VIS/NIR hyperspectral imaging and N-way PLS-DA models for detection of decay lesions in citrus fruits

[EN] In this work an N-way partial least squares regression discriminant analysis (NPLS-DA) methodology is developed to detect symptoms of disease caused by Penicillium digitatum in citrus fruits (green mould) using visible/near infrared (VIS/NIR) hyperspectral images. To build the discriminant model a set of oranges and mandarins was infected by the fungus and another set was infiltrated just with water for control purposes. A double cross-validation strategy is used to validate the discriminant models. Finally, permutation testing is used to select a few bands offering the best correct classification rates in the validation set. The discriminant models developed here can be potentially implemented in a fruit packinghouse to detect infected citrus fruits at their arrival from the field with affordable multispectral (3 5 channels) cameras installed in the packinglines.This research was partially funded by the Spanish Ministry of Science and Innovation through grants DPI2011-28112-C04-02 and DPI2014-55276-C05-1R, and by INIA through grant RTA2012-00062-C04-01. In all cases with the support of European FEDER funds. Authors thank Lluis Palou from the Centro de Tecnologia Postcosecha at the IVIA for the help and supervision in the innoculation process of the fruits.Folch Fortuny, A.; Prats-Montalbán, JM.; Cubero-García, S.; Blasco Ivars, J.; Ferrer, A. (2016). VIS/NIR hyperspectral imaging and N-way PLS-DA models for detection of decay lesions in citrus fruits. Chemometrics and Intelligent Laboratory Systems. 156:241-248. https://doi.org/10.1016/j.chemolab.2016.05.005S24124815

Comparison of latent variable-based and artificial intelligence methods for impurity detection in PET recycling from NIR hyperspectral images

[EN] In polyethylene terephthalate's (PET)'s recycling processes, separation from polyvinyl chloride (PVC) is of prior relevance due to its toxicity, which degrades the final quality of recycled PET. Moreover, the potential presence of some polymers in mixed plastics (such as PVC in PET) is a key aspect for the use of recycled plastic in products such as medical equipment, toys, or food packaging. Many works have dealt with plastic classification by hyperspectral imaging, although only some of them have been directly focused on PET sorting and very few on its separation from PVC. These works use different classification models and preprocessing techniques and show their performance for the problem at hand. However, still, there is a lack of methodology to address the goal of comparing and finding the best model and preprocessing technique. Thus, this paper presents a design of experiments-based methodology for comparing and selecting, for the problem at hand, the best preprocessing technique, and the best latent variable-based and/or artificial intelligence classification method, when using NIR hyperspectral images. There is a lack of methodology to address the goal of comparing and finding the best model and preprocessing technique. Thus, this paper presents a design of experiments-based methodology for comparing and selecting, for the problem at hand, the best preprocessing technique, and the best latent variable-based and/or artificial intelligence classification method when using near-infrared hyperspectral images.Universitat Politecnica de Valencia, Grant/Award Number: UPV-FE-16-B18This research was partially supported by the Universitat Politècnica de València under the project UPV‐FE‐16‐B18.Galdón-Navarro, B.; Prats-Montalbán, JM.; Cubero-García, S.; Blasco Ivars, J.; Ferrer, A. (2018). Comparison of latent variable-based and artificial intelligence methods for impurity detection in PET recycling from NIR hyperspectral images. Journal of Chemometrics. 32(1):1-14. https://doi.org/10.1002/cem.2980S11432

Visible and near-infrared diffuse reflectance spectroscopy for fast qualitative and quantitative assessment of nectarine quality

[EN] Visible and near-infrared spectroscopy has been widely used as a non-invasive and rapid-assessment technique for the quality control of agricultural products. In this study, 325 samples of nectarines representing two commercial varieties, cv. 'Big Top' and cv. 'Magique', were analysed by visible and near-infrared diffuse reflectance spectroscopy (VIS-NIR). The spectral data were pre-treated and analysed to predict the internal quality of the samples and to discriminate between the two varieties. Good prediction of the internal quality of the samples, using partial least-squares regressions, was observed for both (R (2) (P) of 0.909 and 0.927 and RMSEP of 0.235 and 0.238 for cv. Big Top and Magique, respectively). Discriminant models, using linear discriminant and partial least-squares discriminant analyses, were built to classify the nectarines. Both methods provided good results with rates of 97.44 and 100% of correctly classified samples. The results indicated that visible and near-infrared techniques can be useful and simple methods for quality control and for the correct identification of nectarines in commercial lines as an alternative to the slower and less accurate manual classification.This work was partially funded by the Generalitat Valenciana through project AICO/2015/122 and by the INIA and FEDER funds through projects RTA2012-00062-C04-01 and 03, and RTA2015-00078-00-00. Victoria Lopez Cortes thanks the Spanish Ministry of Education, Culture and Sports for the FPU grant (FPU13/04202). The authors are also grateful to Fruits de Ponent (Lerida) for providing the fruit.Cortes-Lopez, V.; Blasco Ivars, J.; Aleixos Borrás, MN.; Cubero García, S.; Talens Oliag, P. (2017). Visible and near-infrared diffuse reflectance spectroscopy for fast qualitative and quantitative assessment of nectarine quality. Food and Bioprocess Technology. 10(10):1755-1766. https://doi.org/10.1007/s11947-017-1943-yS175517661010Bachion de Santana, F., Caixeta Gontijo, L., Mitsutake, H., Júnior Mazivilla, S., Maria de Souza, L., & Borges Neto, W. (2016). Non-destructive fraud detection in rosehip oil by MIR spectroscopy and chemometrics. Food Chemistry, 209, 228–233.Bakeev, K. A. (2010). Process analytical technology. United Kingdom: Wiley.Bonany, J., Buehler, A., Carbó, J., Codarin, S., Donati, F., Echeverria, G., Egger, S., Guerra, W., Hilaire, C., Höller, I., Iglesias, I., Jesionkowska, K., Konopacka, D., Kruczynska, D., Martinelli, A., Pitiot, C., Sansavini, S., Stehr, R., & Schoorl, F. (2013). Consumer eating quality acceptance of new apple varieties in different European countries. Food Quality and Preference, 30, 250–259.Bruun, S. W., Sondergaard, I., & Jacobsen, S. (2007). Analysis of protein structures and interactions in complex food by near-infrared spectroscopy. 1. Gluten powder. Journal of Agricultural and Food Chemistry, 55, 7234–7243.Carlomagno, G., Capozzo, L., Attolico, G., & Distante, A. (2004). Non-destructive grading of peaches by near-infrared spectrometry. Infrared Physics & Technology, 46, 23–29.Carr, G. L, Chubar, O., Dumas, P. (2005). Multichannel detection with a synchrotron light source: Design and potential. Spectrochemical Analysis Using Multichannel Detectors Analytical Chemistry Series, edited by Bhargava P, Levin I. Chapter 3, (pp. 56–84). Oxford: Wiley-BlackwellCayuela, J. A., & Weiland, C. (2010). Intact orange quality prediction with two portable NIR spectrometers. Postharvest Biology and Technology, 58(2), 113–120.Clareton, M. (2000). Peach and nectarine production in France: trends, consumption and perspectives. Summaries Prunus Breeders Meeting. EMPRABA, Clima Temperado. Pelotas (RS) Brazil, November 29 to December 2000, pp. 83–91Cortés, V., Ortiz, C., Aleixos, N., Blasco, J., Cubero, S., & Talens, P. (2016). A new internal quality index for mango and its prediction by external visible and near infrared reflection spectroscopy. Postharvest Biology and Technology, 118, 148–158.Crisosto, C. H. (2002). How do we increase peach consumption? Proceedings of 5th International Symposium on Peach, ISHS. Acta Horticulturae, 592, 601–605.Crisosto, C., & Crisosto, G. (2005). Relationship between ripe soluble solids concentration (RSSC) and consumer acceptance of high and low acid meeting flesh peach and nectarine (Prunus persica (L.) Batsch) cultivars. Postharvest Biology and Technology, 38, 239–246.Crisosto, C. H., Garner, D., Crisosto, G. M., Wiley, P., & Southwick, S. (1997). Evaluation of the minimum maturity index for new cherry cultivars growing in the San Joaquin Valley. Visalia: California Cherry Growers Association.Crisosto, C. H., Crisosto, G. M., & Ritenour, M. A. (2002). Testing the reliability of skin color as an indicator of quality for early season ‘Brooks’ (Prunus avium L.) cherry. Postharvest Biology and Technology, 24, 147–154.Crisosto, C. H., Crisosto, G. M., & Metheney, P. (2003). Consumer acceptance of ‘Brooks’ and ‘Bing’ cherries is mainly dependent on fruit SSC and visual skin color. Postharvest Biology and Technology, 28, 159–167.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, 487–504.Cunha, L. C., Teixeira, G. H. A., Nardini, V., & Walsh, K. (2016). Quality evaluation of intact açaí and juçara fruit by means of near infrared spectroscopy. Postharvest Biology and Technology, 112, 64–74.Della Cara, R. (2005). In calo i consumi e l’export de pesche e nettarine italiane. Rivista di Frutticoltura, 7–8, 19–20.Downey, G. (1997). Authentication of food and food ingredients by near infrared spectroscopy. Journal of Near Infrared Spectroscopy, 4, 47–61.Eskin, N.A.M. & Hoehn, E. (2013). Fruits and vegetables. Eskin, N.A.M., Shahidi, F. (Eds.), Biochemistry of foods, 3rd edn. Amsterdam, The Netherlands: Elsevier Inc. pp. 49–126.Faber, N. M. (1999). Multivariate sensitivity for the interpretation of the effect of spectral pretreatment methods on near-infrared calibration model predictions. Analytical Chemistry, (71), 557–565.Fang, L., Li, H., Liu, Z., & Xian, X. (2013). Online evaluation of yellow peach quality by visible and near-infrared spectroscopy. Advance Journal of Food Science and Technology, 5(5), 606–612.Ferrer, P., Montesinos, J. L., Valero, F., & Solá, C. (2001). Production of native and recombinant lipases by Candida rugosa. Applied Biochemistry and Biotechnology, 95(3), 221–255.Font, D., Tresanchez, M., Pallejà, T., Teixidó, M., Martinez, D., Moreno, J., & Palacín, J. (2014). An image processing method for in-line nectarine variety verification based on the comparison of skin feature histogram vectors. Computers and Electronics in Agriculture, 102, 112–119.Fu, X., Yibin, Y., Lu, H., Xu, H., & Yu, H. (2007). FT-NIR diffuse reflectance spectroscopy for kiwifruit firmness detection. Sensing and Instrumentation for Food Quality and Safety, 1, 29–35.GenCat: Generalitat de Cataluña. 2013.Technical report 1/2011 and Technical Indicator A2. (accessed 13.05.13).Ghiani, A., Negrini, N., Morgutti, S., Baldin, F., Nocito, F. F., Spinardi, A., Mignani, I., Bassi, D., & Cocucci, M. (2011). Melting of ‘Big Top’ nectarine fruit: some physiological, biochemical, and molecular aspects. Journal of the American Society for Horticultural Science, 136, 61–68.Golic, M., & Walsh, K. B. (2006). Robustness of calibration models based on near infrared spectroscopy for the in-line grading of stonefruit for total soluble solids content. Analytica Chimica Acta, 555(2), 286–291.Gorry, P. A. (1990). General least-squares smoothing and differentiation by the convolution (Savitzky-Golay) method. Analytical Chemistry, 62, 570–573.He, Y., Li, X. L., & Shao, Y. N. (2006). Discrimination of varieties of apple using near infrared spectra based on principal component analysis and artificial neural network model. Spectroscopy and Spectral Analysis, 26, 850–853.Hernández, A., He, Y., & García, A. (2006). Non-destructive measurement of acidity, soluble solids and firmness of Satsuma mandarin using Vis/NIR-spectroscopy techniques. Journal of Food Engineering, 77, 313–319.Huang, H., Yu, H., Xu, H., & Ying, Y. (2008). Near infrared spectroscopy for on/in-line monitoring of quality in foods and beverages: a review. Journal of Food Engineering, 87(3), 303–313.Huang, L., Wu, D., Jin, H., Zhang, J., He, Y., & Lou, C. (2011). Internal quality determination of fruit with bumpy surface using visible and near infrared spectroscopy and chemometrics: a case study with mulberry fruit. Biosystems Engineering, 109(4), 377–384.Iglesias, I. (2013). Peach production in Spain: current situation and trends, from production to consumption. Proceedings of the 4th Conference, Innovation in Fruit Growing, 75–96. D. Milatovic (Ed), Serbia (Belgrad)Iglesias, I., & Echeverría, G. (2009). Differential effect of cultivar and harvest date on nectarine colour, quality and consumer acceptance. Scientia Horticulturae, 120, 41–50.Jaiswal, P., Jha, S. N., & Bharadwaj, R. (2012). Non-destructive prediction of quality of intact banana using spectroscopy. Scientia Horticulturae, 135, 14–22.Kamruzzaman, M., ElMasry, G., Sun, D., & Allen, P. (2012). Non-destructive prediction and visualization of chemical composition in lamb meat using NIR hyperspectral imaging and multivariate regression. Innovate Food Science and Emerging Technologies, 16, 218–226.Kozak, M., & Scaman, C. H. (2008). Unsupervised classification methods in food sciences: discussion and outlook. Journal of the Science of Food and Agriculture, 88, 1115–1127.Lichtenthaler, H.K. & Buschmann, C. (2001). Chlorophylls and carotenoids: measurement and characterization by UV-VIS spectroscopy. Current Protocols in Food Analytical Chemistry, pp. F.4.3.1–F.4.3.8. Wiley, New York.Liu, Y., Chen, X., & Ouyang, A. (2008). Nondestructive determination of pear internal quality indices by visible and near infrared spectrometry. LWT - Food Science and Technology, 41, 1720–1725.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 Bioprocess Technology, 5, 1121–1142.Lorente, D., Escandell-Montero, P., Cubero, S., Gómez-Sanchis, J., & Blasco, J. (2015). Visible-NIR reflectance spectroscopy and manifold learning methods applied to the detection of fungal infections on citrus fruit. Journal of Food Engineering, 163, 17–21.Lu, R. (2004). Multispectral imaging for predicting firmness and soluble solids content of apple fruit. Postharvest Biology and Technology, 31(2), 147–157.Ma, G., Fu, X. P., Zhou, Y., Ying, Y. B., Xu, H. R., Xie, L. J., & Lin, T. (2007). Nondestructive sugar content determination of peaches by using near infrared spectroscopy technique. Guang Pu Xue Yu Guang Pu Fen Xi/Spectroscopy and Spectral Analysis, 27(5), 907–910.Magwaza, L. S., Opara, L. U., Nieuwoudt, H., Cronje, P. J. R., Saeys, W., & Nicolaï, B. (2012). NIR spectroscopy applications for internal and external quality analysis of citrus fruit—a review. Food and Bioprocess Technology, 5, 425–444.Martens, H., Nielsen, J. P., & Engelsen, S. B. (2003). Light scattering and light absorbance separated by extended multiplicative signal correction. Application to near-infrared transmission analysis of powder mixtures. Analytical Chemistry, 75, 394–404.Martins, P. A., Cirino de Carvalho, L., Cunha, L. C., Manhas, F., & Teixeira, G. H. (2016). Robust PLS models for soluble solids content and firmness determination in low chilling peach using near infrared spectroscopy (NIR). Postharvest Biology and Technology, 111, 345–351.McGlone, V. A., & Kawano, S. (1998). Firmness, dry-matter and soluble-solids assessment of post-harvest kiwifruit by NIR spectroscopy. Postharvest Biology and Technology, 13, 131–141.Merzlyak, M. N., Solo, A. E., & Gitelson, A. A. (2003). Reflectance spectral features and non-destructive estimation of chlorophyll, carotenoid and anthocyanin content in apple fruit. Postharvest Biology and Technology, 27, 197–211.Nicolaï, B. M., Beullens, K., Bobelyn, E., Peirs, A., Saeys, W., Theron, I. K., & Lammertyn, J. (2007). Non-destructive measurement of fruit and vegetable quality by means of NIR spectroscopy: a review. Postharvest Biology and Technology, 46, 99–118.Osborne, B. G., Fearn, T., & Hindle, P. H. (1993). Practical NIR spectroscopy with applications in food and beverage analysis (2nd ed.pp. 123–132). Burnt Mill, Harlow, Essex, England: Longman Group.Padilla-Zakour, O. I. (2009). Good manufacturing practices. In N. Heredia, I. Wesley, & S. Garcia (Eds.), Microbiologically safe foods (pp. 395–415). New York: John Wiley and Sons Inc..Peiris, K. H. S., Dull, G. G., Leffler, R. G., & Kays, S. J. (1998). Near-infrared spectrometric method for nondestructive determination of soluble solids content of peaches. Journal of the American Society for Horticultural Science, 123(5), 898–905.Pérez-Marín, D., Sánchez, M. T., Paz, P., González-Dugo, V., & Soriano, M. A. (2011). Postharvest shelf-life discrimination of nectarines produced under different irrigation strategies using NIR-spectroscopy. Food Science and Technology, 44, 1405–1414.Ravaglia, G., Sansavini, S., Ventura, M., & Tabanelli, D. (1996). Indici di maturazione e miglioramento cualitativo delle pesche. Revista di Frutticoltura, 3, 61–66.Reita, G., Peano, C., Saranwong, S., & Kawano, S. (2008). An evaluating technique for variety compatibility of fruit applied to a near infrared Brix calibration system: a case study using Brix calibration for nectarines. Journal of Near Infrared Spectroscopy, 16(2), 83–89.Rodriguez-Campos, J., Escalona-Buendía, H. B., Orozco-Avila, I., Lugo-Cervantes, E., & Jaramillo-Flores, M. E. (2011). Dynamics of volatile and non-volatile compounds in cocoa (Theobroma cacao L.) during fermentation and drying processes using principal components analysis. Food Research International, 44, 250–258.Sádecká, J., Jakubíková, M., Májek, P., & Kleinová, A. (2016). Classification of plum spirit drinks by synchronous fluorescence spectroscopy. Food Chemistry, 196, 783–790.Sánchez, M. T., De la Haba, M. J., Guerrero, J. E., Garrido-Varo, A., & Pérez-Marín, D. (2011). Testing of a local approach for the prediction of quality parameters in intact nectarines using a portable NIRS instrument. Postharvest Biology and Technology, 60(2), 130–135.Santos, P., Santos, F., Santos, J., & Bezerra, H. (2013). Application of extended multiplicative signal correction to short-wavelength near infrared spectra of moisture in marzipan. Journal of Data Analysis and Information Processing, 1, 30–34.Savitzky, A., & Golay, M. J. E. (1964). Smoothing and differentiation of data by simplified squares procedures. Analytical Chemistry, 36, 1627–1639.Shao, Y., He, Y., Gómez, A. H., Pereir, A. G., Qiu, Z., & Zhang, Y. (2007). Visible/near infrared spectrometric technique for nondestructive assessment of tomato ‘Heatwave’ (Lycopersicum esculentum) quality characteristics. Journal of Food Engineering, 81(4), 672–678.Singh, Z., Singh, R. K., Sane, V. A., & Nath, P. (2013). Mango—postharvest biology and biotechnology. Critical Reviews in Plant Sciences, 32(4), 217–236.Soares, S. F. C., Gomes, A. A., Galvão Filho, A. R., Araújo, M. C. U., & Galvão, R. K. H. (2013). The successive projections algorithm. Trends in Analytical Chemistry, 42, 84–98.Tijskens, L. M. M., Zerbini, P. E., Schouten, R. E., Vanoli, M., Jacob, S., Grassi, M., & Torricelli, A. (2007). Assessing harvest maturity in nectarines. Postharvest Biology and Technology, 45, 204–213.Valero, A., Marín, S., Ramos, A. J., & Sanchis, V. (2007). Effect of preharvest fungicides and interacting fungi on Aspergillus carbonarius growth and ochratoxin A synthesis in dehydrating grapes. Letters in Applied Microbiology, 45, 194–199.Walsh, K. B., Golic, M., & Greensill, C. V. (2004). Sorting of fruit and vegetables using near infrared spectroscopy: application to soluble solids and dry matter content. Journal of Near Infrared Spectroscopy, 12, 141–148.Williams, P. C. & Norris, K. H. (1987). Qualitative applications of near infrared reflectance spectroscopy. P. C. Williams & K. H. Norris (Eds.), Near infrared technology in the agricultural and food industries, pp. 241–246. St. Paul, MN: American Association of Cereal Chemist

Automated systems based on machine vision for inspecting citrus fruits from the field to postharvest - A review

[EN] Computer vision systems are becoming a scientific but also a commercial tool for food quality assessment. In the field, these systems can be used to predict yield, as well as for robotic harvesting or the early detection of potentially dangerous diseases. In postharvest handling, it is mostly used for the automated inspection of the external quality of the fruits and for sorting them into commercial categories at very high speed. More recently, the use of hyperspectral imaging is allowing not only the detection of defects in the skin of the fruits but also their association to certain diseases of particular importance. In the research works that use this technology, wavelengths that play a significant role in detecting some of these dangerous diseases are found, leading to the development of multispectral imaging systems that can be used in industry. This article reviews recent works that use colour and non-standard computer vision systems for the automated inspection of citrus. It explains the different technologies available to acquire the images and their use for the non-destructive inspection of internal and external features of these fruits. Particular attention is paid to inspection for the early detection of some dangerous diseases like citrus canker, black spot, decay or citrus Huanglongbing.This work was supported by the Instituto Nacional de Investigacion y Tecnologia Agraria y Alimentaria (INIA) through projects RTA2012-00062-C04-01 and RTA2012-00062-C04-03 with the support of European FEDER funds. The authors would like to thank and acknowledge the contributions that were made by all the students, postdocs, technicians and visiting scholars in the Precision Agriculture Laboratory at the University of Florida and the Computer Vision Laboratory at the Agricultural Engineering Centre of IVIA.Cubero García, S.; Lee, WS.; Aleixos Borrás, MN.; Albert Gil, FE.; Blasco Ivars, J. (2016). Automated systems based on machine vision for inspecting citrus fruits from the field to postharvest - A review. Food and Bioprocess Technology. 9(10):1623-1639. https://doi.org/10.1007/s11947-016-1767-1S16231639910Adebayo, S. E., Hashim, N., Abdan, K., & Hanafi, M. (2016). Application and potential of backscattering imaging techniques in agricultural and food processing—a review. Journal of Food Engineering, 169, 155–164.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.Annamalai, P., & Lee, W. S. (2003). Citrus yield mapping system using machine vision. ASAE Paper No. 031002. St. Joseph: ASAE.Annamalai, P., & Lee, W. S. (2004). Identification of green citrus fruits using spectral characteristics. ASAE Paper No. FL04–1001. St. Joseph: ASAE.Balasundaram, D., Burks, T. F., Bulanon, D. M., Schubert, T., & Lee, W. S. (2009). Spectral reflectance characteristics of citrus canker and other peel conditions of grapefruit. Postharvest Biology and Technology, 51, 220–226.Bansal, R., Lee, W. S., & Satish, S. (2013). Green citrus detection using fast Fourier transform (FFT) leakage. Precision Agriculture, 14(1), 59–70.Barreiro, P., Zheng, C., Sun, D.-W., Hernández-Sánchez, N., Pérez-Sánchez, J. M., & Ruiz-Cabello, J. (2008). Non-destructive seed detection in mandarins: comparison of automatic threshold methods in FLASH and COMSPIRA MRIs. Postharvest Biology and Technology, 47, 189–198.Basavaprasad, B., & Ravi, M. (2014). A comparative study on classification of image segmentation methods with a focus on graph based techniques. International Journal of Research in Engineering and Technology, 3, 310–315.Birth, G. S. (1976). How light interacts with foods. In: Gafney J.Jr.(Ed.), Quality detection in foods (pp. 6–11). St. Joseph: ASAE.Blanc, P.G.R., Blasco, J., Moltó, E., Gómez-Sanchis, J., & Cubero, S. (2010) System for the automatic selective separation of rotten citrus fruits. Patent number EP2133157 A1 CN101678405A, EP2133157A4, EP2133157B1, US20100121484Blasco, J., Aleixos, N., & Moltó, E. (2007a). Computer vision detection of peel defects in citrus by means of a region oriented segmentation algorithm. Journal of Food Engineering, 81(3), 535–543.Blasco, J., Aleixos, N., Gómez, J., & Moltó, E. (2007b). Citrus sorting by identification of the most common defects using multispectral computer vision. Journal of Food Engineering, 83(3), 384–393.Blasco, J., Aleixos, N., Gómez-Sanchis, J., & Moltó, E. (2009). Recognition and classification of external skin damages in citrus fruits using multispectral data and morphological features. Biosystems Engineering, 103(2), 137–145.Blasco, J., Cubero, S., & Moltó, E. (2016). Quality evaluation of citrus fruits. In D.-W. Sun (Ed.), Computer vision technology for food quality evaluation (2nd ed.). San Diego: Academic Press.Bulanon, D. M., Burks, T. F., & Alchanatis, V. (2009). Image fusion of visible and thermal images for fruit detection. Biosystems Engineering, 103, 12–22.Bulanon, D.M., Burks, T.F., Kim, D.G., & Ritenour, M.A. (2013). Citrus black spot detection using hyperspectral image analysis. Agricultural Engineering International: CIGR Journal, 15,(3)171.Burks, T. F., Villegas, F., Hannan, M. W., & Flood, S. (2003). Engineering and horticultural aspects of robotic fruit harvesting: opportunities and constraints. HortTechnology, 15(1), 79–87.Campbell, B. L., Nelson, R. G., Ebel, R. C., 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.Chinchuluun, R., Lee, W. S., & Ehsani, R. (2009). Machine vision system for determining citrus count and size on a canopy shake and catch harvester. Applied Engineering in Agriculture, 25(4), 451–458.Choi, D., Lee, W. S., Ehsani, R., & Roka, F. M. (2015). A machine vision system for quantification of citrus fruit dropped on the ground under the canopy. Transactions of the ASABE, 58(4), 933–946.Codex Alimentarius, (2011). Codex standard for oranges. Available at: http://www.codexalimentarius.org/download/standards/10372/CXS_245e.pdf . Accessed March 2016Cubero, S., Aleixos, N., Albert, A., Torregrosa, A., Ortiz, C., García-Navarrete, O., & Blasco, J. (2014a). Optimised computer vision system for automatic pre-grading of citrus fruit in the field using a mobile platform. Precision Agriculture, 15(1), 80–94.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., Diago, M. P., Blasco, J., Tardáguila, J., Millán, B., & Aleixos, N. (2014b). A new method for pedicel/peduncle detection and size assessment of grapevine berries and other fruits by image analysis. Biosystems Engineering, 117, 62–72.Dong, C.-W., Ye, Y., Zhang, J.-Q., Zhu, H.-K., & Liu, F. (2014). Detection of thrips defect on green-peel citrus using hyperspectral imaging technology combining PCA and B-Spline lighting correction method. Journal of Integrative Agriculture, 13(10), 2229–2235.FAOSTAT (2012). URL: http://faostat.fao.org http://www.fao.org/fileadmin/templates/est/COMM_MARKETS_MONITORING/Citrus/Documents/CITRUS_BULLETIN_2012.pdf . Accessed March 2016.Farrell, T. J., Patterson, M. S., & Wilson, B. (1992). A diffusion-theory model of spatially resolved steady-state diffuse reflectance for the noninvasive determination of tissue optical-properties in vivo. Medical Physics, 19, 879–888.Flood, S. J., Burks, T. F., & Teixeira, A. A. (2006). Physical properties of oranges in response to applied gripping forces for robotic harvesting. Transactions of ASAE, 49(2), 341–346.Gaffney, J. J. (1973). Reflectance properties of citrus fruit. Transactions of ASAE, 16(2), 310–314.Garcia-Ruiz, F., Sankaran, S., Maja, J. M., Lee, W. S., Rasmussen, J., & Ehsani, R. (2013). Comparison of two aerial imaging platforms for identification of Huanglongbing infected citrus trees. Computers and Electronics in Agriculture, 91, 106–115.Gómez, J., Blasco, J., Moltó, E., & Camps-Valls, G. (2007). Hyperspectral detection of citrus damage with a Mahalanobis kernel classifier. Electronics Letters, 43(20), 1082–1084.Gómez-Sanchis, J., Blasco, J., Soria-Olivas, E., Lorente, D., Escandell-Montero, P., Martínez-Martínez, J. M., Martínez-Sober, M., & Aleixos, N. (2013). Hyperspectral LCTF-based system for classification of decay in mandarins caused by Penicillium digitatum and Penicillium italicum using the most relevant bands and non-linear classifiers. Postharvest Biology and Technology, 82, 76–86.Gómez-Sanchis, J., Gómez-Chova, L., Aleixos, N., Camps-Valls, G., Montesinos-Herrero, C., Moltó, E., & Blasco, J. (2008). Hyperspectral system for early detection of rottenness caused by Penicillium digitatum in mandarins. Journal of Food Engineering, 89(1), 80–86.Gómez-Sanchis, J., Lorente, D., Soria-Olivas, E., Aleixos, N., Cubero, S., & Blasco, J. (2014). 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, 1047–1056.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.Gong, A., Yu, J., He, Y., & Qiu, Z. (2013). Citrus yield estimation based on images processed by an android mobile phone. Biosystems Engineering, 115, 162–170.Gottwald, T. R., Graham, J. H., & Schubert, T. S. (2002). Citrus canker: the pathogen and its impact. Plant Health Progress. doi: 10.1094/PHP-2002-0812-01-RV.Hannan, M., Burks, T. F., & Bulanon, D.M. (2009). A machine vision algorithm for orange fruit detection. The CIGR Ejournal. Manuscript 1281. Vol XI. December 2009.Harrell, R. C., Adsit, P. D., & Slaughter, D. C. (1985). Real-time vision-servoing of a robotic tree-fruit harvester. ASAE Paper No (pp. 85–3550). St. Joseph: ASAE.Hernández-Sánchez, N., Barreiro, P., & Ruiz-Cabello, J. (2006). On-line identification of seeds in mandarins with magnetic resonance imaging. Biosystems Engineering, 95, 529–536.Holmes, G. J., & Eckert, J. W. (1999). Sensitivity of Penicillium digitatum and P. italicum to postharvest citrus fungicides in California. Phytopathology, 89(9), 716–721.Iqbal, S. M., Gopal, A., Sankaranarayanan, P. E., & Nair, A. B. (2016). Classification of selected citrus fruits based on color using machine vision system. International Journal of Food Properties, 19, 272–288.Jackson, J. E. (1991). A user’s guide to principal components. New York: Wiley.Jafari, A., Fazayeli, A., & Zarezadeh, M. R. (2014). Estimation of orange skin thickness based on visual texture coarseness. Biosystems Engineering, 117, 73–82.Jiménez-Cuesta, M. J., Cuquerella, J., & Martínez-Jávega, J. M. (1981). Determination of a color index for citrus fruit degreening. In Proceedings of the International Society of Citriculture, 2, 750–753.Kim, D. G., Burks, T. F., Qin, J., & Bulanon, D. M. (2009). Classification of grapefruit peel diseases using color texture feature analysis. International Journal of Agricultural and Biological Engineering, 2, 41–50.Kim, D. G., Burks, T. F., Ritenour, M. A., & Qin, J. (2014). Citrus black spot detection using hyperspectral imaging. International Journal of Agricultural and Biological Engineering, 7, 20–27.Kohno, Y., Kondo, N., Iida, M., Kurita, M., Shiigi, T., Ogawa, Y., Kaichi, T., & Okamoto, S. (2011). Development of a mobile grading machine for citrus fruit. Engineering in Agriculture, Environment and Food, 4, 7–11.Kondo, N., Kuramoto, M., Shimizu, H., Ogawa, Y., Kurita, M., Nishizu, T., Chong, V. K., & Yamamoto, K. (2009). Identification of fluorescent substance in mandarin orange skin for machine vision system to detect rotten citrus fruits. Engineering in Agriculture, Environment and Food, 2, 54–59.Kurita, M., Kondo, N., Shimizu, H., Ling, P. P., Falzea, P. D., Shiigi, T., Ninomiya, K., Nishizu, T., & Yamamoto, K. (2009). A double image acquisition system with visible and UV LEDs for citrus fruit. Journal of Robotics and Mechatronics, 21, 533–540.Kurtulmus, F., Lee, W. S., & Vardar, A. (2011). Green citrus detection using eigenfruit, color and circular Gabor texture features under natural outdoor conditions. Computers and Electronics in Agriculture, 78(2), 140–149.Ladaniya, M. S. (2010). Citrus fruit: biology, technology and evaluation. San Diego: Academic Press.Li, H., Lee, W. S., & Wang, K. (2016). Immature green citrus fruit detection and counting based on fast normalized cross correlation (FNCC) using natural outdoor colour images. Precision Agriculture. doi: 10.1007/s11119-016-9443-z.Li, H., Lee, W. S., Wang, K., Ehsani, R., & Yang, C. (2014). Extended spectral angle mapping (ESAM) for citrus greening disease detection using airborne hyperspectral imaging. Precision Agriculture, 15, 162–183.Li, J., Rao, X., & Ying, Y. (2011). Detection of common defects on oranges using hyperspectral reflectance imaging. Computers and Electronics in Agriculture, 78, 38–48.Li, J., Rao, X., & Ying, Y. (2012a). Development of algorithms for detecting citrus canker based on hyperspectral reflectance imaging. Journal of the Science of Food and Agriculture, 92, 125–134.Li, J., Rao, X., Wang, F., Wu, W., & Ying, Y. (2013). Automatic detection of common surface defects on oranges using combined lighting transform and image ratio methods. Postharvest Biology and Technology, 82, 59–69.Li, J., Rao, X., Ying, Y., & Wang, D. (2010). Detection of navel oranges canker based on hyperspectral imaging technology. Nongye Gongcheng Xuebao/Transactions of the Chinese Society of Agricultural Engineering, 26, 222–228.Li, X., Lee, W. S., Li, M., Ehsani, R., Mishra, A., Yang, C., & Mangan, R. (2012b). Spectral difference analysis and airborne imaging classification for citrus greening infected trees. Computers and Electronics in Agriculture, 83, 32–46.Li, X., Lee, W. S., Li, M., Ehsani, R., Mishra, A. R., Yang, C., & Mangan, R. L. (2015). Feasibility study on Huanglongbing (citrus greening) detection based on WorldView-2 satellite imagery. Biosystems Engineering, 132, 28–38.Lopes, L. B., VanDeWall, H., Li, H. T., Venugopal, V., Li, H. K., Naydin, S., Hosmer, J., Levendusky, M., Zheng, H., Bentley, M. V., Levin, R., & Hass, M. A. (2010). Topical delivery of lycopene using microemulsions: enhanced skin penetration and tissue antioxidant activity. Journal of Pharmaceutical Sciences, 99, 1346–1357.López, J. J., Cobos, M., & Aguilera, E. (2011). Computer-based detection and classification of flaws in citrus fruits. Neural Computing and Applications, 20, 975–981.López-García, F., Andreu, G., 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., & Blasco, J. (2013a). 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.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.Lorente, D., Blasco, J., Serrano, A. J., Soria-Olivas, E., Aleixos, N., & Gómez-Sanchis, J. (2013b). Comparison of ROC feature selection method for the detection of decay in citrus fruit using hyperspectral images. Food and Bioprocess Technology, 6(12), 3613–3619.Lorente, D., Zude, M., Regen, C., Palou, L., Gómez-Sanchis, J., & Blasco, J. (2013c). Early decay detection in citrus fruit using laser-light backscattering imaging. Postharvest Biology and Technology, 86, 424–430.Lorente, D., Zude, M., Idler, C., Gómez-Sanchis, J., & Blasco, J. (2015). Laser-light backscattering imaging for early decay detection in citrus fruit using both a statistical and a physical model. Journal of Food Engineering, 154, 76–85.Maf Industries. (2016). VIOTEC brochure. http://mafindustries.com/wp-content/uploads/2015/02/viotec3.pdf . Accessed March 2016.Magwaza, L. S., Opara, U. L., Nieuwoudt, H., Cronje, P. J. R., Saeys, W., & Nicolaï, B. (2012). NIR spectroscopy applications for internal and external quality analysis of citrus fruit—a review. Food and Bioprocess Technology, 5(2), 425–444.Mehta, S. S., & Burks, T. F. (2014). Vision-based control of robotic manipulator for citrus harvesting. Computers and Electronics in Agriculture, 102, 146–158.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). California: Academic Press.Moltó, E., Plá, F., & Juste, F. (1992). Vision systems for the location of citrus fruit in a tree canopy. Journal of Agricultural Engineering Research, 52, 101–110.Momin, A., Kondo, N., Kuramoto, M., Ogawa, Y., Yamamoto, K., & Shiigi, T. (2012). Investigation of excitation wavelength for fluorescence emission of citrus peels based on UV-VIS spectra. Engineering in Agriculture, Environment and Food, 5, 126–132.Momin, A., Kondo, N., Ogawa, Y., Ido, K., & Ninomiya, K. (2013b). Patterns of fluorescence associated with citrus peel defects. Engineering in Agriculture, Environment and Food, 6, 54–60.Momin, A., Kuramoto, M., Kondo, N., Ido, K., Ogawa, Y., Shiigi, T., & Ahmad, U. (2013a). Identification of UV-fluorescence components for detecting peel defects of lemon and yuzu using machine vision. Engineering in Agriculture, Environment and Food, 6, 165–171.Morgan, S. P., & Stockford, I. M. (2003). Surface-reflection elimination in polarization imaging of superficial tissue. Optics Letters, 28, 114–116.Niphadkar, N. P., Burks, T. F., Qin, J., & Ritenour, M. (2013b). Edge effect compensation for citrus canker lesion detection due to light source variation—a hyperspectral imaging application. Agricultural Engineering International: CIGR Journal, 15, 314–327.Niphadkar, N. P., Burks, T. F., Qin, J. W., & Ritenour, M. A. (2013a). Estimation of citrus canker lesion size using hyperspectral reflectance imaging. International Journal of Agricultural and Biological Engineering, 6, 41–51.Obenland, D., Margosan, D., Smilanick, J. L., & Mackey, B. (2010). Ultraviolet fluorescence to identify navel oranges with poor peel quality and decay. HortTechnology, 20, 991–995.Ogawa, Y., Abdul, M. M., Kuramoto, M., Kohno, Y., Shiigi, T., Yamamoto, K., & Kondo, K. (2011). Rotten part detection on citrus fruit surfaces by use of fluorescent images. The Review of Laser Engineering, 394, 255–261.Okamoto, H., & Lee, W. S. (2009). Green citrus detection using hyperspectral imaging. Computers and Electronics in Agriculture, 66(2), 201–208.Omid, M., Khojastehnazhand, M., & Tabatabaeefar, A. (2010). Estimating volume and mass of citrus fruits by image processing technique. Journal of Food Engineering, 100, 315–321.Ottavian, M., Barolo, M., & García-Muñoz, S. (2013). Maintenance of machine vision systems for product quality assessment. Part I. Addressing changes in lighting conditions. Industrial & Engineering Chemistry Research, 52, 12309–12318.Ottavian, M., Barolo, M., & García-Muñoz, S. (2014). Maintenance of machine vision systems for product quality assessment. Part II. Addressing camera replacement. Industrial & Engineering Chemistry Research, 53, 1529–1536.Palou, L. (2014). Penicillium digitatum, Penicillium italicum (green mold, blue mold). In S. Bautista-Baños (Ed.), Postharvest decay. Control strategies. London: Elsevier.Palou, L., Smilanick, J. L., Montesinos-Herrero, C., Valencia-Chamorro, S., & Pérez-Gago, M. B. (2011). Novel approaches for postharvest preservation of fresh citrus fruits. In Slaker (Ed.), Citrus fruits: properties, consumption and nutrition. New York: Nova Science Publishers, Inc..Pongnumkul, S., Chaovalit, P., & Surasvadi, N. (2015). Applications of smartphone-based sensors in agriculture: a systematic review of research. Journal of Sensors, Open Access Article ID 195308.Pourreza, A., Lee, W. S., Ehsani, R., Schueller, J. K., & Raveh, E. (2015a). An optimum method for real-time in-field detection of Huanglongbing disease using a vision sensor. Computers and Electronics in Agriculture, 110, 221–232.Pourreza, A., Lee, W. S., Etxeberria, E., & Banerjee, A. (2015b). An evaluation of a vision based sensor performance in Huanglongbing disease identification. Biosystems Engineering, 130, 13–22.Qin, J., Burks, T. F., Kim, M. S., Chao, K., & Ritenour, M. A. (2008). Citrus canker detection using hyperspectral reflectance imaging and PCA-based image classification method. Sensing and Instrumentation for Food Quality and Safety, 2(3), 168–177.Qin, J., Burks, T. F., Ritenour, M. A., & Gordon Bonn, W. (2009). Detection of citrus canker using hyperspectral reflectance imaging with spectral information divergence. Journal of Food Engineering, 93, 183–191.Qin, J., Burks, T. F., Zhao, X., Niphadkar, N., & Ritenour, M. A. (2011). Multispectral detection of citrus canker using hyperspectral band selection. Transactions of the ASABE, 54, 2331–2341.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, 87–93.Sengupta, S., & Lee, W. S. (2014). Identification and determination of the number of immature green citrus fruit under different ambient light conditions.

Non-destructive quality assessment of horticultural products in sorting lines

[SPA] En el sector hortofrutícola, la exigencia de calidad por parte del consumidor es cada vez mayor. Actualmente, hay una creciente demanda dirigida a una adecuada automatización de los procesos industriales que permitan garantizar una calidad excelente del producto final. El Centro de AgroIngeniería del Instituto Valenciano de Investigaciones Agrarias (IVIA), en colaboración con el sector industrial, ha desarrollado durante los últimos años sensores electrónicos y sistemas de inspección por visión artificial que permiten una clasificación automática de distintos productos hortofrutícolas muy significativos para la agricultura española, entre los que cabe citar: - Un sensor de firmeza capaz de clasificar melocotones en tres categorías: muy firmes, firmes y poco firmes. Los ensayos realizados en una línea de confección precomercial demuestran que el sensor puede trabajar adecuadamente a 8 frutas s-1 y es capaz de clasificar la firmeza con un 80% de repetibilidad. - Un prototipo capaz de inspeccionar automáticamente granos de granada para el consumo por visión artificial. La máquina individualiza, inspecciona, clasifica y separa los granos de granada en cuatro de categorías de calidad que son función de su color y tamaño, rechazando aquellos que no cumplen las especificaciones mínimas y agrupando los que presentan características similares. Con ello se consigue el envasado en lotes de producto uniforme y de alta calidad, resultando más atractivo al consumidor. - Un sistema para clasificar automáticamente gajos de mandarina para conserva por visión artificial. El sistema distingue entre gajos enteros, rotos o dobles, además de detectar la presencia de semillas en los gajos. El sistema clasifica correctamente más del 75% de los gajos analizados. [ENG] In the fruit and vegetable sector, the quality of the produce has become an important demand for consumers that makes necessary the automation the industrial processes that allow to guarantee an excellent quality of the final product During the last ten years, The Centre for Agroengineering research of the Instituto Valenciano de Investigaciones Agrarias (IVIA), in collaboration with industrial sector, has developed electronic sensors and computer vision systems for the automatic on-line inspection and classification of several horticultural products of great interest for Mediterranean agriculture. Among the recent researches it is necessary to mention: - A firmness sensor capable of classifying peaches under three categories: very firm, firm and slightly firm. A module composed by two sensors has been integrated in a precommercial sorting line working at 8 fruits s-1. The performed tests have demonstrated its reliability with up to 80% of success rate. - A prototype capable of inspecting automatically pomegranate arils for the consumption by means of artificial vision. The machine individualizes, inspects, classifies and separates the arils in four of categories of quality that are a function of the colour and size, rejecting those that do not fulfil the minimal specifications and grouping those who present similar characteristics. - A system to classify automatically segments of mandarin for conserve by means of artificial vision. The system distinguishes among sound, broken or double segments, and is able to detect the presence of seeds in the segments. The system classifies correctly more tan 75 % of the analyzed segments.Los trabajos de investigación descritos en el artículo han sido financiados parcialmente por la UE a través de los contratos QLK1-CT-2002-70791 y QLK1-2000-70106, por el Instituto Nacional de Investigación y Tecnología Agraria y Alimentaría (INIA) a través de los proyectos RTA03-105 y TRT2006-00046-00-00 y por las empresas Frutas Mira Hermanos, S.L.,Agriconsa, S.A. y Fomesa, S.

Application for the estimation of the standard citrus colour index (CCI) using image processing in mobile devices

[EN] The collection of oranges normally begins before they have reached the typical orange colour. Moreover, citrus fruits are subjected to certain degreening treatments that depend on the standard citrus colour index (CCI) at harvest. In order to facilitate the measure of this index, a free application that uses image processing techniques has been developed for Android-based mobile devices using the built-in camera of the device. The image analysis process is performed on all the images from the live input of the camera to obtain the CCI of such fruit using the open source OpenCV library. For this purpose, the RGB (red, green and blue colour coordinates) average value of a pre-selected area of the input image is calculated and then converted to HunterLab colour space to finally calculate the CCI. Several tests were carried out in the field with the fruit on the trees and under laboratory conditions with different varieties of oranges (Navel, Bonanza, Cram and Navelina) at different stages of maturity, and using different Android devices. The results were obtained for each device and condition in relation to the colour measured by a camera and compared with the performance of a panel of workers who evaluated the colour using the traditional methods. Best R-2 values obtained were 0.854 for outdoors conditions and 0.881 when measurements were done indoors.This work was partially funded by INIA and FEDER funds through research project RTA2015-00078-00-00.Cubero-García, S.; Albert Gil, FE.; Prats-Montalbán, JM.; Fernandez-Pacheco, DG.; Blasco Ivars, J.; Aleixos Borrás, MN. (2018). Application for the estimation of the standard citrus colour index (CCI) using image processing in mobile devices. Biosystems Engineering. 167:63-74. doi:10.1016/j.biosystemseng.2017.12.012S637416

A new internal quality index for mango and its predicción by external visible and near-infrared reflection spectroscopy

[EN] A non-destructive method based on external visible and near-infrared reflection spectroscopy for determining the internal quality of intact mango cv. ‘Osteen’ was investigated. An internal quality index, well correlated with the ripening index of the samples, was developed based on the combination of a biochemical property (total soluble solids) and physical properties (firmness and flesh colour) of mango samples. The diffuse reflectance spectra of the samples were recorded and used to predict the internal quality and the ripening index. These spectra were obtained using different spectroscopic external measurement sensors involving a spectrometer, capable of measuring in different spectral ranges (600– 1100 nm and 900–1750 nm), and also a spectrocolorimeter that measured in the visible range (400– 700 nm). Three regression models were developed by partial least squares to establish the relationship between spectra and indices. Good results in the prediction of internal quality of the samples were obtained using the full spectral range (Rp 2 = 0.833–0.879, RMSEP = 0.403–0.507 and RPD = 2.341–2.826) and some selected wavelengths (Rp 2 = 0.815–0.896, RMSEP = 0.403–0.537 and RPD = 2.060–2.905). The results obtained from this study revealed that external visible and near-infrared reflection spectroscopy can be used as a non-destructive method to determine the internal quality of mango cv. ‘Osteen’.This work was partially funded by the Conselleria d' Educació, Investigació, Cultura i Esport, Generalitat Valenciana, through the project AICO/2015/122 and by the INIA through the projects RTA2012-00062-C04-01, 02 and 03 with the support of FEDER funds. V. Cortés López thanks the Spanish MEC for the FPU grant (FPU13/04202).Cortés López, V.; Ortiz Sánchez, MC.; Aleixos Borrás, MN.; Blasco Ivars, J.; Cubero García, S.; Talens Oliag, P. (2016). A new internal quality index for mango and its predicción by external visible and near-infrared reflection spectroscopy. Postharvest Biology and Technology. (118):148-158. doi:10.1016/j.postharvbio. 2016.04.011S14815811

Astringency assessment of persimmon by hyperspectral imaging

[EN] One of the current challenges of persimmon postharvest research is the development of non-destructive methods that allow determination of the internal properties of the fruit, such as maturity, flesh firmness and astringency. This study evaluates the usefulness of hyperspectral imaging in the 460 1020 nm range as a non-destructive tool to achieve these aims in Persimmon cv. Rojo Brillante which is an astringent cultivar. Fruit were harvested at three different stages of commercial maturity and exposed to different treatments of CO2 (95% CO2 20 ºC from 0 to 24 h) in order to obtain fruit with different levels of astringency. Partial Least Square (PLS) based methods were used to classify persimmon fruits by maturity and to predict flesh firmness from the average spectrum of each fruit. The results showed a 97.9% rate of correct maturity classification and an R2P of 0.80 for firmness prediction with only five selected wavelengths. For astringency assessment, as our results showed that the soluble tannins that remain after CO2 treatments are distributed irregularly inside the flesh, a model based on PLS was built using the spectrum of every pixel in the fruit. The model obtained an R2P of 0.91 which allowed the creation of the predicted distribution maps of the tannins in the flesh of the fruit, thereby pointing to hyperspectral systems as a promising technology to assess the effectiveness of the deastringency treatments that are usually applied before commercialising persimmons from astringent cultivars.This work has been partially funded by the Instituto Nacional de Investigacion y Tecnologia Agraria y Alimentaria de Espana (INIA) through projects RTA2012-00062-C04-01, RTA2012-00062-C04-03 and RTA2013-00043-C02 with the support of FEDER funds and by the Conselleria d' Educacio, Investigacio, Cultura i Esport, Generalitat Valenciana, through the project AICO/2015/122. Sandra Munera thanks INIA for the grant FPI-INIA #43 (CPR2014-0082) partially supported by FSE funds.S354112

Optimised computer vision system for automatic pre-grading of citrus fruit in the field using a mobile platform

The mechanisation and automation of citrus harvesting is considered to be one of the best options to reduce production costs. Computer vision technology has been shown to be a useful tool for fresh fruit and vegetable inspection, and is currently used in post-harvest fruit and vegetable automated grading systems in packing houses. Although computer vision technology has been used in some harvesting robots, it is not commonly utilised in fruit grading during harvesting due to the difficulties involved in adapting it to field conditions. Carrying out fruit inspection before arrival at the packing lines could offer many advantages, such as having an accurate fruit assessment in order to decide among different fruit treatments or savings in the cost of transport and marketing non-commercial fruit. This work presents a computer vision system, mounted on a mobile platform where workers place the harvested fruits, that was specially designed for sorting fruit in the field. Due to the specific field conditions, an efficient and robust lighting system, very low-power image acquisition and processing hardware, and a reduced inspection chamber had to be developed. The equipment is capable of analysing fruit colour and size at a speed of eight fruits per second. The algorithms developed achieved prediction accuracy with an R-2 coefficient of 0.993 for size estimation and an R-2 coefficient of 0.918 for the colour index.This research work has been funded by the Instituto Nacional de Investigacion y Tecnologia Agraria y Alimentaria de Espana (INIA) and the European FEDER funds (projects RTA2009-00118-C02-01 and RTA2009-00118-C02-02). The authors wish to thank the collaboration of the company Argiles Diseny i Fabricacio, S.L.Cubero García, S.; Aleixos Borrás, MN.; Albert Gil, FE.; Torregrosa, A.; Ortiz Sánchez, MC.; García Navarrete, OL.; Blasco Ivars, J. (2014). Optimised computer vision system for automatic pre-grading of citrus fruit in the field using a mobile platform. Precision Agriculture. 15(1):80-94. doi:10.1007/s11119-013-9324-7S8094151Baeten, J., Donné, K., Boedrij, S., Beckers, W., & Claesen, E. (2008). Autonomous fruit picking machine: A robotic apple harvester. Springer Tracts in Advanced Robotics, 42, 531–539.Blasco, J., Aleixos, N., Gómez-Sanchis, J., & Moltó, E. (2009a). Recognition and classification of external skin damage in citrus fruits using multispectral data and morphological features. Biosystems Engineering, 103, 137–145.Blasco, J., Aleixos, N., Roger, J. M., Rabatel, G., & Moltó, E. (2002). Robotic weed control using machine vision. Biosystems Engineering, 83(2), 149–157.Blasco, J., Cubero, S., Gómez-Sanchis, J., Mira, P., & Moltó, E. (2009b). Development of a machine for the automatic sorting of pomegranate (Punica granatum) arils based on computer vision. Journal of Food Engineering, 90, 27–34.Chong, V. K., Monta, M., Ninomiya, K., Kondo, N., Namba, K., Terasaki, E., et al. (2008). Development of mobile eggplant grading robot for dynamic in-field variability sensing––manufacture of robot and performance test. Engineering in Agriculture, Environment and Food, 1(2), 68–76.Coppock, G. E., & Jutras, P. J. (1960). Mechanizing citrus fruit harvesting. Transactions of the ASAE, 3(2), 130–132.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., et al. (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.DOGV. (2006). Diari Oficial de la Comunitat Valenciana, 5346, 30321–30328.Edan, Y., Rogozin, D., Flash, T., & Miles, G. E. (2000). Robotic melon harvesting. IEEE Transactions on Robotics and Automation, 16(6), 831–834.Ehsani, M. R., Grift, T. E., Maja, J. M., & Zhong, D. (2009). Two fruit counting techniques for citrus mechanical harvesting machinery. Computers and Electronics in Agriculture, 65(2), 186–191.Feng, G., Qixin, C., & Masateru, N. (2008). Fruit detachment and classification method for strawberry harvesting robot. International Journal of Advanced Robotic Systems, 5(1), 41–48.Gómez-Sanchis, J., Gómez-Chova, L., Aleixos, N., Camps-Valls, G., Montesinos-Herrero, C., Moltó, E., et al. (2008). Hyperspectral system for early detection of rottenness caused by Penicillium digitatum in mandarins. Journal of Food Engineering, 89(1), 80–86.HunterLab. (2008). Applications note, 8(9) http://www.hunterlab.com/appnotes/an08_96a.pdf . Accessed Nov 2012.Jiménez-Cuesta, M.J., Cuquerella, J., & Martínez-Jávega, J.M. (1981). Determination of a color index for citrus fruit degreening. In: Proceedings of the International Society of Citriculture, Tokyo (Japan), vol. 2 (pp. 750–753).Jutras, P.J., & Coppock, G.E. (1958). Mechanization of citrus fruit picking. Florida State Horticultural Society, 71, 201,204.Kohno, Y., Kondo, N., Iida, M., Kurita, M., Shiigi, T., Ogawa, Y., et al. (2011). Development of a mobile grading machine for citrus fruit. Engineering in Agriculture, Environment and Food, 4(1), 7–11.Kondo, N. (2009). Robotization in fruit grading system. Sensors and Instrumentation for Food Quality, 3, 81–87.Lee, W. S., & Slaughter, D. C. (2004). Recognition of partially occluded plant leaves using a modified Watershed algorithm. Transactions of the ASAE, 47, 1269–1280.Lee, W. S., Slaughter, D. C., & Giles, D. K. (1999). Robotic weed control system for tomatoes. Precision Agriculture, 1(1), 95–113.Li, Z., Li, P., & Liu, J. (2011). Physical and mechanical properties of tomato fruits as related to robot harvesting. Journal of Food Engineering, 103(2), 170–178.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.Mazzetto, F., Calcante, A., Mena, A., & Vercesi, A. (2010). Integration of optical and analogue sensors for monitoring canopy health and vigour in precision viticulture. Precision Agriculture, 11(6), 636–649.McBratney, A., Whelan, B., Ancev, T., & Bouma, J. (2005). Future directions of precision agriculture. Precision Agriculture, 6(1), 7–23.Mizushima, A., & Lu, R. (2011). Cost benefits analysis of in-field presorting for the apple industry. Applied Engineering in Agriculture, 27(1), 33–40.Muscato, G., Prestifilippo, M., Abbate, N., & Rizzuto, I. (2005). A prototype of an orange picking robot: Past history and experimental results. Industrial Robot, 32(2), 128–138.Nieuwenhuizen, A. T., Hofstee, J. W., & van Henten, E. J. (2010). Adaptive detection of volunteer potato plants in sugar beet fields. Precision Agriculture, 11, 433–447.Official Journal of European Communities. (2001). 14.09.2001. pp. L244/12–L244/18. http://eur-lex.europa.eu/LexUriServ/LexUriServ.do?uri=OJ:L:2001:244:0012:0018:EN:PDF . Accessed May 2013.Ortiz, C., Blasco, J., Balasch, S., & Torregrosa, A. (2011). Shock absorbing surfaces for collecting fruit during the mechanical harvesting of citrus. Biosystems Engineering, 110, 2–9.Qiao, J., Sasao, A., Shibusawa, S., Kondo, N., & Morimoto, E. (2004). Mobile fruit grading robot (part1)––Development of a robotic system for grading sweet peppers. Journal of the Japanese Society of Agricultural Machinery (JSAM), 66(2), 113–122.Qiao, J., Sasao, A., Shibusawa, S., Kondo, N., & Morimoto, E. (2005). Mapping yield and quality using the mobile fruit grading robot. Biosystems Engineering, 90(2), 135–142.Ruiz-Altisent, M., Ortiz-Cañavate, J., & Valero, C. (2004). Fruit and vegetables harvesting systems. In: R. Dris and S. M. Jain (Eds.), Production practices and quality assessment of food crops, vol. 1: Preharvest practice (pp. 261–285). Dordrecht: Kluwer.Torregrosa, A., Gil, J., Ortiz, C., Ortí, E., & Martín, B. (2009). Mechanical harvesting of oranges and mandarins in Spain. Biosystems Engineering, 104(1), 18–24.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

Non-destructive assessment of the internal quality of intact persimmon using colour and VIS/NIR hyperspectral imaging

The internal quality of intact persimmon cv. Rojo Brillante was assessed trough visible and near infrared hyperspectral imaging. Fruits at three stages of commercial maturity were exposed to different treatments with CO2 to obtain fruit with different ripeness and level of astringency (soluble tannin content). Spectral and spatial information were used for building classification models to predict ripeness and astringency trough multivariate analysis techniques like linear and quadratic discriminant analysis (LDA and QDA) and support vector machine (SVM). Additionally, flesh firmness was predicted by partial least square regression (PLSR). The full spectrum was used to determine the internal properties and later principal component analysis (PCA) was used to select optimal wavelengths (580, 680 and 1050 nm). The correct classification was above 92% for the three classifiers in the case of ripeness and 95% for QDA in the case of astringency. A value of R2 = 0.80 and a ratio of prediction deviation (RPD) of 1.86 were obtained with the selected wavelengths for the prediction of firmness which demonstrated the potential of hyperspectral imaging as a non-destructive tool in the assessment of the firmness, ripeness state and astringency level of Rojo Brillante persimmon.This work has been partially funded by the INIA and FEDER through projects RTA2012-00062-C04-01, RTA2012-00062-C04-03 and RTA2013-00043-C02, GVA through the project AICO/2015/122, the International S&T Cooperation Programs of China (2015DFA71150), and the International S&T Cooperation Program of Guangdong Province, China (2013B051000010). Sandra Munera thanks INIA for the grant FPI-INIA #43 (CPR2014-0082) partially supported by FSE funds.Munera-Picazo, S.; Besada Ferreiro, CM.; Aleixos Borrás, MN.; Talens Oliag, P.; Salvador, A.; Sun, D.; Cubero-García, S.... (2017). Non-destructive assessment of the internal quality of intact persimmon using colour and VIS/NIR hyperspectral imaging. Food Science and Technology. 77:241-248. https://doi.org/10.1016/j.lwt.2016.11.063S2412487