In-Line estimation of the standard colour index of citrus fruits using a computer vision system developed for a mobile platform

The final publication is available at Springer via http://dx.doi.org/10.1007/s11947-012-1015-2A key aspect for the consumer when it comes to deciding on a particular product is the colour. In order to make fruit available to consumers as early as possible, the collection of oranges and mandarins begins before they ripen fully and reach their typical orange colour. As a result, they are therefore subjected to certain degreening treatments, depending on their standard colour citrus index at harvest. Recently, a mobile platform that incorporates a computer vision system capable of pre-sorting the fruit while it is being harvested has been developed as an aid in the harvesting task. However, due to the restrictions of working in the field, the computer vision system developed for this machine is limited in its technology and processing capacity compared to conventional systems. This work shows the optimised algorithms for estimating the colour of citrus in-line that were developed for this mobile platform and its performance is evaluated against that of a spectrophotometer used as a reference in the analysis of colour in food. The results obtained prove that our analysis system predicts the colour index of citrus with a good reliability (R2 = 0.925) working in real time. Findings also show that it is effective for classifying harvested fruits in the field according to their colour. © 2012 Springer Science+Business Media New York.This work was partially funded by the INIA through research project RTA2009-00118-C02-01 with the support of European FEDER funds, and by the project PAID-05-11-2745, Vicerectorat d'Investigacio, Universitat Politecnica de Valencia.Vidal, A.; Talens Oliag, P.; Prats-Montalbán, JM.; Cubero García, S.; Albert Gil, FE.; Blasco Ivars, J. (2013). 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. 6(12):3412-3419. https://doi.org/10.1007/s11947-012-1015-2S34123419612Arzate-Vázquez, I., Chanona-Pérez, J. J., Perea-Flores, M. J., Calderón-Domínguez, G., Moreno-Armendáriz, M. A., Calvo, H., Godoy-Calderón, S., Quevedo, R., & Gutiérrez-López, G. (2011). Image processing applied to classification of avocado variety Hass (Persea americana Mill.) during the ripening process. Food and Bioprocess Technology, 4(7), 1307–1313.Blasco, J., Aleixos, N., Cubero, S., Gómez-Sanchis, J., & Moltó, E. (2009). Automatic sorting of satsuma (Citrus unshiu) segments using computer vision and morphological features. Computers and Electronics in Agriculture, 66, 1–8.Campbell, B. L., Nelson, R. G., Ebel, C. E., Dozier, W. A., Adrian, J. L., & Hockema, B. R. (2004). Fruit quality characteristics that affect consumer preferences for satsuma mandarins. HortScience, 39(7), 1664–1669.Cavazza, A., Corradini, C., Rinaldi, M., Salvadeo, P., Borromei, C., & Massini, R. (2012). Evaluation of pasta thermal treatment by determination of carbohydrates, furosine, and color indices. Food and Bioprocess Technology. doi: 10.1007/s11947-012-0906-6 . In-press.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.Cubero, S., Moltó, E., Gutiérrez, A., Aleixos, N., García-Navarrete, O. L., Juste, F., & Blasco, J. (2010). Real-time inspection of fruit on a mobile harvesting platform in field conditions using computer vision. Progress in Agricultural Engineering Science, 6, 1–16.Díaz, R., Faus, G., Blasco, M., Blasco, J., & Moltó, E. (2000). The application of a fast algorithm for the classification of olives by machine vision. Food Research International, 33, 305–309.DOGV (2006) Diari Oficial de la Comunitat Valenciana, 5346, 30321-30328.Gardner, J. L. (2007). Comparison of calibration methods for tristimulus colorimeters. Journal of Research of the National Institute of Standards and Technology, 112, 129–138.Hashim, N., Janius, R. B., Baranyai, L., Rahman, R. A., Osman, A., & Zude, M. (2011). Kinetic model for colour changes in bananas during the appearance of chilling injury symptoms. Food and Bioprocess Technology, 5(8), 2952–2963.HunterLab (2008): Applications note, 8(9), http://www.hunterlab.com/appnotes/an08_96a.pdf . Accessed September 2012.Hutchings, J. B., Luo, R., & Ji, W. (2002). Calibrated colour imaging analysis of food. In D. MacDougall (Ed.), Colour in Food (pp. 352–366). Cambridge: Woodhead Publishing.Jiménez-Cuesta MJ, Cuquerella J & Martínez-Jávega JM (1981) Determination of a color index for citrus fruit degreening. In Proc. of the International Society of Citriculture, Vol. 2, 750-753Kang, S. P., East, A. R., & Trujillo, F. J. (2008). Colour vision system evaluation of bicolour fruit: A case study with ‘B74’ mango. Postharvest Biology and Technology, 49, 77–85.Lang, C., & Hübert, T. (2011). A colour ripeness indicator for apples. Food and Bioprocess Technology, 5(8), 3244–3249.López-Camelo, A. F., & Gómez, P. A. (2004). Comparison of color indexes for tomato ripening. Horticultura Brasileira, 22(3), 534–537. http://www.scielo.br/scielo.php?script=sci_arttext&pid=S0102-05362004000300006 .López-García, F., Andreu-García, A., Blasco, J., Aleixos, N., & Valiente, J. M. (2010). Automatic detection of skin defects in citrus fruits using a multivariate image analysis approach. Computers and Electronics in Agriculture, 71, 189–197.Lorente, D., Aleixos, N., Gómez-Sanchis, J., Cubero, S., García-Navarrete, O. L., & Blasco, J. (2012). Recent advances and applications of hyperspectral imaging for fruit and vegetable quality assessment. Food and Bioprocess Technology, 5(4), 1121–1142.Mendoza, F., Dejmek, P., & Aguilera, J. M. (2006). Calibrated color measurements of agricultural foods using image analysis. Postharvest Biology and Technology, 41, 285–295.Montgomery, D. C. (2005). Design and analysis of experiments, 6th ed. Tempe: Wiley.Noboru, O., & Robertson, A. R. (2005). Colorimetry. West Sussex: Wiley.Pathare, P. B., Opara, U. L., & Al-Said, F. A. (2012). Colour measurement and analysis in fresh and processed foods: a review. Food and Bioprocess Technology. doi: 10.1007/s11947-012-0867-9 . In-press.Quevedo, R. A., Aguilera, J. M., & Pedreschi, F. (2010). Colour of salmon fillets by computer vision and sensory panel. Food and Bioprocess Technology, 3, 637–643.Sahin, S., & Sumnu, S. G. (2006). Physical properties of foods. New York: Springer.Quevedo, R., Valencia, E., Alvarado, F., Ronceros, B., & Bastias, J. M. (2011). Comparison of whiteness index vs. fractal Fourier in the determination of bloom chocolate using image analysis. Food and Bioprocess Technology. doi: 10.1007/s11947-011-0729-x . In-press.Smith, T., & Guild, J. (1931). The C.I.E. colorimetric standards and their use. Transactions of the Optical Society, 33(3), 73–134.Yam, K. L., & Papadakis, S. E. (2004). A simple digital imaging method for measuring and analyzing color of food surfaces. Journal of Food Engineering, 61, 137–142