Detection of Invisible Damages in ‘Rojo Brillante’ Persimmon Fruit at Different Stages Using Hyperspectral Imaging and Chemometrics
The main cause of flesh browning in ‘Rojo Brillante’ persimmon fruit is mechanical damage caused during harvesting and packing. Innovation and research on nondestructive techniques to detect this phenomenon in the packing lines are necessary because this type of alteration is often only seen when th...
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| Format: | article |
| Language: | Inglés |
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MDPI
2021
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| Online Access: | http://hdl.handle.net/20.500.11939/7618 https://www.mdpi.com/2304-8158/10/9/2170 |
| _version_ | 1855032638993072128 |
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| author | Munera, Sandra Rodríguez-Ortega, Alejandro Aleixos, Nuria Cubero, Sergio Gómez-Sanchís, Juan Blasco, José |
| author_browse | Aleixos, Nuria Blasco, José Cubero, Sergio Gómez-Sanchís, Juan Munera, Sandra Rodríguez-Ortega, Alejandro |
| author_facet | Munera, Sandra Rodríguez-Ortega, Alejandro Aleixos, Nuria Cubero, Sergio Gómez-Sanchís, Juan Blasco, José |
| author_sort | Munera, Sandra |
| collection | ReDivia |
| description | The main cause of flesh browning in ‘Rojo Brillante’ persimmon fruit is mechanical damage caused during harvesting and packing. Innovation and research on nondestructive techniques to detect this phenomenon in the packing lines are necessary because this type of alteration is often only seen when the final consumer peels the fruit. In this work, we have studied the application of hyperspectral imaging in the range of 450–1040 nm to detect mechanical damage without any external symptoms. The fruit was damaged in a controlled manner. Later, images were acquired before and at 0, 1, 2 and 3 days after damage induction. First, the spectral data captured from the images were analysed through an algorithm based on principal component analysis (PCA). The aim was to automatically separate intact and damaged fruit, and to detect the damage in the PC images when present. With this algorithm, 90.0% of intact fruit and 90.8% of damaged fruit were correctly detected. A model based on partial least squares—discriminant analysis (PLS-DA), was later calibrated using the mean spectrum of the pixels detected as damaged, to determine the moment when the fruit was damaged. The model differentiated fruit corresponding correctly to 0, 1, 2 and 3 days after damage induction, achieving a total accuracy of 99.4%. |
| format | article |
| id | ReDivia7618 |
| institution | Instituto Valenciano de Investigaciones Agrarias (IVIA) |
| language | Inglés |
| publishDate | 2021 |
| publishDateRange | 2021 |
| publishDateSort | 2021 |
| publisher | MDPI |
| publisherStr | MDPI |
| record_format | dspace |
| spelling | ReDivia76182025-04-25T14:48:25Z Detection of Invisible Damages in ‘Rojo Brillante’ Persimmon Fruit at Different Stages Using Hyperspectral Imaging and Chemometrics Munera, Sandra Rodríguez-Ortega, Alejandro Aleixos, Nuria Cubero, Sergio Gómez-Sanchís, Juan Blasco, José Fruit quality Nondestructive Chemometrics N01 Agricultural engineering H20 Plant diseases H50 Miscellaneous plant disorders Q01 Food science and technology Q02 Food processing and preservation Diospyros kaki Browning Computer vision The main cause of flesh browning in ‘Rojo Brillante’ persimmon fruit is mechanical damage caused during harvesting and packing. Innovation and research on nondestructive techniques to detect this phenomenon in the packing lines are necessary because this type of alteration is often only seen when the final consumer peels the fruit. In this work, we have studied the application of hyperspectral imaging in the range of 450–1040 nm to detect mechanical damage without any external symptoms. The fruit was damaged in a controlled manner. Later, images were acquired before and at 0, 1, 2 and 3 days after damage induction. First, the spectral data captured from the images were analysed through an algorithm based on principal component analysis (PCA). The aim was to automatically separate intact and damaged fruit, and to detect the damage in the PC images when present. With this algorithm, 90.0% of intact fruit and 90.8% of damaged fruit were correctly detected. A model based on partial least squares—discriminant analysis (PLS-DA), was later calibrated using the mean spectrum of the pixels detected as damaged, to determine the moment when the fruit was damaged. The model differentiated fruit corresponding correctly to 0, 1, 2 and 3 days after damage induction, achieving a total accuracy of 99.4%. 2021-09-23T16:22:30Z 2021-09-23T16:22:30Z 2021 article publishedVersion Munera, S., Rodríguez-Ortega, A., Aleixos, N., Cubero, S., Gómez-Sanchis, J. & Blasco, J. (2021). Detection of Invisible Damages in ‘Rojo Brillante’Persimmon Fruit at Different Stages Using Hyperspectral Imaging and Chemometrics. Foods, 10(9), 2170. 2304-8158 http://hdl.handle.net/20.500.11939/7618 10.3390/foods10092170 https://www.mdpi.com/2304-8158/10/9/2170 en info:eu-repo/grantAgreement/ERDF/POCV 2014-2020/51918 This work is co-funded by the projects AEI PID2019-107347RR-C31, PID2019-107347RRC32, PID2019-107347RR-C33, IVIA-GVA 51918 and the European Union through the European Regional Development Fund (ERDF) of the Generalitat Valenciana 2014–2020. Atribución-NoComercial-SinDerivadas 3.0 España http://creativecommons.org/licenses/by-nc-nd/3.0/es/ openAccess MDPI electronico |
| spellingShingle | Fruit quality Nondestructive Chemometrics N01 Agricultural engineering H20 Plant diseases H50 Miscellaneous plant disorders Q01 Food science and technology Q02 Food processing and preservation Diospyros kaki Browning Computer vision Munera, Sandra Rodríguez-Ortega, Alejandro Aleixos, Nuria Cubero, Sergio Gómez-Sanchís, Juan Blasco, José Detection of Invisible Damages in ‘Rojo Brillante’ Persimmon Fruit at Different Stages Using Hyperspectral Imaging and Chemometrics |
| title | Detection of Invisible Damages in ‘Rojo Brillante’ Persimmon Fruit at Different Stages Using Hyperspectral Imaging and Chemometrics |
| title_full | Detection of Invisible Damages in ‘Rojo Brillante’ Persimmon Fruit at Different Stages Using Hyperspectral Imaging and Chemometrics |
| title_fullStr | Detection of Invisible Damages in ‘Rojo Brillante’ Persimmon Fruit at Different Stages Using Hyperspectral Imaging and Chemometrics |
| title_full_unstemmed | Detection of Invisible Damages in ‘Rojo Brillante’ Persimmon Fruit at Different Stages Using Hyperspectral Imaging and Chemometrics |
| title_short | Detection of Invisible Damages in ‘Rojo Brillante’ Persimmon Fruit at Different Stages Using Hyperspectral Imaging and Chemometrics |
| title_sort | detection of invisible damages in rojo brillante persimmon fruit at different stages using hyperspectral imaging and chemometrics |
| topic | Fruit quality Nondestructive Chemometrics N01 Agricultural engineering H20 Plant diseases H50 Miscellaneous plant disorders Q01 Food science and technology Q02 Food processing and preservation Diospyros kaki Browning Computer vision |
| url | http://hdl.handle.net/20.500.11939/7618 https://www.mdpi.com/2304-8158/10/9/2170 |
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