Potential of VIS-NIR hyperspectral imaging and chemometric methods to identify similar cultivars of nectarine
Product inspection is essential to ensure good quality and to avoid fraud. New nectarine cultivars with similar external appearance but different physicochemical properties may be mixed in the market, causing confusion and rejection among consumers, and consequently affecting sales and prices. Hyper...
| Autores principales: | , , , , , |
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| Formato: | acceptedVersion |
| Lenguaje: | Inglés |
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Elsevier
2017
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| Materias: | |
| Acceso en línea: | http://hdl.handle.net/20.500.11939/5740 http://www.sciencedirect.com/science/article/pii/S0956713517305224?via%3Dihub |
| _version_ | 1855032326655836160 |
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| author | Munera, Sandra Amigo, José M. Aleixos, Nuria Talens, Pau Cubero, Sergio Blasco, José |
| author_browse | Aleixos, Nuria Amigo, José M. Blasco, José Cubero, Sergio Munera, Sandra Talens, Pau |
| author_facet | Munera, Sandra Amigo, José M. Aleixos, Nuria Talens, Pau Cubero, Sergio Blasco, José |
| author_sort | Munera, Sandra |
| collection | ReDivia |
| description | Product inspection is essential to ensure good quality and to avoid fraud. New nectarine cultivars with similar external appearance but different physicochemical properties may be mixed in the market, causing confusion and rejection among consumers, and consequently affecting sales and prices. Hyperspectral reflectance imaging in the range of 450–1040 nm was studied as a non-destructive method to differentiate two cultivars of nectarines with a very similar appearance but different taste. Partial least squares discriminant analysis (PLS-DA) was used to develop a prediction model to distinguish intact fruits of the cultivars using pixel-wise and mean spectrum approaches, and then the model was projected onto the complete surface of fruits allowing visual inspection. The results indicated that mean spectrum of the fruit was the most accurate method, a correct discrimination rate of 94% being achieved. Wavelength selection reduced the dimensionality of the hyperspectral images using the regression coefficients of the PLS-DA model. An accuracy of 96% was obtained by using 14 optimal wavelengths, whereas colour imaging and a trained inspection panel achieved a rate of correct classification of only 57% of the fruits. |
| format | acceptedVersion |
| id | ReDivia5740 |
| institution | Instituto Valenciano de Investigaciones Agrarias (IVIA) |
| language | Inglés |
| publishDate | 2017 |
| publishDateRange | 2017 |
| publishDateSort | 2017 |
| publisher | Elsevier |
| publisherStr | Elsevier |
| record_format | dspace |
| spelling | ReDivia57402025-04-25T14:45:54Z Potential of VIS-NIR hyperspectral imaging and chemometric methods to identify similar cultivars of nectarine Munera, Sandra Amigo, José M. Aleixos, Nuria Talens, Pau Cubero, Sergio Blasco, José Stone fruit Quality control Cultivar discrimination Non-destructive PLS-DA Colour analysis Hyperspectral image Q01 Food science and technology Stone fruits Cultivars Quality controls Image processing Product inspection is essential to ensure good quality and to avoid fraud. New nectarine cultivars with similar external appearance but different physicochemical properties may be mixed in the market, causing confusion and rejection among consumers, and consequently affecting sales and prices. Hyperspectral reflectance imaging in the range of 450–1040 nm was studied as a non-destructive method to differentiate two cultivars of nectarines with a very similar appearance but different taste. Partial least squares discriminant analysis (PLS-DA) was used to develop a prediction model to distinguish intact fruits of the cultivars using pixel-wise and mean spectrum approaches, and then the model was projected onto the complete surface of fruits allowing visual inspection. The results indicated that mean spectrum of the fruit was the most accurate method, a correct discrimination rate of 94% being achieved. Wavelength selection reduced the dimensionality of the hyperspectral images using the regression coefficients of the PLS-DA model. An accuracy of 96% was obtained by using 14 optimal wavelengths, whereas colour imaging and a trained inspection panel achieved a rate of correct classification of only 57% of the fruits. 2017-12-12T15:45:38Z 2017-12-12T15:45:38Z 2018 acceptedVersion Munera S, Amigo J.M., Aleixos N, Talens P, Cubero S, Blasco J (2018). Potential of VIS-NIR hyperspectral imaging and chemometric methods to identify similar cultivars of nectarine. Food Control, 86, 1-10. 0956-7135 http://hdl.handle.net/20.500.11939/5740 10.1016/j.foodcont.2017.10.037 http://www.sciencedirect.com/science/article/pii/S0956713517305224?via%3Dihub en info:eu-repo/grantAgreement/MINECO/Programa estatal de i+D+i Orientada a los Retos de la Sociedad/RTA2015-00078-00-00//Sistemas no destructivos para la determinación automática de la calidad interna de frutas en línea utilizando métodos ópticos e información espectral Sistemas no destructivos para la determinación automática de la calidad interna de frutas en línea utilizando métodos ópticos e información espectral INIA (RTA2015-00078-00-00) Sistemas de sensado de interés agrario. Incorporación de tecnologías ópticas para evaluar la cantidad y calidad de la cosecha (IVIA 51431) Elsevier impreso |
| spellingShingle | Stone fruit Quality control Cultivar discrimination Non-destructive PLS-DA Colour analysis Hyperspectral image Q01 Food science and technology Stone fruits Cultivars Quality controls Image processing Munera, Sandra Amigo, José M. Aleixos, Nuria Talens, Pau Cubero, Sergio Blasco, José Potential of VIS-NIR hyperspectral imaging and chemometric methods to identify similar cultivars of nectarine |
| title | Potential of VIS-NIR hyperspectral imaging and chemometric methods to identify similar cultivars of nectarine |
| title_full | Potential of VIS-NIR hyperspectral imaging and chemometric methods to identify similar cultivars of nectarine |
| title_fullStr | Potential of VIS-NIR hyperspectral imaging and chemometric methods to identify similar cultivars of nectarine |
| title_full_unstemmed | Potential of VIS-NIR hyperspectral imaging and chemometric methods to identify similar cultivars of nectarine |
| title_short | Potential of VIS-NIR hyperspectral imaging and chemometric methods to identify similar cultivars of nectarine |
| title_sort | potential of vis nir hyperspectral imaging and chemometric methods to identify similar cultivars of nectarine |
| topic | Stone fruit Quality control Cultivar discrimination Non-destructive PLS-DA Colour analysis Hyperspectral image Q01 Food science and technology Stone fruits Cultivars Quality controls Image processing |
| url | http://hdl.handle.net/20.500.11939/5740 http://www.sciencedirect.com/science/article/pii/S0956713517305224?via%3Dihub |
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