Hyperspectral Surface Analysis for Ripeness Estimation and Quick UV-C Surface Treatments for Preservation of Bananas
This study aimed to determine the ripeness of bananas using hyperspectral surface analysis and how a rapid UV-C (ultraviolet-C light) surface treatment could reduce decay. The surface of the banana fruit and its stages of maturity were studied using a hyperspectral imaging technique in the visible a...
Ausführliche Beschreibung
Gespeichert in:
| Autor*in: |
Zhao, W. [verfasserIn] Yang, Zh. [verfasserIn] Chen, Zh. [verfasserIn] Liu, J. [verfasserIn] Wang, W. Ch. [verfasserIn] Zheng, W. Yu. [verfasserIn] |
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| Format: |
E-Artikel |
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| Sprache: |
Englisch |
| Erschienen: |
2016 |
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| Übergeordnetes Werk: |
Enthalten in: Journal of applied spectroscopy - Dordrecht [u.a.] : Springer Science + Business Media B.V, 1965, 83(2016), 2 vom: Mai, Seite 254-260 |
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| Übergeordnetes Werk: |
volume:83 ; year:2016 ; number:2 ; month:05 ; pages:254-260 |
| Links: |
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| DOI / URN: |
10.1007/s10812-016-0278-1 |
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| Katalog-ID: |
SPR013507958 |
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| 520 | |a This study aimed to determine the ripeness of bananas using hyperspectral surface analysis and how a rapid UV-C (ultraviolet-C light) surface treatment could reduce decay. The surface of the banana fruit and its stages of maturity were studied using a hyperspectral imaging technique in the visible and near infrared (370–1000 nm) regions. The vselected color ratios from these spectral images were used for classifying the whole banana into immature, ripe, half-ripe and overripe stages. By using a BP neural network, models based on the wavelengths were developed to predict quality attributes. The mean discrimination rate was 98.17%. The surface of the fresh bananas was treated with UV-C at dosages from 15–55 μW/$ cm^{2} $. The visual qualities with or without UV-C treatment were compared using the image, the chromatic aberration test, the firmness test and the area of black spot on the banana skin. The results showed that high dosages of UV-C damaged the banana skin, while low dosages were more efficient at delaying changes in the relative brightness of the skin. The maximum UV-C treatment dose for satisfactory banana preservation was between 21 and 24 μW/$ cm^{2} $. These results could help to improve the visual quality of bananas and to classify their ripeness more easily. | ||
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| 700 | 1 | |a Chen, Zh. |e verfasserin |4 aut | |
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| 700 | 1 | |a Wang, W. Ch. |e verfasserin |4 aut | |
| 700 | 1 | |a Zheng, W. Yu. |e verfasserin |4 aut | |
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10.1007/s10812-016-0278-1 doi (DE-627)SPR013507958 (SPR)s10812-016-0278-1-e DE-627 ger DE-627 rakwb eng 530 ASE 33.00 bkl Zhao, W. verfasserin aut Hyperspectral Surface Analysis for Ripeness Estimation and Quick UV-C Surface Treatments for Preservation of Bananas 2016 Text txt rdacontent Computermedien c rdamedia Online-Ressource cr rdacarrier This study aimed to determine the ripeness of bananas using hyperspectral surface analysis and how a rapid UV-C (ultraviolet-C light) surface treatment could reduce decay. The surface of the banana fruit and its stages of maturity were studied using a hyperspectral imaging technique in the visible and near infrared (370–1000 nm) regions. The vselected color ratios from these spectral images were used for classifying the whole banana into immature, ripe, half-ripe and overripe stages. By using a BP neural network, models based on the wavelengths were developed to predict quality attributes. The mean discrimination rate was 98.17%. The surface of the fresh bananas was treated with UV-C at dosages from 15–55 μW/$ cm^{2} $. The visual qualities with or without UV-C treatment were compared using the image, the chromatic aberration test, the firmness test and the area of black spot on the banana skin. The results showed that high dosages of UV-C damaged the banana skin, while low dosages were more efficient at delaying changes in the relative brightness of the skin. The maximum UV-C treatment dose for satisfactory banana preservation was between 21 and 24 μW/$ cm^{2} $. These results could help to improve the visual quality of bananas and to classify their ripeness more easily. Yang, Zh. verfasserin aut Chen, Zh. verfasserin aut Liu, J. verfasserin aut Wang, W. Ch. verfasserin aut Zheng, W. Yu. verfasserin aut Enthalten in Journal of applied spectroscopy Dordrecht [u.a.] : Springer Science + Business Media B.V, 1965 83(2016), 2 vom: Mai, Seite 254-260 (DE-627)325609918 (DE-600)2037920-1 1573-8647 nnns volume:83 year:2016 number:2 month:05 pages:254-260 https://dx.doi.org/10.1007/s10812-016-0278-1 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_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_206 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 33.00 ASE AR 83 2016 2 05 254-260 |
| spelling |
10.1007/s10812-016-0278-1 doi (DE-627)SPR013507958 (SPR)s10812-016-0278-1-e DE-627 ger DE-627 rakwb eng 530 ASE 33.00 bkl Zhao, W. verfasserin aut Hyperspectral Surface Analysis for Ripeness Estimation and Quick UV-C Surface Treatments for Preservation of Bananas 2016 Text txt rdacontent Computermedien c rdamedia Online-Ressource cr rdacarrier This study aimed to determine the ripeness of bananas using hyperspectral surface analysis and how a rapid UV-C (ultraviolet-C light) surface treatment could reduce decay. The surface of the banana fruit and its stages of maturity were studied using a hyperspectral imaging technique in the visible and near infrared (370–1000 nm) regions. The vselected color ratios from these spectral images were used for classifying the whole banana into immature, ripe, half-ripe and overripe stages. By using a BP neural network, models based on the wavelengths were developed to predict quality attributes. The mean discrimination rate was 98.17%. The surface of the fresh bananas was treated with UV-C at dosages from 15–55 μW/$ cm^{2} $. The visual qualities with or without UV-C treatment were compared using the image, the chromatic aberration test, the firmness test and the area of black spot on the banana skin. The results showed that high dosages of UV-C damaged the banana skin, while low dosages were more efficient at delaying changes in the relative brightness of the skin. The maximum UV-C treatment dose for satisfactory banana preservation was between 21 and 24 μW/$ cm^{2} $. These results could help to improve the visual quality of bananas and to classify their ripeness more easily. Yang, Zh. verfasserin aut Chen, Zh. verfasserin aut Liu, J. verfasserin aut Wang, W. Ch. verfasserin aut Zheng, W. Yu. verfasserin aut Enthalten in Journal of applied spectroscopy Dordrecht [u.a.] : Springer Science + Business Media B.V, 1965 83(2016), 2 vom: Mai, Seite 254-260 (DE-627)325609918 (DE-600)2037920-1 1573-8647 nnns volume:83 year:2016 number:2 month:05 pages:254-260 https://dx.doi.org/10.1007/s10812-016-0278-1 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_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_206 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 33.00 ASE AR 83 2016 2 05 254-260 |
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10.1007/s10812-016-0278-1 doi (DE-627)SPR013507958 (SPR)s10812-016-0278-1-e DE-627 ger DE-627 rakwb eng 530 ASE 33.00 bkl Zhao, W. verfasserin aut Hyperspectral Surface Analysis for Ripeness Estimation and Quick UV-C Surface Treatments for Preservation of Bananas 2016 Text txt rdacontent Computermedien c rdamedia Online-Ressource cr rdacarrier This study aimed to determine the ripeness of bananas using hyperspectral surface analysis and how a rapid UV-C (ultraviolet-C light) surface treatment could reduce decay. The surface of the banana fruit and its stages of maturity were studied using a hyperspectral imaging technique in the visible and near infrared (370–1000 nm) regions. The vselected color ratios from these spectral images were used for classifying the whole banana into immature, ripe, half-ripe and overripe stages. By using a BP neural network, models based on the wavelengths were developed to predict quality attributes. The mean discrimination rate was 98.17%. The surface of the fresh bananas was treated with UV-C at dosages from 15–55 μW/$ cm^{2} $. The visual qualities with or without UV-C treatment were compared using the image, the chromatic aberration test, the firmness test and the area of black spot on the banana skin. The results showed that high dosages of UV-C damaged the banana skin, while low dosages were more efficient at delaying changes in the relative brightness of the skin. The maximum UV-C treatment dose for satisfactory banana preservation was between 21 and 24 μW/$ cm^{2} $. These results could help to improve the visual quality of bananas and to classify their ripeness more easily. Yang, Zh. verfasserin aut Chen, Zh. verfasserin aut Liu, J. verfasserin aut Wang, W. Ch. verfasserin aut Zheng, W. Yu. verfasserin aut Enthalten in Journal of applied spectroscopy Dordrecht [u.a.] : Springer Science + Business Media B.V, 1965 83(2016), 2 vom: Mai, Seite 254-260 (DE-627)325609918 (DE-600)2037920-1 1573-8647 nnns volume:83 year:2016 number:2 month:05 pages:254-260 https://dx.doi.org/10.1007/s10812-016-0278-1 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_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_206 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 33.00 ASE AR 83 2016 2 05 254-260 |
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10.1007/s10812-016-0278-1 doi (DE-627)SPR013507958 (SPR)s10812-016-0278-1-e DE-627 ger DE-627 rakwb eng 530 ASE 33.00 bkl Zhao, W. verfasserin aut Hyperspectral Surface Analysis for Ripeness Estimation and Quick UV-C Surface Treatments for Preservation of Bananas 2016 Text txt rdacontent Computermedien c rdamedia Online-Ressource cr rdacarrier This study aimed to determine the ripeness of bananas using hyperspectral surface analysis and how a rapid UV-C (ultraviolet-C light) surface treatment could reduce decay. The surface of the banana fruit and its stages of maturity were studied using a hyperspectral imaging technique in the visible and near infrared (370–1000 nm) regions. The vselected color ratios from these spectral images were used for classifying the whole banana into immature, ripe, half-ripe and overripe stages. By using a BP neural network, models based on the wavelengths were developed to predict quality attributes. The mean discrimination rate was 98.17%. The surface of the fresh bananas was treated with UV-C at dosages from 15–55 μW/$ cm^{2} $. The visual qualities with or without UV-C treatment were compared using the image, the chromatic aberration test, the firmness test and the area of black spot on the banana skin. The results showed that high dosages of UV-C damaged the banana skin, while low dosages were more efficient at delaying changes in the relative brightness of the skin. The maximum UV-C treatment dose for satisfactory banana preservation was between 21 and 24 μW/$ cm^{2} $. These results could help to improve the visual quality of bananas and to classify their ripeness more easily. Yang, Zh. verfasserin aut Chen, Zh. verfasserin aut Liu, J. verfasserin aut Wang, W. Ch. verfasserin aut Zheng, W. Yu. verfasserin aut Enthalten in Journal of applied spectroscopy Dordrecht [u.a.] : Springer Science + Business Media B.V, 1965 83(2016), 2 vom: Mai, Seite 254-260 (DE-627)325609918 (DE-600)2037920-1 1573-8647 nnns volume:83 year:2016 number:2 month:05 pages:254-260 https://dx.doi.org/10.1007/s10812-016-0278-1 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_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_206 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 33.00 ASE AR 83 2016 2 05 254-260 |
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10.1007/s10812-016-0278-1 doi (DE-627)SPR013507958 (SPR)s10812-016-0278-1-e DE-627 ger DE-627 rakwb eng 530 ASE 33.00 bkl Zhao, W. verfasserin aut Hyperspectral Surface Analysis for Ripeness Estimation and Quick UV-C Surface Treatments for Preservation of Bananas 2016 Text txt rdacontent Computermedien c rdamedia Online-Ressource cr rdacarrier This study aimed to determine the ripeness of bananas using hyperspectral surface analysis and how a rapid UV-C (ultraviolet-C light) surface treatment could reduce decay. The surface of the banana fruit and its stages of maturity were studied using a hyperspectral imaging technique in the visible and near infrared (370–1000 nm) regions. The vselected color ratios from these spectral images were used for classifying the whole banana into immature, ripe, half-ripe and overripe stages. By using a BP neural network, models based on the wavelengths were developed to predict quality attributes. The mean discrimination rate was 98.17%. The surface of the fresh bananas was treated with UV-C at dosages from 15–55 μW/$ cm^{2} $. The visual qualities with or without UV-C treatment were compared using the image, the chromatic aberration test, the firmness test and the area of black spot on the banana skin. The results showed that high dosages of UV-C damaged the banana skin, while low dosages were more efficient at delaying changes in the relative brightness of the skin. The maximum UV-C treatment dose for satisfactory banana preservation was between 21 and 24 μW/$ cm^{2} $. These results could help to improve the visual quality of bananas and to classify their ripeness more easily. Yang, Zh. verfasserin aut Chen, Zh. verfasserin aut Liu, J. verfasserin aut Wang, W. Ch. verfasserin aut Zheng, W. Yu. verfasserin aut Enthalten in Journal of applied spectroscopy Dordrecht [u.a.] : Springer Science + Business Media B.V, 1965 83(2016), 2 vom: Mai, Seite 254-260 (DE-627)325609918 (DE-600)2037920-1 1573-8647 nnns volume:83 year:2016 number:2 month:05 pages:254-260 https://dx.doi.org/10.1007/s10812-016-0278-1 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_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_206 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 33.00 ASE AR 83 2016 2 05 254-260 |
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The surface of the banana fruit and its stages of maturity were studied using a hyperspectral imaging technique in the visible and near infrared (370–1000 nm) regions. The vselected color ratios from these spectral images were used for classifying the whole banana into immature, ripe, half-ripe and overripe stages. By using a BP neural network, models based on the wavelengths were developed to predict quality attributes. The mean discrimination rate was 98.17%. The surface of the fresh bananas was treated with UV-C at dosages from 15–55 μW/$ cm^{2} $. The visual qualities with or without UV-C treatment were compared using the image, the chromatic aberration test, the firmness test and the area of black spot on the banana skin. The results showed that high dosages of UV-C damaged the banana skin, while low dosages were more efficient at delaying changes in the relative brightness of the skin. 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Zhao, W. |
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Zhao, W. ddc 530 bkl 33.00 Hyperspectral Surface Analysis for Ripeness Estimation and Quick UV-C Surface Treatments for Preservation of Bananas |
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530 ASE 33.00 bkl Hyperspectral Surface Analysis for Ripeness Estimation and Quick UV-C Surface Treatments for Preservation of Bananas |
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Hyperspectral Surface Analysis for Ripeness Estimation and Quick UV-C Surface Treatments for Preservation of Bananas |
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Hyperspectral Surface Analysis for Ripeness Estimation and Quick UV-C Surface Treatments for Preservation of Bananas |
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Zhao, W. |
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Journal of applied spectroscopy |
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Zhao, W. Yang, Zh. Chen, Zh. Liu, J. Wang, W. Ch. Zheng, W. Yu. |
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hyperspectral surface analysis for ripeness estimation and quick uv-c surface treatments for preservation of bananas |
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Hyperspectral Surface Analysis for Ripeness Estimation and Quick UV-C Surface Treatments for Preservation of Bananas |
| abstract |
This study aimed to determine the ripeness of bananas using hyperspectral surface analysis and how a rapid UV-C (ultraviolet-C light) surface treatment could reduce decay. The surface of the banana fruit and its stages of maturity were studied using a hyperspectral imaging technique in the visible and near infrared (370–1000 nm) regions. The vselected color ratios from these spectral images were used for classifying the whole banana into immature, ripe, half-ripe and overripe stages. By using a BP neural network, models based on the wavelengths were developed to predict quality attributes. The mean discrimination rate was 98.17%. The surface of the fresh bananas was treated with UV-C at dosages from 15–55 μW/$ cm^{2} $. The visual qualities with or without UV-C treatment were compared using the image, the chromatic aberration test, the firmness test and the area of black spot on the banana skin. The results showed that high dosages of UV-C damaged the banana skin, while low dosages were more efficient at delaying changes in the relative brightness of the skin. The maximum UV-C treatment dose for satisfactory banana preservation was between 21 and 24 μW/$ cm^{2} $. These results could help to improve the visual quality of bananas and to classify their ripeness more easily. |
| abstractGer |
This study aimed to determine the ripeness of bananas using hyperspectral surface analysis and how a rapid UV-C (ultraviolet-C light) surface treatment could reduce decay. The surface of the banana fruit and its stages of maturity were studied using a hyperspectral imaging technique in the visible and near infrared (370–1000 nm) regions. The vselected color ratios from these spectral images were used for classifying the whole banana into immature, ripe, half-ripe and overripe stages. By using a BP neural network, models based on the wavelengths were developed to predict quality attributes. The mean discrimination rate was 98.17%. The surface of the fresh bananas was treated with UV-C at dosages from 15–55 μW/$ cm^{2} $. The visual qualities with or without UV-C treatment were compared using the image, the chromatic aberration test, the firmness test and the area of black spot on the banana skin. The results showed that high dosages of UV-C damaged the banana skin, while low dosages were more efficient at delaying changes in the relative brightness of the skin. The maximum UV-C treatment dose for satisfactory banana preservation was between 21 and 24 μW/$ cm^{2} $. These results could help to improve the visual quality of bananas and to classify their ripeness more easily. |
| abstract_unstemmed |
This study aimed to determine the ripeness of bananas using hyperspectral surface analysis and how a rapid UV-C (ultraviolet-C light) surface treatment could reduce decay. The surface of the banana fruit and its stages of maturity were studied using a hyperspectral imaging technique in the visible and near infrared (370–1000 nm) regions. The vselected color ratios from these spectral images were used for classifying the whole banana into immature, ripe, half-ripe and overripe stages. By using a BP neural network, models based on the wavelengths were developed to predict quality attributes. The mean discrimination rate was 98.17%. The surface of the fresh bananas was treated with UV-C at dosages from 15–55 μW/$ cm^{2} $. The visual qualities with or without UV-C treatment were compared using the image, the chromatic aberration test, the firmness test and the area of black spot on the banana skin. The results showed that high dosages of UV-C damaged the banana skin, while low dosages were more efficient at delaying changes in the relative brightness of the skin. The maximum UV-C treatment dose for satisfactory banana preservation was between 21 and 24 μW/$ cm^{2} $. These results could help to improve the visual quality of bananas and to classify their ripeness more easily. |
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| title_short |
Hyperspectral Surface Analysis for Ripeness Estimation and Quick UV-C Surface Treatments for Preservation of Bananas |
| url |
https://dx.doi.org/10.1007/s10812-016-0278-1 |
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| author2 |
Yang, Zh Chen, Zh Liu, J. Wang, W. Ch Zheng, W. Yu |
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Yang, Zh Chen, Zh Liu, J. Wang, W. Ch Zheng, W. Yu |
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| doi_str |
10.1007/s10812-016-0278-1 |
| up_date |
2024-07-03T20:12:50.335Z |
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| score |
7.4006233 |