Photosynthetica 2016, 54(1):101-109 | DOI: 10.1007/s11099-015-0163-9

Measurements of reflectance and fluorescence spectra for nondestructive characterizing ripeness of grapevine berries

M. Navrátil1,*, C. Buschmann2
1 Faculty of Science, Department of Physics, University of Ostrava, Slezská Ostrava, Czech Republic
2 Botanical Institute, Karlsruhe Institute of Technology (KIT), Karlsruhe, Germany

In vivo reflectance and fluorescence spectra from berry skins of a white (Riesling) and red (Cabernet Sauvignon) grapevine variety were measured during a ripening season with a new CMOS radiometer instrument. Classical reference measurements were also carried out for a sugar content of the berry juice [°Brix] and pigment contents (chlorophyll a and b, carotenoids, anthocyanins) from methanol extracts of the berry skin. We showed that the colours and the spectra analysed from them could be taken as an unambiguous indicator of grapevine ripening. Reflectance spectra, which were affected by the content of pigments (chlorophylls and anthocyanins), effects of surface (wax layers), and tissue structure (cell size) of the berries well correlated (R 2 = 0.89) with the °Brix measurements of the berries. The fast data acquisition of both reflectance and fluorescence spectra in one sample with our radiometer instrument made it superior over the time-consuming, traditional, and mostly destructive chemical analysis used in vine-growing management.

Additional key words: Cabernet Sauvignon; CIE 1931; plant pigments; Riesling

Received: June 8, 2014; Accepted: June 22, 2015; Published: March 1, 2016  Show citation

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Navrátil, M., & Buschmann, C. (2016). Measurements of reflectance and fluorescence spectra for nondestructive characterizing ripeness of grapevine berries. Photosynthetica, 54(1), 101-109. doi: 10.1007/s11099-015-0163-9
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    References

    1. Abu-Khalaf N., Bennedsen B.S.: Near infrared (NIR) technology and multivariate data analysis for sensing taste attributes of apples. - Int. Agrophys. 18: 203-211, 2004.
    2. Agati G., Pinelli P., Ebner S.C. et al.: Nondestructive evaluation of anthocyanins in olive (Olea europaea) fruits by in situ chlorophyll fluorescence spectroscopy. - J. Agr. Food Chem. 53: 1354-1363, 2005. Go to original source...
    3. Agati G., Meyer S., Matteini P., Cerovic Z.G.: Assessment of anthocyanins in grape (Vitis vinifera L.) berries using a noninvasive chlorophyll fluorescence method. - J. Agr. Food Chem. 55: 1053-1061, 2007. Go to original source...
    4. Ben Ghozlen N., Cerovic Z.G., Germain C. et al.: Nondestructive optical monitoring of grape maturation by proximal sensing. - Sensors 10: 10040-10068, 2010. Go to original source...
    5. Buschmann C.: Variability and application of the chlorophyll fluorescence emission ratio red/far-red of leaves. - Photosynth. Res. 92: 261-271, 2007. Go to original source...
    6. Buschmann C., Nagel E.: In vivo spectroscopy and internal optics of leaves as basis for remote sensing of vegetation. - Int. J. Remote Sens. 14: 711-722, 1993. Go to original source...
    7. Buschmann C., Lenk S., Lichtenthaler H.K.: Reflectance spectra and images of green leaves with different tissue structure and chlorophyll content. - Isr. J. Plant Sci. 60: 49-64, 2012. Go to original source...
    8. Cao F., Wu D., He Y.: Soluble solids content and pH prediction and varieties discrimination of grapes based on visible-near infrared spectroscopy. - Comput. Electron. Agr. 71S: S15-S18, 2010. Go to original source...
    9. Coombe B.G.: Distribution of solutes within the developing grape berry in relation to its morphology. - Am. J. Enol. Viticult. 38: 120-127, 1987. Go to original source...
    10. Coombe B.G., McCarthy M.G.: Dynamics of grape berry growth and physiology of ripening. - Aust. J. Grape Wine R. 6: 131-135, 2000. Go to original source...
    11. Cozzolino D., Dambergs R.G., Janik L. et al.: Analysis of grapes and wine by near infrared spectroscopy. - J. Near Infrared Spec. 14: 279-289, 2006. Go to original source...
    12. Granitto P.M., Navone H.D., Verdes P.F., Ceccatto H.A.: Weed seeds identification by machine vision. - Comput. Electron. Agr. 33: 91-103, 2002. Go to original source...
    13. Granitto P.M., Verdes P.F., Ceccatto H.A.: Large-scale investigation of weed seed identification by machine vision. - Comput. Electron. Agr. 47: 15-24, 2005. Go to original source...
    14. Harris J.M., Kriedemann P.E., Possingham J.V.: Anatomical aspects of grape berry development. - Vitis 7: 106-119, 1968.
    15. Jackson R.S.: Wine Science. Principles and Applications, 3rd ed. Pp. 776. Elsevier, Amsterdam 2008.
    16. Jamshidi B., Minaei S., Mohajerani E., Ghassemian H.: Reflectance Vis/NIR spectroscopy for nondestructive taste characterization of Valencia oranges. - Comput. Electron. Agr. 85: 64-69, 2012. Go to original source...
    17. Jones H.G., Vaugham R.A.: Remote Sensing of Vegetation. - Principles, Techniques and Applications. Pp. 400. Oxford University Press, Oxford, 2010.
    18. Kennedy J.A., Hayasaka Y., Vidal S. et al.: Composition of grape skin proanthocyanidins at different stages of berry development. - J. Agr. Food Chem. 49: 5348-5355, 2001. Go to original source...
    19. Kolb C.A., Wirth E., Kaiser W.M. et al.: Non-invasive evaluation of the degree of ripeness in grape berries (Vitis vinifera L. cv. Bacchus and Silvaner) by chlorophyll fluorescence. - J. Agr. Food Chem. 54: 299-305, 2006. Go to original source...
    20. Kondo N., Ahmad U., Monta M., Murase H.: Machine vision based quality evaluation of Iyokan orange fruit using neural networks. - Comput. Electron. Agr. 29: 135-147, 2000. Go to original source...
    21. Kurtulmus F., Lee W.S., Vardar A.: Green citrus detection using' eigenfruit', color and circular Gabor texture features under natural outdoor conditions. - Comput. Electron. Agr. 78: 140-149, 2011. Go to original source...
    22. le Maire G., François C., Dufrêne E.: Towards universal broad leaf chlorophyll indices using PROSPECT simulated database and hyperspectral reflectance measurements. - Remote Sens. Environ. 89: 1-28, 2004. Go to original source...
    23. Lenk S., Buschmann C., Pfündel E.: In vivo assessing flavonols in white grape berries (Vitis vinifera L. cv. Pinot Blanc) of different degrees of ripeness using chlorophyll fluorescence imaging. - Funct. Plant Biol. 34: 1092-1104, 2007. Go to original source...
    24. Lichtenthaler H.K.: Chlorophylls and carotenoids, the pigments of photosynthetic biomembranes. - Methods Enzymol. 148: 350-382, 1987. Go to original source...
    25. Malacara D.: Color Vision and Colorimetry: Theory and Applications. Pp. 176. SPIE Press, Bellingham 2002. Go to original source...
    26. Mebatsion H.K., Paliwal J., Jayas D.S.: Automatic classification of non-touching cereal grains in digital images using limited morphological and color features. - Comput. Electron. Agr. 90: 99-105, 2013. Go to original source...
    27. Meyer G.E., Neto J.C., Jones D.D., Hindman T.W.: Intensified fuzzy clusters for classifying plant, soil, and residue regions of interest from color images. - Comput. Electron. Agr. 42: 161-180, 2004. Go to original source...
    28. Mollazade K., Omid M., Arefi A.: Comparing data mining classifiers for grading raisins based on visual features. - Comput. Electron. Agr. 84: 124-131, 2012. Go to original source...
    29. Onyango C.M., Marchant J.A.: Segmentation of row crop plants from weeds using colour and morphology. - Comput. Electron. Agr. 39: 141-155, 2003. Go to original source...
    30. Papageorgiou G.C., Govindjee: Chlorophyll a Fluorescence: A Signature of Photosynthesis. Pp. 820. Springer, Dordrecht 2004. Go to original source...
    31. Payne A.B., Walsh K.B., Subedi P.P., Jarvis D.: Estimation of mango crop yield using image analysis - Segmentation method. - Comput. Electron. Agr. 91: 57-64, 2013. Go to original source...
    32. Pieczywek P.M., Zdunek A.: Automatic classification of cells and intercellular spaces of apple tissue. - Comput. Electron. Agr. 81: 72-78, 2012. Go to original source...
    33. Possner D.R.E., Kliewer W.M.: The localization of acids, sugars, potassium and calcium in developing grape berries. - Vitis 24: 229-240, 1985.
    34. Rodríguez-Pulido F.J., Gómez-Robledo L., Melgosa M. et al.: Ripeness estimation of grape berries and seeds by image analysis. - Comput. Electron. Agr. 82: 128-133, 2012. Go to original source...
    35. Romeyer F.M., Macheix J.J., Goiffon J.P. et al.: The browning capacity of grapes. 3. Changes and importance of hydroxycinnamic acid-tartaric acid esters during development and maturation of the fruit." - J. Agr. Food Chem. 31: 346-349, 1983. Go to original source...
    36. Rouse J.W.J., Haas H.R., Schell A.J., Deering W.D.: Monitoring vegetation systems in the Great Plains with ERTS. - In: Freden S.C., Mercanti E.P., Becker M.A. (ed.): 3rd Earth Resources Technology Satellite-1 Symposium, Volume I: Technical Presentations. NASA SP-351. Pp. 309-317, NASA, Washington, D.C. 1974.
    37. Ruiz-Altisent M., Ruiz-Garcia L., Moreda G.P. et al.: Sensors for product characterization and quality of specialty crops - A review. - Comput. Electron. Agr. 74: 176-194, 2010. Go to original source...
    38. Rustioni L., Basilico R., Fiori S. et al.: Grape colour phenotyping: development of a method based on the reflectance spectrum. - Phytochem. Anal. 24: 453-459, 2013. Go to original source...
    39. Rustioni L., Rocchi L., Guffanti E. et al.: Characterization of Grape (Vitis vinifera L.) Berry Sunburn Symptoms by Reflectance. - J. Agric. Food Chem., DOI: 10.1021/jf405772f, 2014. Go to original source...
    40. Steele M.R., Gitelson A.A., Rundquist D.C., Merzlyak M.N.: Nondestructive estimation of anthocyanin content in grapevine leaves. - Am. J. Enol. Viticult. 60: 87-92, 2009. Go to original source...
    41. Tian Q., Giusti M.M., Stoner G.D.: Screening for anthocyanins using high-performance liquid chromatography coupled to electrospray ionization tandem mass spectrometry with precursor-ion analysis, product-ion analysis, common-neutralloss analysis, and selected reaction monitoring. - J. Chromatogr. A 1091: 72-82, 2005. Go to original source...
    42. Tremblay N., Wang Z., Cerovic Z.G.: Sensing crop nitrogen status with fluorescence indicators. A review. - Agron. Sustain. Dev. 32: 451-464, 2012 Go to original source...
    43. Valeur B., Berberan-Santos M.: Molecular Fluorescence -Principles and Applications. 2nd ed. Pp. 592. Wiley-VCH, Weinheim, 2012 Go to original source...
    44. Zhang H., Lan Y., Suh C.P.-C. et al.: Fusion of remotely sensed data from airborne and ground-based sensors to enhance detection of cotton plants. - Comput. Electron. Agr. 93: 55-59, 2013. Go to original source...

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