered a highly-discriminatory capacity, with sensitivity and specificity over 85%. The classification performance decreased in Soft Independent Modelling of Class Analogies (SIMCA) models due to the decrease of the specificity. These findings suggest that NIRS technology in combination with PLS-DA, may be useful for the control of the pre-cure freezing practice. seasonality (dpeaa)DE-He213 Iberian (dpeaa)DE-He213 Pre-frozen dry-cured (dpeaa)DE-He213 Dry-cured product authentication (dpeaa)DE-He213 Ortiz, Alberto (orcid)0000-0003-2432-2373 aut Tejerina, David aut Enthalten in Journal of food science and technology Mysore, India : Springer, 2010 59(2022), 11 vom: 23. Juni, Seite 4499-4509 (DE-627)618327800 (DE-600)2537738-3 0975-8402 nnns volume:59 year:2022 number:11 day:23 month:06 pages:4499-4509 https://dx.doi.org/10.1007/s13197-022-05530-0 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_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_2056 GBV_ILN_2057 GBV_ILN_2059 GBV_ILN_2061 GBV_ILN_2064 GBV_ILN_2065 GBV_ILN_2068 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_2118 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_4328 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 59 2022 11 23 06 4499-4509 |
| spelling |
10.1007/s13197-022-05530-0 doi (DE-627)SPR048262633 (SPR)s13197-022-05530-0-e DE-627 ger DE-627 rakwb eng León, Lucía verfasserin aut Near infrared spectroscopy for the pre-cure freezing discrimination of Montanera Iberian dry-cured lomito 2022 Text txt rdacontent Computermedien c rdamedia Online-Ressource cr rdacarrier © Association of Food Scientists & Technologists (India) 2022 Abstract The time lapse between the moment Montanera dry-cured products become available in the market and the consumers purchase could be overcome by freezing the raw meat prior to its curing technological process, i.e. the pre-cure freezing. This study is an attempt to assess the suitability of using Near Infrared Spectroscopy (NIRS) for the pre-cure freezing discrimination in Montanera Iberian dry-cured lomitos (the commercial name given to the Iberian dry-cured presa, Serratus ventralis muscle). The best fitting models developed through Partial Least Square-Discriminant Analysis (PLS-DA) offered a highly-discriminatory capacity, with sensitivity and specificity over 85%. The classification performance decreased in Soft Independent Modelling of Class Analogies (SIMCA) models due to the decrease of the specificity. These findings suggest that NIRS technology in combination with PLS-DA, may be useful for the control of the pre-cure freezing practice. seasonality (dpeaa)DE-He213 Iberian (dpeaa)DE-He213 Pre-frozen dry-cured (dpeaa)DE-He213 Dry-cured product authentication (dpeaa)DE-He213 Ortiz, Alberto (orcid)0000-0003-2432-2373 aut Tejerina, David aut Enthalten in Journal of food science and technology Mysore, India : Springer, 2010 59(2022), 11 vom: 23. Juni, Seite 4499-4509 (DE-627)618327800 (DE-600)2537738-3 0975-8402 nnns volume:59 year:2022 number:11 day:23 month:06 pages:4499-4509 https://dx.doi.org/10.1007/s13197-022-05530-0 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_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_2056 GBV_ILN_2057 GBV_ILN_2059 GBV_ILN_2061 GBV_ILN_2064 GBV_ILN_2065 GBV_ILN_2068 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_2118 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_4328 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 59 2022 11 23 06 4499-4509 |
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10.1007/s13197-022-05530-0 doi (DE-627)SPR048262633 (SPR)s13197-022-05530-0-e DE-627 ger DE-627 rakwb eng León, Lucía verfasserin aut Near infrared spectroscopy for the pre-cure freezing discrimination of Montanera Iberian dry-cured lomito 2022 Text txt rdacontent Computermedien c rdamedia Online-Ressource cr rdacarrier © Association of Food Scientists & Technologists (India) 2022 Abstract The time lapse between the moment Montanera dry-cured products become available in the market and the consumers purchase could be overcome by freezing the raw meat prior to its curing technological process, i.e. the pre-cure freezing. This study is an attempt to assess the suitability of using Near Infrared Spectroscopy (NIRS) for the pre-cure freezing discrimination in Montanera Iberian dry-cured lomitos (the commercial name given to the Iberian dry-cured presa, Serratus ventralis muscle). The best fitting models developed through Partial Least Square-Discriminant Analysis (PLS-DA) offered a highly-discriminatory capacity, with sensitivity and specificity over 85%. The classification performance decreased in Soft Independent Modelling of Class Analogies (SIMCA) models due to the decrease of the specificity. These findings suggest that NIRS technology in combination with PLS-DA, may be useful for the control of the pre-cure freezing practice. seasonality (dpeaa)DE-He213 Iberian (dpeaa)DE-He213 Pre-frozen dry-cured (dpeaa)DE-He213 Dry-cured product authentication (dpeaa)DE-He213 Ortiz, Alberto (orcid)0000-0003-2432-2373 aut Tejerina, David aut Enthalten in Journal of food science and technology Mysore, India : Springer, 2010 59(2022), 11 vom: 23. Juni, Seite 4499-4509 (DE-627)618327800 (DE-600)2537738-3 0975-8402 nnns volume:59 year:2022 number:11 day:23 month:06 pages:4499-4509 https://dx.doi.org/10.1007/s13197-022-05530-0 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_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_2056 GBV_ILN_2057 GBV_ILN_2059 GBV_ILN_2061 GBV_ILN_2064 GBV_ILN_2065 GBV_ILN_2068 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_2118 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_4328 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 59 2022 11 23 06 4499-4509 |
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10.1007/s13197-022-05530-0 doi (DE-627)SPR048262633 (SPR)s13197-022-05530-0-e DE-627 ger DE-627 rakwb eng León, Lucía verfasserin aut Near infrared spectroscopy for the pre-cure freezing discrimination of Montanera Iberian dry-cured lomito 2022 Text txt rdacontent Computermedien c rdamedia Online-Ressource cr rdacarrier © Association of Food Scientists & Technologists (India) 2022 Abstract The time lapse between the moment Montanera dry-cured products become available in the market and the consumers purchase could be overcome by freezing the raw meat prior to its curing technological process, i.e. the pre-cure freezing. This study is an attempt to assess the suitability of using Near Infrared Spectroscopy (NIRS) for the pre-cure freezing discrimination in Montanera Iberian dry-cured lomitos (the commercial name given to the Iberian dry-cured presa, Serratus ventralis muscle). The best fitting models developed through Partial Least Square-Discriminant Analysis (PLS-DA) offered a highly-discriminatory capacity, with sensitivity and specificity over 85%. The classification performance decreased in Soft Independent Modelling of Class Analogies (SIMCA) models due to the decrease of the specificity. These findings suggest that NIRS technology in combination with PLS-DA, may be useful for the control of the pre-cure freezing practice. seasonality (dpeaa)DE-He213 Iberian (dpeaa)DE-He213 Pre-frozen dry-cured (dpeaa)DE-He213 Dry-cured product authentication (dpeaa)DE-He213 Ortiz, Alberto (orcid)0000-0003-2432-2373 aut Tejerina, David aut Enthalten in Journal of food science and technology Mysore, India : Springer, 2010 59(2022), 11 vom: 23. Juni, Seite 4499-4509 (DE-627)618327800 (DE-600)2537738-3 0975-8402 nnns volume:59 year:2022 number:11 day:23 month:06 pages:4499-4509 https://dx.doi.org/10.1007/s13197-022-05530-0 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_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_2056 GBV_ILN_2057 GBV_ILN_2059 GBV_ILN_2061 GBV_ILN_2064 GBV_ILN_2065 GBV_ILN_2068 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_2118 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_4328 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 59 2022 11 23 06 4499-4509 |
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10.1007/s13197-022-05530-0 doi (DE-627)SPR048262633 (SPR)s13197-022-05530-0-e DE-627 ger DE-627 rakwb eng León, Lucía verfasserin aut Near infrared spectroscopy for the pre-cure freezing discrimination of Montanera Iberian dry-cured lomito 2022 Text txt rdacontent Computermedien c rdamedia Online-Ressource cr rdacarrier © Association of Food Scientists & Technologists (India) 2022 Abstract The time lapse between the moment Montanera dry-cured products become available in the market and the consumers purchase could be overcome by freezing the raw meat prior to its curing technological process, i.e. the pre-cure freezing. This study is an attempt to assess the suitability of using Near Infrared Spectroscopy (NIRS) for the pre-cure freezing discrimination in Montanera Iberian dry-cured lomitos (the commercial name given to the Iberian dry-cured presa, Serratus ventralis muscle). The best fitting models developed through Partial Least Square-Discriminant Analysis (PLS-DA) offered a highly-discriminatory capacity, with sensitivity and specificity over 85%. The classification performance decreased in Soft Independent Modelling of Class Analogies (SIMCA) models due to the decrease of the specificity. These findings suggest that NIRS technology in combination with PLS-DA, may be useful for the control of the pre-cure freezing practice. seasonality (dpeaa)DE-He213 Iberian (dpeaa)DE-He213 Pre-frozen dry-cured (dpeaa)DE-He213 Dry-cured product authentication (dpeaa)DE-He213 Ortiz, Alberto (orcid)0000-0003-2432-2373 aut Tejerina, David aut Enthalten in Journal of food science and technology Mysore, India : Springer, 2010 59(2022), 11 vom: 23. Juni, Seite 4499-4509 (DE-627)618327800 (DE-600)2537738-3 0975-8402 nnns volume:59 year:2022 number:11 day:23 month:06 pages:4499-4509 https://dx.doi.org/10.1007/s13197-022-05530-0 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_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_2056 GBV_ILN_2057 GBV_ILN_2059 GBV_ILN_2061 GBV_ILN_2064 GBV_ILN_2065 GBV_ILN_2068 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_2118 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_4328 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 59 2022 11 23 06 4499-4509 |
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León, Lucía |
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León, Lucía misc seasonality misc Iberian misc Pre-frozen dry-cured misc Dry-cured product authentication Near infrared spectroscopy for the pre-cure freezing discrimination of Montanera Iberian dry-cured lomito |
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Near infrared spectroscopy for the pre-cure freezing discrimination of Montanera Iberian dry-cured lomito seasonality (dpeaa)DE-He213 Iberian (dpeaa)DE-He213 Pre-frozen dry-cured (dpeaa)DE-He213 Dry-cured product authentication (dpeaa)DE-He213 |
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misc seasonality misc Iberian misc Pre-frozen dry-cured misc Dry-cured product authentication |
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Near infrared spectroscopy for the pre-cure freezing discrimination of Montanera Iberian dry-cured lomito |
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Near infrared spectroscopy for the pre-cure freezing discrimination of Montanera Iberian dry-cured lomito |
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near infrared spectroscopy for the pre-cure freezing discrimination of montanera iberian dry-cured lomito |
| title_auth |
Near infrared spectroscopy for the pre-cure freezing discrimination of Montanera Iberian dry-cured lomito |
| abstract |
Abstract The time lapse between the moment Montanera dry-cured products become available in the market and the consumers purchase could be overcome by freezing the raw meat prior to its curing technological process, i.e. the pre-cure freezing. This study is an attempt to assess the suitability of using Near Infrared Spectroscopy (NIRS) for the pre-cure freezing discrimination in Montanera Iberian dry-cured lomitos (the commercial name given to the Iberian dry-cured presa, Serratus ventralis muscle). The best fitting models developed through Partial Least Square-Discriminant Analysis (PLS-DA) offered a highly-discriminatory capacity, with sensitivity and specificity over 85%. The classification performance decreased in Soft Independent Modelling of Class Analogies (SIMCA) models due to the decrease of the specificity. These findings suggest that NIRS technology in combination with PLS-DA, may be useful for the control of the pre-cure freezing practice. © Association of Food Scientists & Technologists (India) 2022 |
| abstractGer |
Abstract The time lapse between the moment Montanera dry-cured products become available in the market and the consumers purchase could be overcome by freezing the raw meat prior to its curing technological process, i.e. the pre-cure freezing. This study is an attempt to assess the suitability of using Near Infrared Spectroscopy (NIRS) for the pre-cure freezing discrimination in Montanera Iberian dry-cured lomitos (the commercial name given to the Iberian dry-cured presa, Serratus ventralis muscle). The best fitting models developed through Partial Least Square-Discriminant Analysis (PLS-DA) offered a highly-discriminatory capacity, with sensitivity and specificity over 85%. The classification performance decreased in Soft Independent Modelling of Class Analogies (SIMCA) models due to the decrease of the specificity. These findings suggest that NIRS technology in combination with PLS-DA, may be useful for the control of the pre-cure freezing practice. © Association of Food Scientists & Technologists (India) 2022 |
| abstract_unstemmed |
Abstract The time lapse between the moment Montanera dry-cured products become available in the market and the consumers purchase could be overcome by freezing the raw meat prior to its curing technological process, i.e. the pre-cure freezing. This study is an attempt to assess the suitability of using Near Infrared Spectroscopy (NIRS) for the pre-cure freezing discrimination in Montanera Iberian dry-cured lomitos (the commercial name given to the Iberian dry-cured presa, Serratus ventralis muscle). The best fitting models developed through Partial Least Square-Discriminant Analysis (PLS-DA) offered a highly-discriminatory capacity, with sensitivity and specificity over 85%. The classification performance decreased in Soft Independent Modelling of Class Analogies (SIMCA) models due to the decrease of the specificity. These findings suggest that NIRS technology in combination with PLS-DA, may be useful for the control of the pre-cure freezing practice. © Association of Food Scientists & Technologists (India) 2022 |
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11 |
| title_short |
Near infrared spectroscopy for the pre-cure freezing discrimination of Montanera Iberian dry-cured lomito |
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https://dx.doi.org/10.1007/s13197-022-05530-0 |
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Ortiz, Alberto Tejerina, David |
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| doi_str |
10.1007/s13197-022-05530-0 |
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2024-07-03T18:05:15.714Z |
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|
| score |
7.401613 |