Maturity assessment of ‘Rojo Brillante’ persimmon by Hyperspectral Imaging
Persimmon cv. ‘Rojo Brillante’ is an astringent cultivar highly appreciated by consumers due to its good aspect, high size, sweetness and absence of seeds. However, this cultivar is very astringent and the fruit cannot be consumed until a high degree of overripeness with takes long time and makes th...
| Autores principales: | , , , , , , |
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| Formato: | Objeto de conferencia |
| Lenguaje: | Español |
| Publicado: |
2021
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| Materias: | |
| Acceso en línea: | http://hdl.handle.net/20.500.11939/7586 https://conferences.au.dk/uploads/tx_powermail/2016cigr_ageng_full_paper_smp.pdf |
| _version_ | 1855492314136313856 |
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| author | Munera, Sandra Blasco, José Cubero, Sergio Besada, Cristina Salvador, Alejandra Talens, Pau Aleixos, Nuria |
| author_browse | Aleixos, Nuria Besada, Cristina Blasco, José Cubero, Sergio Munera, Sandra Salvador, Alejandra Talens, Pau |
| author_facet | Munera, Sandra Blasco, José Cubero, Sergio Besada, Cristina Salvador, Alejandra Talens, Pau Aleixos, Nuria |
| author_sort | Munera, Sandra |
| collection | ReDivia |
| description | Persimmon cv. ‘Rojo Brillante’ is an astringent cultivar highly appreciated by consumers due to its good aspect, high size, sweetness and absence of seeds. However, this cultivar is very astringent and the fruit cannot be consumed until a high degree of overripeness with takes long time and makes the fruit difficult to handle. A method based on exposing fruit to high CO2 concentrations was recently developed to eliminate quickly the astringency preserving the firmness; however, the adequate duration of this treatment depends mostly on the maturity at harvest. Therefore the aim of this work was to investigate a non-destructive and reliable method based on hyperspectral imaging to assess the maturity of persimmon cv ‘Rojo Brillante’ before deastringency treatments. For this purpose, 150 persimmon fruits were harvested at three different stages of commercial maturity and flesh firmness was determined after the image acquisition. Hyperspectral images of each fruit were taken using a hyperspectral system based on two liquid crystal tuneable filters, sensitive in the spectral range 420-1080 nm. Partial Least Square-Discriminant Analysis (PLS-DA) was used on the hyperspectral images to select optimal wavelengths and classify persimmon fruits by maturity. The results achieved 90.1% of correct classification using six selected wavelengths. Additionally, flesh firmness was predicted by using partial least square regression (PLS-R) and the selected wavelengths. A R2 of 0.80 and a square error of prediction (SEP) of 4.34 N were obtained. All of these results were considered as good for a non-invasive maturity assessment technique of ‘Rojo Brillante’ persimmon. |
| format | Objeto de conferencia |
| id | ReDivia7586 |
| institution | Instituto Valenciano de Investigaciones Agrarias (IVIA) |
| language | Español |
| publishDate | 2021 |
| publishDateRange | 2021 |
| publishDateSort | 2021 |
| record_format | dspace |
| spelling | ReDivia75862025-04-25T14:53:06Z Maturity assessment of ‘Rojo Brillante’ persimmon by Hyperspectral Imaging Munera, Sandra Blasco, José Cubero, Sergio Besada, Cristina Salvador, Alejandra Talens, Pau Aleixos, Nuria Internal quality Q01 Food science and technology N01 Agricultural engineering J10 Handling, transport, storage and protection of agricultural products Fruit Classification Prediction Computer vision Persimmon cv. ‘Rojo Brillante’ is an astringent cultivar highly appreciated by consumers due to its good aspect, high size, sweetness and absence of seeds. However, this cultivar is very astringent and the fruit cannot be consumed until a high degree of overripeness with takes long time and makes the fruit difficult to handle. A method based on exposing fruit to high CO2 concentrations was recently developed to eliminate quickly the astringency preserving the firmness; however, the adequate duration of this treatment depends mostly on the maturity at harvest. Therefore the aim of this work was to investigate a non-destructive and reliable method based on hyperspectral imaging to assess the maturity of persimmon cv ‘Rojo Brillante’ before deastringency treatments. For this purpose, 150 persimmon fruits were harvested at three different stages of commercial maturity and flesh firmness was determined after the image acquisition. Hyperspectral images of each fruit were taken using a hyperspectral system based on two liquid crystal tuneable filters, sensitive in the spectral range 420-1080 nm. Partial Least Square-Discriminant Analysis (PLS-DA) was used on the hyperspectral images to select optimal wavelengths and classify persimmon fruits by maturity. The results achieved 90.1% of correct classification using six selected wavelengths. Additionally, flesh firmness was predicted by using partial least square regression (PLS-R) and the selected wavelengths. A R2 of 0.80 and a square error of prediction (SEP) of 4.34 N were obtained. All of these results were considered as good for a non-invasive maturity assessment technique of ‘Rojo Brillante’ persimmon. 2021-09-02T10:06:03Z 2021-09-02T10:06:03Z 2016 conferenceObject Munera, S., Blasco, J., Cubero, S., Besada, C., Salvador, A., Talens, P., & Aleixos, N. (2016). Maturity assessment of'Rojo Brillante'persimmon by hyperspectral imaging. In: CIGR-AgEng Conference, 1-7. http://hdl.handle.net/20.500.11939/7586 https://conferences.au.dk/uploads/tx_powermail/2016cigr_ageng_full_paper_smp.pdf es 2016-06-26 4th CIGR International Conference of Agricultural Engineering (CIGR-AgEng2016). Aarhus, Denmark This work has been partially funded by the Instituto Nacional de Investigación y Tecnología Agraria y Alimentaria de España (INIA) through research projects RTA2012-00062-C04-01, RTA2012-00062-C04-03 and RTA2013-00043-C02 with the support of European FEDER funds and by the Conselleria d' Educació, Investigació, Cultura i Esport, Generalitat Valenciana, through the project AICO/2015/122. Atribución-NoComercial-SinDerivadas 3.0 España http://creativecommons.org/licenses/by-nc-nd/3.0/es/ openAccess electronico |
| spellingShingle | Internal quality Q01 Food science and technology N01 Agricultural engineering J10 Handling, transport, storage and protection of agricultural products Fruit Classification Prediction Computer vision Munera, Sandra Blasco, José Cubero, Sergio Besada, Cristina Salvador, Alejandra Talens, Pau Aleixos, Nuria Maturity assessment of ‘Rojo Brillante’ persimmon by Hyperspectral Imaging |
| title | Maturity assessment of ‘Rojo Brillante’ persimmon by Hyperspectral Imaging |
| title_full | Maturity assessment of ‘Rojo Brillante’ persimmon by Hyperspectral Imaging |
| title_fullStr | Maturity assessment of ‘Rojo Brillante’ persimmon by Hyperspectral Imaging |
| title_full_unstemmed | Maturity assessment of ‘Rojo Brillante’ persimmon by Hyperspectral Imaging |
| title_short | Maturity assessment of ‘Rojo Brillante’ persimmon by Hyperspectral Imaging |
| title_sort | maturity assessment of rojo brillante persimmon by hyperspectral imaging |
| topic | Internal quality Q01 Food science and technology N01 Agricultural engineering J10 Handling, transport, storage and protection of agricultural products Fruit Classification Prediction Computer vision |
| url | http://hdl.handle.net/20.500.11939/7586 https://conferences.au.dk/uploads/tx_powermail/2016cigr_ageng_full_paper_smp.pdf |
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