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dc.contributor.author | Gomez-Sanchis, J. | es_ES |
dc.contributor.author | Lorente, D. | es_ES |
dc.contributor.author | Soria Olivas, Emilio | es_ES |
dc.contributor.author | Aleixos Borrás, María Nuria | es_ES |
dc.contributor.author | Cubero, S. | es_ES |
dc.contributor.author | Blasco, J. | es_ES |
dc.date.accessioned | 2016-07-22T09:58:22Z | |
dc.date.available | 2016-07-22T09:58:22Z | |
dc.date.issued | 2013-04 | |
dc.identifier.issn | 1935-5130 | |
dc.identifier.uri | http://hdl.handle.net/10251/68020 | |
dc.description.abstract | Hyperspectral systems are characterised by offering the possibility of acquiring a large number of images at different consecutive wavebands. To ensure reliable and repeatable results using this kind of optical sensors, the intensity shown by the objects in the different spectral images must be independent from the differences in sensitivity of the system for the different wavelengths. The spectral efficiency of the acquisition devices and the spectral emission of the lighting system vary across the spectrum and the images, and therefore the results can reproduce these variations if the system is not properly calibrated and corrected. This is particularly complex, when several LCTF devices are used to obtain large spectral ranges. This work presents the development of a hyperspectral system based on two liquid crystal tuneable filters for the acquisition of images of spherical fruits. It also proposes a methodology for acquiring and segmenting images of citrus fruits aimed at detecting decay in citrus fruits that has been capable of correctly classifying 98 % of pixels as rotten or non-rotten and 95 % of fruit. | es_ES |
dc.description.sponsorship | This work has been partially funded by the Instituto Nacional de Investigacion y Tecnologia Agraria y Alimentaria de Espana (INIA) through research project RTA2012-00062-C04-01 and RTA2012-00062-C04-03 with the support of European FEDER funds, the Universitat de Valencia through project UV-INV-AE11-41271, and the UPV-IVIA through collaboration agreement UPV-2013000005. | en_EN |
dc.language | Inglés | es_ES |
dc.publisher | Springer Verlag | es_ES |
dc.relation.ispartof | Food and Bioprocess Technology | es_ES |
dc.rights | Reserva de todos los derechos | es_ES |
dc.subject | Hyperspectral | es_ES |
dc.subject | Citrus fruits | es_ES |
dc.subject | Decay detection | es_ES |
dc.subject | Fruit inspection | es_ES |
dc.subject | Artificial neural networks | es_ES |
dc.subject.classification | EXPRESION GRAFICA EN LA INGENIERIA | es_ES |
dc.title | Development of a hyperspectral computer vision system based on two liquid crystal tuneable filters for fruit inspection. Application to detect citrus fruits decay | es_ES |
dc.type | Artículo | es_ES |
dc.identifier.doi | 10.1007/s11947-013-1158-9 | |
dc.relation.projectID | info:eu-repo/grantAgreement/MINECO//RTA2012-00062-C04-01/ES/Nuevas técnicas de inspección basadas en espectrometría para la estimación de propiedades y determinación automática de la calidad interna y sanidad de productos agroalimentarios aplicadas a líneas de inspección y manipulación (SPEC-DACSA)/ / | es_ES |
dc.relation.projectID | info:eu-repo/grantAgreement/UV//UV-INV-AE11-41271/ | es_ES |
dc.relation.projectID | info:eu-repo/grantAgreement/UPV//IVIA%2FUPV-2013000005/ | es_ES |
dc.relation.projectID | info:eu-repo/grantAgreement/MINECO//RTA2012-00062-C04-03/ES/Nuevas técnicas de inspección basadas en visión por computador multiespectral para la estimación de propiedades y determinación automática de la calidad y sanidad de la producción agroalimentaria en líneas de inspección y manipulación (VIS-DACSA)/ | es_ES |
dc.rights.accessRights | Abierto | es_ES |
dc.contributor.affiliation | Universitat Politècnica de València. Departamento de Ingeniería Gráfica - Departament d'Enginyeria Gràfica | es_ES |
dc.contributor.affiliation | Universitat Politècnica de València. Instituto Interuniversitario de Investigación en Bioingeniería y Tecnología Orientada al Ser Humano - Institut Interuniversitari d'Investigació en Bioenginyeria i Tecnologia Orientada a l'Ésser Humà | es_ES |
dc.description.bibliographicCitation | Gomez-Sanchis, J.; Lorente, D.; Soria Olivas, E.; Aleixos Borrás, MN.; Cubero, S.; Blasco, J. (2013). Development of a hyperspectral computer vision system based on two liquid crystal tuneable filters for fruit inspection. Application to detect citrus fruits decay. Food and Bioprocess Technology. 7(4):1047-1056. https://doi.org/10.1007/s11947-013-1158-9 | es_ES |
dc.description.accrualMethod | S | es_ES |
dc.relation.publisherversion | http://dx.doi.org/10.1007/s11947-013-1158-9 | es_ES |
dc.description.upvformatpinicio | 1047 | es_ES |
dc.description.upvformatpfin | 1056 | es_ES |
dc.type.version | info:eu-repo/semantics/publishedVersion | es_ES |
dc.description.volume | 7 | es_ES |
dc.description.issue | 4 | es_ES |
dc.relation.senia | 246263 | es_ES |
dc.identifier.eissn | 1935-5149 | |
dc.contributor.funder | Ministerio de Economía y Competitividad | es_ES |
dc.contributor.funder | Universitat de València | es_ES |
dc.contributor.funder | Universitat Politècnica de València | es_ES |
dc.contributor.funder | European Regional Development Fund | es_ES |
dc.description.references | Aleixos, N., Blasco, J., Navarrón, F., & Moltó, E. (2002). Multispectral inspection of citrus in real-time using machine vision and digital signal processors. Computers and Electronics in Agriculture, 33(2), 121–137. | es_ES |
dc.description.references | Ando, F. (1990). Multi-functional solid state imaging techniques. Journal of the Institute of Television Engineering, 44(2), 127–131. | es_ES |
dc.description.references | Bei, L., Dennis, G. I., Miller, H. M., Spaine, T. W., & Carnahan, J. W. (2004). Acousto-optic tunable filters: fundamentals and applications as applied to chemical analysis techniques. Progress in Quantum Electronics, 28(2), 67–87. | es_ES |
dc.description.references | Blasco, J., Aleixos, N., Gómez-Sanchis, J., & Moltó, E. (2009). Recognition and classification of external skin damage in citrus fruits using multispectral data and morphological features. Biosystems Engineering, 103(2), 137–145. | es_ES |
dc.description.references | Cubero, S., Aleixos, N., Moltó, E., Gómez-Sanchis, J., & Blasco, J. (2011). Advances in machine vision applications for automatic inspection and quality evaluation of fruits and vegetables. Food and Bioprocess Technology, 4(4), 487–504. | es_ES |
dc.description.references | Eckert, J., & Eaks, I. (1989). Postharvest disorders and diseases of citrus. CA, USA: The citrus industry, University California Press. | es_ES |
dc.description.references | ElMasry, G., Wang, N., Vigneault, C., Qiao, J., & ElSayed, A. (2008). Early detection of apple bruises on different background colours using hyperspectral imaging. LWT- Food Science and Technology, 41(2), 337–345. | es_ES |
dc.description.references | Erives, H., & Fitzgerald, G. J. (2005). Automated registration of hyperspectral images for precision agriculture. Computers and Electronics in Agriculture, 47(2), 103–119. | es_ES |
dc.description.references | Geladi, P. L. M. (2007). Calibration standards and image calibration. In H. F. Grahn & P. Geladi (Eds.), Techniques and applications of hyperspectral image analysis, pp 203–220. Chichester, England: John Wiley & Sons. | es_ES |
dc.description.references | Gómez-Sanchis, J., Gómez-Chova, L., Aleixos, N., Camps-Valls, G., Montesino-Herrero, C., Moltó, E., et al. (2008a). Hyperspectral system for early detection of rottenness caused by Penicillium digitatum in mandarins. Journal of Food Engineering, 89(1), 80–86. | es_ES |
dc.description.references | Gómez-Sanchis, J., Moltó, E., Camps-Valls, G., Gómez-Chova, L., Aleixos, N., & Blasco, J. (2008b). Automatic correction of the effects of the light source on spherical objects. An application to the analysis of hyperspectral images of citrus fruits. Journal of Food Engineering, 85(2), 191–200. | es_ES |
dc.description.references | Gómez-Sanchis, J., Martín-Guerrero, J. D., Soria-Olivas, E., Martínez-Sober, M., Magdalena-Benedito, R., & Blasco, J. (2012). Detecting rottenness caused by Penicillium in citrus fruits using machine learning techniques. Expert Systems with Applications, 39(1), 780–785. | es_ES |
dc.description.references | Hecht, E. (2003). Optics (4th ed.). Reading, USA: Addison Wesley. | es_ES |
dc.description.references | Karoui, R., & Blecker, C. (2011). Fluorescence spectroscopy measurement for quality assessment of food systems—a review. Food and Bioprocess Technology, 4(3), 364–386. | es_ES |
dc.description.references | Kim, D. G., Burks, T. F., Qin, J., & Bulanon, M. D. (2009). Classification of grapefruit peel diseases using colour texture feature analysis. International Journal of Agricultural and Biological Engineering, 2(3), 41–50. | es_ES |
dc.description.references | Kokawa, M., Sugiyama, J., Tsuta, M., Yoshimura, M., Fujita, K., Shibata, M., Araki, T., & Nabetani, H. (2012). Development of a quantitative visualisation technique for gluten in dough using fluorescence fingerprint imaging. Food and Bioprocess Technology. DOI 10.1007/s1947-012-0982-7 (In press) | es_ES |
dc.description.references | López-Álvarez, M., Hernández-Andrés, J., Romero, J., Campos, J., & Pons, A. (2009). Calibrating the elements of a multispectral imaging system. Journal of Imaging Science and Technology, 53(3), 31102-1-31102-10. | es_ES |
dc.description.references | Lorente, D., Blasco, J., Serrano, A. J., Soria-Olivas, E., Aleixos, N., & Gómez-Sanchis, J. (2012b). Comparison of ROC feature selection method for the detection of decay in citrus fruit using hyperspectral images. Food and Bioprocess Technology. DOI: 10.1007/s11947-012-0951-1 (In press). | es_ES |
dc.description.references | Lorente, D., Aleixos, N., Gómez-Sanchis, J., Cubero, S., García-Navarrete, O. L., & Blasco, J. (2012b). Recent advances and applications of hyperspectral imaging for fruit and vegetable quality assessment. Food and Bioprocess Technology, 5(4), 1121–1142. | es_ES |
dc.description.references | Lorente, D., Aleixos, N., Gómez-Sanchis, J., Cubero, S., & Blasco, J. (2013). Selection of optimal wavelength features for decay detection in citrus fruit using the ROC curve and neural networks. Food and Bioprocess Technology, 6(2), 530–541. | es_ES |
dc.description.references | Magwaza, L. S., Opara, U. L., Nieuwoudt, H., Cronje, P. J. R., Saeys, W., & Nicolaï, B. (2011). NIR Spectroscopy applications for internal and external quality analysis of citrus fruit—a review. Food and Bioprocess Technology, 5(2), 425–424. | es_ES |
dc.description.references | Menesatti, P., Zanella, A., D’Andrea, S., Costa, C., Paglia, G., & Pallottino, F. (2009). Supervised multivariate analysis of hyper-spectral NIR images to evaluate the starch index of apples. Food and Bioprocess Technology, 2(3), 308–314. | es_ES |
dc.description.references | Moltó, E., Blasco, J., & Gómez-Sanchis, J. (2010). Analysis of hyperspectral images of citrus fruits. In D.-W. Sun (Ed.), Hyperspectral Imaging for food quality analysis and control (pp. 321–348). San Diego, California, USA: Academic Press/Elsevier. | es_ES |
dc.description.references | Palou, L., Smilanick, J. L., & Droby, S. (2008). Alternatives to conventional fungicides for the control of citrus postharvest green and blue moulds. Stewart Postharvest Review, 4(2), 1–16. | es_ES |
dc.description.references | Pang, Z., Laplante, N. E., & Filkins, R. J. (2012). Dark pixel intensity determination and its applications in normalising different exposure time and autofluorescence removal. Journal of Microscopy, 246(1), 1–10. | es_ES |
dc.description.references | Pathare, P. B., Opara, U. L., & Al-Said, F. A. (2013). Colour measurement and analysis in fresh and processed foods: a review. Food and Bioprocess Technology, 6(1), 36–60. | es_ES |
dc.description.references | Peng, Y., & Lu, R. (2006). An LCTF-based multispectral imaging system for estimation of apple fruit firmness; part I. acquisition and characterization of scattering images. Transactions of ASAE, 49(1), 259–267. | es_ES |
dc.description.references | Qin, J., Burksa, T., Ritenourb, M., & Bonn, W. (2009). Detection of citrus canker using hyperspectral reflectance imaging with spectral information divergence. Journal of Food Engineering, 93(2), 183–191. | es_ES |
dc.description.references | Qin, J., Burks, T. F., Zhao, X., Niphadkar, N., & Ritenour, M. A. (2012). Development of a two-band spectral imaging system for real-time citrus canker detection. Journal of Food Engineering, 108(1), 87–93. | es_ES |
dc.description.references | Sun, D.-W. (Ed.). (2009). Infrared spectroscopy for food quality analysis and control. San Diego, California, USA: Academic Press/Elsevier. | es_ES |
dc.description.references | Sun, D.-W. (Ed.). (2010). Hyperspectral imaging for food quality analysis and control. San Diego, California, USA: Academic Press/Elsevier. | es_ES |
dc.description.references | Vadivambal, R., & Jayas, D. S. (2011). Applications of thermal imaging in agriculture and food industry—a review. Food and Bioprocess Technology, 4(2), 186–199. | es_ES |
dc.description.references | Valencia-Chamorro, S. A., Palou, L., del Río, M. A., & Pérez-Gago, M. B. (2011). Performance of hydroxypropyl methylcellulose (HPMC)-lipid edible coatings with antifungal food additives during cold storage of ‘Clemenules’ mandarins. LWT- Food Science and Technology, 44(10), 2342–2348. | es_ES |
dc.description.references | Vélez-Rivera, N., Blasco, J., Chanona-Pérez, J. J., Calderón-Domínguez, G., Perea-Flores, M. J., Arzate-Vázquez, I., Cubero, S., & Farrera-Rebollo, R. (2013). Computer vision system applied to classification of ‘Manila’ mangoes during ripening process. Food and Bioprocess Technology. DOI: 10.1007/s11947-013-1142-4 (In press). | es_ES |
dc.description.references | Vidal, A., Talens, P., Prats-Montalbán, J. M., Cubero, S., Albert, F., & Blasco, J. (2012). In-line estimation of the standard colour index of citrus fruits using a computer vision system developed for a mobile platform. Food and Bioprocess Technology. DOI: 10.1007/s11947-012-1015-2 (In press). | es_ES |
dc.description.references | Vila-Francés, J., Calpe-Maravilla, J., Gómez-Chova, L., & Amorós-López, J. (2010). Analysis of acousto-optic tunable filter performance for imaging applications. Optical Engineering, 49(11), 113203. | es_ES |
dc.description.references | Vila-Francés, J., Calpe-Maravilla, J., Gómez-Chova, L., & Amorós-López, J. (2011). Design of a configurable multispectral imaging system based on an AOTF. IEEE Transactions on Ultrasonics, Ferroelectrics, and Frequency Control, 58(1), 259–262. | es_ES |
dc.description.references | Wang, W., Li, C., Tollner, E. W., Rains, G. C., & Gitaitis, R. D. (2012). A liquid crystal tunable filter based shortwave infrared spectral imaging system: calibration and characterization. Computers and Electronics in Agriculture, 80, 145–154. | es_ES |
dc.description.references | Wu, D., Wang, S., Wang, N., Nie, P., He, Y., Sun, D. -W., & Yao, J. (2012). Application of time series hyperspectral imaging (TS-HSI) for determining water distribution within beef and spectral kinetic analysis during dehydration. Food and Bioprocess Technology. DOI 10.1007/s11947-012-0928-0 (In press). | es_ES |
dc.description.references | Zhu, F., Zhang, D., He, Y., Liu, F., & Sun, D. -W. (2012). Application of visible and near infrared hyperspectral imaging to differentiate between fresh and frozen–thawed fish fillets. Food and Bioprocess Technology. DOI 10.1007/s11947-012-0825-6 (In press). | es_ES |