Nondestructive Measurements of Freezing Parameters of Frozen Porcine Meat by NIR Hyperspectral Imaging
Abstract The freezing medium temperature and the freezing rate are two important parameters that affect the quality of frozen product. The traditional measurement of freezing parameters will destroy the integrity of the sample and can only be implemented during the freezing process. This study aimed...
Ausführliche Beschreibung
Gespeichert in:
| Autor*in: |
Xie, Anguo [verfasserIn] Sun, Da-Wen [verfasserIn] Zhu, Zhiwei [verfasserIn] Pu, Hongbin [verfasserIn] |
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| Format: |
E-Artikel |
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| Sprache: |
Englisch |
| Erschienen: |
2016 |
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| Schlagwörter: |
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| Übergeordnetes Werk: |
Enthalten in: Food and bioprocess technology - New York, NY : Springer Science + Business Media, LLC, 2008, 9(2016), 9 vom: 14. Juli, Seite 1444-1454 |
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| Übergeordnetes Werk: |
volume:9 ; year:2016 ; number:9 ; day:14 ; month:07 ; pages:1444-1454 |
| Links: |
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| DOI / URN: |
10.1007/s11947-016-1766-2 |
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| Katalog-ID: |
SPR023097531 |
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| 520 | |a Abstract The freezing medium temperature and the freezing rate are two important parameters that affect the quality of frozen product. The traditional measurement of freezing parameters will destroy the integrity of the sample and can only be implemented during the freezing process. This study aimed to develop nondestructive hyperspectral imaging (HSI) methods to rapidly detect freezing parameters. The spectral features of the porcine meat samples in frozen state were studied, in which 90 pieces of porcine samples were frozen by different methods with different freezing medium (air and liquid) at different temperatures (from −20 to −120 °C) and freezing rates (from 0.307 to 5.1 cm/h). The result showed that the freezing process would strongly influence spectra of the frozen sample. The reflectance increased with the decrease in freezing medium temperatures, and the negative correlation reached a highly significant level. The freezing parameters did not change the position of the spectral peaks but altered the spectral intensity. Most changes were near 1070, 1172, 1420, 1586, and 1890 nm. The partial least-squares regression spectral models exhibited good performance for predicting freezing medium temperatures %$ \left({R}_c^2=0.898,{R}_p^2=0.844\right) %$ and freezing rates %$ \left({R}_c^2=0.879,{R}_p^2=0.829\right) %$. The study confirmed that could be used for measuring freezing parameters of frozen product. This novel method will not damage the sample integrity, and measurement can be implemented anytime rather than only during the freezing process by traditional methods. | ||
| 650 | 4 | |a Hyperspectral imaging |7 (dpeaa)DE-He213 | |
| 650 | 4 | |a NIR spectroscopy |7 (dpeaa)DE-He213 | |
| 650 | 4 | |a Freezing rate |7 (dpeaa)DE-He213 | |
| 650 | 4 | |a Freezing medium temperature |7 (dpeaa)DE-He213 | |
| 650 | 4 | |a Freezing methods |7 (dpeaa)DE-He213 | |
| 700 | 1 | |a Sun, Da-Wen |e verfasserin |4 aut | |
| 700 | 1 | |a Zhu, Zhiwei |e verfasserin |4 aut | |
| 700 | 1 | |a Pu, Hongbin |e verfasserin |4 aut | |
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10.1007/s11947-016-1766-2 doi (DE-627)SPR023097531 (SPR)s11947-016-1766-2-e DE-627 ger DE-627 rakwb eng 660 ASE 58.34 bkl Xie, Anguo verfasserin aut Nondestructive Measurements of Freezing Parameters of Frozen Porcine Meat by NIR Hyperspectral Imaging 2016 Text txt rdacontent Computermedien c rdamedia Online-Ressource cr rdacarrier Abstract The freezing medium temperature and the freezing rate are two important parameters that affect the quality of frozen product. The traditional measurement of freezing parameters will destroy the integrity of the sample and can only be implemented during the freezing process. This study aimed to develop nondestructive hyperspectral imaging (HSI) methods to rapidly detect freezing parameters. The spectral features of the porcine meat samples in frozen state were studied, in which 90 pieces of porcine samples were frozen by different methods with different freezing medium (air and liquid) at different temperatures (from −20 to −120 °C) and freezing rates (from 0.307 to 5.1 cm/h). The result showed that the freezing process would strongly influence spectra of the frozen sample. The reflectance increased with the decrease in freezing medium temperatures, and the negative correlation reached a highly significant level. The freezing parameters did not change the position of the spectral peaks but altered the spectral intensity. Most changes were near 1070, 1172, 1420, 1586, and 1890 nm. The partial least-squares regression spectral models exhibited good performance for predicting freezing medium temperatures %$ \left({R}_c^2=0.898,{R}_p^2=0.844\right) %$ and freezing rates %$ \left({R}_c^2=0.879,{R}_p^2=0.829\right) %$. The study confirmed that could be used for measuring freezing parameters of frozen product. This novel method will not damage the sample integrity, and measurement can be implemented anytime rather than only during the freezing process by traditional methods. Hyperspectral imaging (dpeaa)DE-He213 NIR spectroscopy (dpeaa)DE-He213 Freezing rate (dpeaa)DE-He213 Freezing medium temperature (dpeaa)DE-He213 Freezing methods (dpeaa)DE-He213 Sun, Da-Wen verfasserin aut Zhu, Zhiwei verfasserin aut Pu, Hongbin verfasserin aut Enthalten in Food and bioprocess technology New York, NY : Springer Science + Business Media, LLC, 2008 9(2016), 9 vom: 14. Juli, Seite 1444-1454 (DE-627)566012294 (DE-600)2425455-1 1935-5149 nnns volume:9 year:2016 number:9 day:14 month:07 pages:1444-1454 https://dx.doi.org/10.1007/s11947-016-1766-2 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_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_4035 GBV_ILN_4037 GBV_ILN_4046 GBV_ILN_4112 GBV_ILN_4125 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 58.34 ASE AR 9 2016 9 14 07 1444-1454 |
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10.1007/s11947-016-1766-2 doi (DE-627)SPR023097531 (SPR)s11947-016-1766-2-e DE-627 ger DE-627 rakwb eng 660 ASE 58.34 bkl Xie, Anguo verfasserin aut Nondestructive Measurements of Freezing Parameters of Frozen Porcine Meat by NIR Hyperspectral Imaging 2016 Text txt rdacontent Computermedien c rdamedia Online-Ressource cr rdacarrier Abstract The freezing medium temperature and the freezing rate are two important parameters that affect the quality of frozen product. The traditional measurement of freezing parameters will destroy the integrity of the sample and can only be implemented during the freezing process. This study aimed to develop nondestructive hyperspectral imaging (HSI) methods to rapidly detect freezing parameters. The spectral features of the porcine meat samples in frozen state were studied, in which 90 pieces of porcine samples were frozen by different methods with different freezing medium (air and liquid) at different temperatures (from −20 to −120 °C) and freezing rates (from 0.307 to 5.1 cm/h). The result showed that the freezing process would strongly influence spectra of the frozen sample. The reflectance increased with the decrease in freezing medium temperatures, and the negative correlation reached a highly significant level. The freezing parameters did not change the position of the spectral peaks but altered the spectral intensity. Most changes were near 1070, 1172, 1420, 1586, and 1890 nm. The partial least-squares regression spectral models exhibited good performance for predicting freezing medium temperatures %$ \left({R}_c^2=0.898,{R}_p^2=0.844\right) %$ and freezing rates %$ \left({R}_c^2=0.879,{R}_p^2=0.829\right) %$. The study confirmed that could be used for measuring freezing parameters of frozen product. This novel method will not damage the sample integrity, and measurement can be implemented anytime rather than only during the freezing process by traditional methods. Hyperspectral imaging (dpeaa)DE-He213 NIR spectroscopy (dpeaa)DE-He213 Freezing rate (dpeaa)DE-He213 Freezing medium temperature (dpeaa)DE-He213 Freezing methods (dpeaa)DE-He213 Sun, Da-Wen verfasserin aut Zhu, Zhiwei verfasserin aut Pu, Hongbin verfasserin aut Enthalten in Food and bioprocess technology New York, NY : Springer Science + Business Media, LLC, 2008 9(2016), 9 vom: 14. Juli, Seite 1444-1454 (DE-627)566012294 (DE-600)2425455-1 1935-5149 nnns volume:9 year:2016 number:9 day:14 month:07 pages:1444-1454 https://dx.doi.org/10.1007/s11947-016-1766-2 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_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_4035 GBV_ILN_4037 GBV_ILN_4046 GBV_ILN_4112 GBV_ILN_4125 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 58.34 ASE AR 9 2016 9 14 07 1444-1454 |
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10.1007/s11947-016-1766-2 doi (DE-627)SPR023097531 (SPR)s11947-016-1766-2-e DE-627 ger DE-627 rakwb eng 660 ASE 58.34 bkl Xie, Anguo verfasserin aut Nondestructive Measurements of Freezing Parameters of Frozen Porcine Meat by NIR Hyperspectral Imaging 2016 Text txt rdacontent Computermedien c rdamedia Online-Ressource cr rdacarrier Abstract The freezing medium temperature and the freezing rate are two important parameters that affect the quality of frozen product. The traditional measurement of freezing parameters will destroy the integrity of the sample and can only be implemented during the freezing process. This study aimed to develop nondestructive hyperspectral imaging (HSI) methods to rapidly detect freezing parameters. The spectral features of the porcine meat samples in frozen state were studied, in which 90 pieces of porcine samples were frozen by different methods with different freezing medium (air and liquid) at different temperatures (from −20 to −120 °C) and freezing rates (from 0.307 to 5.1 cm/h). The result showed that the freezing process would strongly influence spectra of the frozen sample. The reflectance increased with the decrease in freezing medium temperatures, and the negative correlation reached a highly significant level. The freezing parameters did not change the position of the spectral peaks but altered the spectral intensity. Most changes were near 1070, 1172, 1420, 1586, and 1890 nm. The partial least-squares regression spectral models exhibited good performance for predicting freezing medium temperatures %$ \left({R}_c^2=0.898,{R}_p^2=0.844\right) %$ and freezing rates %$ \left({R}_c^2=0.879,{R}_p^2=0.829\right) %$. The study confirmed that could be used for measuring freezing parameters of frozen product. This novel method will not damage the sample integrity, and measurement can be implemented anytime rather than only during the freezing process by traditional methods. Hyperspectral imaging (dpeaa)DE-He213 NIR spectroscopy (dpeaa)DE-He213 Freezing rate (dpeaa)DE-He213 Freezing medium temperature (dpeaa)DE-He213 Freezing methods (dpeaa)DE-He213 Sun, Da-Wen verfasserin aut Zhu, Zhiwei verfasserin aut Pu, Hongbin verfasserin aut Enthalten in Food and bioprocess technology New York, NY : Springer Science + Business Media, LLC, 2008 9(2016), 9 vom: 14. Juli, Seite 1444-1454 (DE-627)566012294 (DE-600)2425455-1 1935-5149 nnns volume:9 year:2016 number:9 day:14 month:07 pages:1444-1454 https://dx.doi.org/10.1007/s11947-016-1766-2 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_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_4035 GBV_ILN_4037 GBV_ILN_4046 GBV_ILN_4112 GBV_ILN_4125 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 58.34 ASE AR 9 2016 9 14 07 1444-1454 |
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10.1007/s11947-016-1766-2 doi (DE-627)SPR023097531 (SPR)s11947-016-1766-2-e DE-627 ger DE-627 rakwb eng 660 ASE 58.34 bkl Xie, Anguo verfasserin aut Nondestructive Measurements of Freezing Parameters of Frozen Porcine Meat by NIR Hyperspectral Imaging 2016 Text txt rdacontent Computermedien c rdamedia Online-Ressource cr rdacarrier Abstract The freezing medium temperature and the freezing rate are two important parameters that affect the quality of frozen product. The traditional measurement of freezing parameters will destroy the integrity of the sample and can only be implemented during the freezing process. This study aimed to develop nondestructive hyperspectral imaging (HSI) methods to rapidly detect freezing parameters. The spectral features of the porcine meat samples in frozen state were studied, in which 90 pieces of porcine samples were frozen by different methods with different freezing medium (air and liquid) at different temperatures (from −20 to −120 °C) and freezing rates (from 0.307 to 5.1 cm/h). The result showed that the freezing process would strongly influence spectra of the frozen sample. The reflectance increased with the decrease in freezing medium temperatures, and the negative correlation reached a highly significant level. The freezing parameters did not change the position of the spectral peaks but altered the spectral intensity. Most changes were near 1070, 1172, 1420, 1586, and 1890 nm. The partial least-squares regression spectral models exhibited good performance for predicting freezing medium temperatures %$ \left({R}_c^2=0.898,{R}_p^2=0.844\right) %$ and freezing rates %$ \left({R}_c^2=0.879,{R}_p^2=0.829\right) %$. The study confirmed that could be used for measuring freezing parameters of frozen product. This novel method will not damage the sample integrity, and measurement can be implemented anytime rather than only during the freezing process by traditional methods. Hyperspectral imaging (dpeaa)DE-He213 NIR spectroscopy (dpeaa)DE-He213 Freezing rate (dpeaa)DE-He213 Freezing medium temperature (dpeaa)DE-He213 Freezing methods (dpeaa)DE-He213 Sun, Da-Wen verfasserin aut Zhu, Zhiwei verfasserin aut Pu, Hongbin verfasserin aut Enthalten in Food and bioprocess technology New York, NY : Springer Science + Business Media, LLC, 2008 9(2016), 9 vom: 14. Juli, Seite 1444-1454 (DE-627)566012294 (DE-600)2425455-1 1935-5149 nnns volume:9 year:2016 number:9 day:14 month:07 pages:1444-1454 https://dx.doi.org/10.1007/s11947-016-1766-2 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_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_4035 GBV_ILN_4037 GBV_ILN_4046 GBV_ILN_4112 GBV_ILN_4125 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 58.34 ASE AR 9 2016 9 14 07 1444-1454 |
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10.1007/s11947-016-1766-2 doi (DE-627)SPR023097531 (SPR)s11947-016-1766-2-e DE-627 ger DE-627 rakwb eng 660 ASE 58.34 bkl Xie, Anguo verfasserin aut Nondestructive Measurements of Freezing Parameters of Frozen Porcine Meat by NIR Hyperspectral Imaging 2016 Text txt rdacontent Computermedien c rdamedia Online-Ressource cr rdacarrier Abstract The freezing medium temperature and the freezing rate are two important parameters that affect the quality of frozen product. The traditional measurement of freezing parameters will destroy the integrity of the sample and can only be implemented during the freezing process. This study aimed to develop nondestructive hyperspectral imaging (HSI) methods to rapidly detect freezing parameters. The spectral features of the porcine meat samples in frozen state were studied, in which 90 pieces of porcine samples were frozen by different methods with different freezing medium (air and liquid) at different temperatures (from −20 to −120 °C) and freezing rates (from 0.307 to 5.1 cm/h). The result showed that the freezing process would strongly influence spectra of the frozen sample. The reflectance increased with the decrease in freezing medium temperatures, and the negative correlation reached a highly significant level. The freezing parameters did not change the position of the spectral peaks but altered the spectral intensity. Most changes were near 1070, 1172, 1420, 1586, and 1890 nm. The partial least-squares regression spectral models exhibited good performance for predicting freezing medium temperatures %$ \left({R}_c^2=0.898,{R}_p^2=0.844\right) %$ and freezing rates %$ \left({R}_c^2=0.879,{R}_p^2=0.829\right) %$. The study confirmed that could be used for measuring freezing parameters of frozen product. This novel method will not damage the sample integrity, and measurement can be implemented anytime rather than only during the freezing process by traditional methods. Hyperspectral imaging (dpeaa)DE-He213 NIR spectroscopy (dpeaa)DE-He213 Freezing rate (dpeaa)DE-He213 Freezing medium temperature (dpeaa)DE-He213 Freezing methods (dpeaa)DE-He213 Sun, Da-Wen verfasserin aut Zhu, Zhiwei verfasserin aut Pu, Hongbin verfasserin aut Enthalten in Food and bioprocess technology New York, NY : Springer Science + Business Media, LLC, 2008 9(2016), 9 vom: 14. Juli, Seite 1444-1454 (DE-627)566012294 (DE-600)2425455-1 1935-5149 nnns volume:9 year:2016 number:9 day:14 month:07 pages:1444-1454 https://dx.doi.org/10.1007/s11947-016-1766-2 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_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_4035 GBV_ILN_4037 GBV_ILN_4046 GBV_ILN_4112 GBV_ILN_4125 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 58.34 ASE AR 9 2016 9 14 07 1444-1454 |
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English |
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Enthalten in Food and bioprocess technology 9(2016), 9 vom: 14. Juli, Seite 1444-1454 volume:9 year:2016 number:9 day:14 month:07 pages:1444-1454 |
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Enthalten in Food and bioprocess technology 9(2016), 9 vom: 14. Juli, Seite 1444-1454 volume:9 year:2016 number:9 day:14 month:07 pages:1444-1454 |
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findex.gbv.de |
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Hyperspectral imaging NIR spectroscopy Freezing rate Freezing medium temperature Freezing methods |
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Food and bioprocess technology |
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Xie, Anguo @@aut@@ Sun, Da-Wen @@aut@@ Zhu, Zhiwei @@aut@@ Pu, Hongbin @@aut@@ |
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2016-07-14T00:00:00Z |
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The traditional measurement of freezing parameters will destroy the integrity of the sample and can only be implemented during the freezing process. This study aimed to develop nondestructive hyperspectral imaging (HSI) methods to rapidly detect freezing parameters. The spectral features of the porcine meat samples in frozen state were studied, in which 90 pieces of porcine samples were frozen by different methods with different freezing medium (air and liquid) at different temperatures (from −20 to −120 °C) and freezing rates (from 0.307 to 5.1 cm/h). The result showed that the freezing process would strongly influence spectra of the frozen sample. The reflectance increased with the decrease in freezing medium temperatures, and the negative correlation reached a highly significant level. The freezing parameters did not change the position of the spectral peaks but altered the spectral intensity. Most changes were near 1070, 1172, 1420, 1586, and 1890 nm. The partial least-squares regression spectral models exhibited good performance for predicting freezing medium temperatures %$ \left({R}_c^2=0.898,{R}_p^2=0.844\right) %$ and freezing rates %$ \left({R}_c^2=0.879,{R}_p^2=0.829\right) %$. The study confirmed that could be used for measuring freezing parameters of frozen product. This novel method will not damage the sample integrity, and measurement can be implemented anytime rather than only during the freezing process by traditional methods.</subfield></datafield><datafield tag="650" ind1=" " ind2="4"><subfield code="a">Hyperspectral imaging</subfield><subfield code="7">(dpeaa)DE-He213</subfield></datafield><datafield tag="650" ind1=" " ind2="4"><subfield code="a">NIR spectroscopy</subfield><subfield code="7">(dpeaa)DE-He213</subfield></datafield><datafield tag="650" ind1=" " ind2="4"><subfield code="a">Freezing rate</subfield><subfield code="7">(dpeaa)DE-He213</subfield></datafield><datafield tag="650" ind1=" " ind2="4"><subfield code="a">Freezing medium temperature</subfield><subfield code="7">(dpeaa)DE-He213</subfield></datafield><datafield tag="650" ind1=" " ind2="4"><subfield code="a">Freezing methods</subfield><subfield code="7">(dpeaa)DE-He213</subfield></datafield><datafield tag="700" ind1="1" ind2=" "><subfield code="a">Sun, Da-Wen</subfield><subfield code="e">verfasserin</subfield><subfield code="4">aut</subfield></datafield><datafield tag="700" ind1="1" ind2=" "><subfield code="a">Zhu, Zhiwei</subfield><subfield code="e">verfasserin</subfield><subfield code="4">aut</subfield></datafield><datafield tag="700" ind1="1" ind2=" "><subfield code="a">Pu, Hongbin</subfield><subfield code="e">verfasserin</subfield><subfield code="4">aut</subfield></datafield><datafield tag="773" ind1="0" ind2="8"><subfield code="i">Enthalten in</subfield><subfield code="t">Food and bioprocess technology</subfield><subfield code="d">New York, NY : Springer Science + Business Media, LLC, 2008</subfield><subfield code="g">9(2016), 9 vom: 14. 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Xie, Anguo |
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Xie, Anguo ddc 660 bkl 58.34 misc Hyperspectral imaging misc NIR spectroscopy misc Freezing rate misc Freezing medium temperature misc Freezing methods Nondestructive Measurements of Freezing Parameters of Frozen Porcine Meat by NIR Hyperspectral Imaging |
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660 ASE 58.34 bkl Nondestructive Measurements of Freezing Parameters of Frozen Porcine Meat by NIR Hyperspectral Imaging Hyperspectral imaging (dpeaa)DE-He213 NIR spectroscopy (dpeaa)DE-He213 Freezing rate (dpeaa)DE-He213 Freezing medium temperature (dpeaa)DE-He213 Freezing methods (dpeaa)DE-He213 |
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ddc 660 bkl 58.34 misc Hyperspectral imaging misc NIR spectroscopy misc Freezing rate misc Freezing medium temperature misc Freezing methods |
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ddc 660 bkl 58.34 misc Hyperspectral imaging misc NIR spectroscopy misc Freezing rate misc Freezing medium temperature misc Freezing methods |
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Nondestructive Measurements of Freezing Parameters of Frozen Porcine Meat by NIR Hyperspectral Imaging |
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Nondestructive Measurements of Freezing Parameters of Frozen Porcine Meat by NIR Hyperspectral Imaging |
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nondestructive measurements of freezing parameters of frozen porcine meat by nir hyperspectral imaging |
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Nondestructive Measurements of Freezing Parameters of Frozen Porcine Meat by NIR Hyperspectral Imaging |
| abstract |
Abstract The freezing medium temperature and the freezing rate are two important parameters that affect the quality of frozen product. The traditional measurement of freezing parameters will destroy the integrity of the sample and can only be implemented during the freezing process. This study aimed to develop nondestructive hyperspectral imaging (HSI) methods to rapidly detect freezing parameters. The spectral features of the porcine meat samples in frozen state were studied, in which 90 pieces of porcine samples were frozen by different methods with different freezing medium (air and liquid) at different temperatures (from −20 to −120 °C) and freezing rates (from 0.307 to 5.1 cm/h). The result showed that the freezing process would strongly influence spectra of the frozen sample. The reflectance increased with the decrease in freezing medium temperatures, and the negative correlation reached a highly significant level. The freezing parameters did not change the position of the spectral peaks but altered the spectral intensity. Most changes were near 1070, 1172, 1420, 1586, and 1890 nm. The partial least-squares regression spectral models exhibited good performance for predicting freezing medium temperatures %$ \left({R}_c^2=0.898,{R}_p^2=0.844\right) %$ and freezing rates %$ \left({R}_c^2=0.879,{R}_p^2=0.829\right) %$. The study confirmed that could be used for measuring freezing parameters of frozen product. This novel method will not damage the sample integrity, and measurement can be implemented anytime rather than only during the freezing process by traditional methods. |
| abstractGer |
Abstract The freezing medium temperature and the freezing rate are two important parameters that affect the quality of frozen product. The traditional measurement of freezing parameters will destroy the integrity of the sample and can only be implemented during the freezing process. This study aimed to develop nondestructive hyperspectral imaging (HSI) methods to rapidly detect freezing parameters. The spectral features of the porcine meat samples in frozen state were studied, in which 90 pieces of porcine samples were frozen by different methods with different freezing medium (air and liquid) at different temperatures (from −20 to −120 °C) and freezing rates (from 0.307 to 5.1 cm/h). The result showed that the freezing process would strongly influence spectra of the frozen sample. The reflectance increased with the decrease in freezing medium temperatures, and the negative correlation reached a highly significant level. The freezing parameters did not change the position of the spectral peaks but altered the spectral intensity. Most changes were near 1070, 1172, 1420, 1586, and 1890 nm. The partial least-squares regression spectral models exhibited good performance for predicting freezing medium temperatures %$ \left({R}_c^2=0.898,{R}_p^2=0.844\right) %$ and freezing rates %$ \left({R}_c^2=0.879,{R}_p^2=0.829\right) %$. The study confirmed that could be used for measuring freezing parameters of frozen product. This novel method will not damage the sample integrity, and measurement can be implemented anytime rather than only during the freezing process by traditional methods. |
| abstract_unstemmed |
Abstract The freezing medium temperature and the freezing rate are two important parameters that affect the quality of frozen product. The traditional measurement of freezing parameters will destroy the integrity of the sample and can only be implemented during the freezing process. This study aimed to develop nondestructive hyperspectral imaging (HSI) methods to rapidly detect freezing parameters. The spectral features of the porcine meat samples in frozen state were studied, in which 90 pieces of porcine samples were frozen by different methods with different freezing medium (air and liquid) at different temperatures (from −20 to −120 °C) and freezing rates (from 0.307 to 5.1 cm/h). The result showed that the freezing process would strongly influence spectra of the frozen sample. The reflectance increased with the decrease in freezing medium temperatures, and the negative correlation reached a highly significant level. The freezing parameters did not change the position of the spectral peaks but altered the spectral intensity. Most changes were near 1070, 1172, 1420, 1586, and 1890 nm. The partial least-squares regression spectral models exhibited good performance for predicting freezing medium temperatures %$ \left({R}_c^2=0.898,{R}_p^2=0.844\right) %$ and freezing rates %$ \left({R}_c^2=0.879,{R}_p^2=0.829\right) %$. The study confirmed that could be used for measuring freezing parameters of frozen product. This novel method will not damage the sample integrity, and measurement can be implemented anytime rather than only during the freezing process by traditional methods. |
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| container_issue |
9 |
| title_short |
Nondestructive Measurements of Freezing Parameters of Frozen Porcine Meat by NIR Hyperspectral Imaging |
| url |
https://dx.doi.org/10.1007/s11947-016-1766-2 |
| remote_bool |
true |
| author2 |
Sun, Da-Wen Zhu, Zhiwei Pu, Hongbin |
| author2Str |
Sun, Da-Wen Zhu, Zhiwei Pu, Hongbin |
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| doi_str |
10.1007/s11947-016-1766-2 |
| up_date |
2024-07-03T16:51:15.754Z |
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| score |
7.4018154 |