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

详细信息    查看全文

• 作者：M. Navrátil ; C. Buschmann
• 关键词：Cabernet Sauvignon ; CIE 1931 ; plant pigments ; Riesling
• 刊名：Photosynthetica
• 出版年：2016
• 出版时间：March 2016
• 年：2016
• 卷：54
• 期：1
• 页码：101-109
• 全文大小：589 KB
• 参考文献：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.
  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.CrossRef
  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.CrossRef
  Ben Ghozlen N., Cerovic Z.G., Germain C. et al.: Nondestructive optical monitoring of grape maturation by proximal sensing. — Sensors 10: 10040–10068, 2010.CrossRef PubMed PubMedCentral
  Buschmann C.: Variability and application of the chlorophyll fluorescence emission ratio red/far-red of leaves. — Photosynth. Res. 92: 261–271, 2007.CrossRef PubMed
  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.CrossRef
  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.CrossRef
  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.CrossRef
  Coombe B.G.: Distribution of solutes within the developing grape berry in relation to its morphology. — Am. J. Enol. Viticult. 38: 120–127, 1987.
  Coombe B.G., McCarthy M.G.: Dynamics of grape berry growth and physiology of ripening. — Aust. J. Grape Wine R. 6: 131–135, 2000.CrossRef
  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.CrossRef
  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.CrossRef
  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.CrossRef
  Harris J.M., Kriedemann P.E., Possingham J.V.: Anatomical aspects of grape berry development. — Vitis 7: 106–119, 1968.
  Jackson R.S.: Wine Science. Principles and Applications, 3rd ed. Pp. 776. Elsevier, Amsterdam 2008.
  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.CrossRef
  Jones H.G., Vaugham R.A.: Remote Sensing of Vegetation. — Principles, Techniques and Applications. Pp. 400. Oxford University Press, Oxford, 2010.
  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.CrossRef
  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.CrossRef
  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.CrossRef
  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.CrossRef
  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.CrossRef
  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.CrossRef
  Lichtenthaler H.K.: Chlorophylls and carotenoids, the pigments of photosynthetic biomembranes. — Methods Enzymol. 148: 350–382, 1987.CrossRef
  Malacara D.: Color Vision and Colorimetry: Theory and Applications. Pp. 176. SPIE Press, Bellingham 2002.
  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.CrossRef
  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.CrossRef
  Mollazade K., Omid M., Arefi A.: Comparing data mining classifiers for grading raisins based on visual features. — Comput. Electron. Agr. 84: 124–131, 2012.CrossRef
  Onyango C.M., Marchant J.A.: Segmentation of row crop plants from weeds using colour and morphology. — Comput. Electron. Agr. 39: 141-155, 2003.
  Papageorgiou G.C., Govindjee: Chlorophyll a Fluorescence: A Signature of Photosynthesis. Pp. 820. Springer, Dordrecht 2004.CrossRef
  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.CrossRef
  Pieczywek P.M., Zdunek A.: Automatic classification of cells and intercellular spaces of apple tissue. — Comput. Electron. Agr. 81: 72-78, 2012.
  Possner D.R.E., Kliewer W.M.: The localization of acids, sugars, potassium and calcium in developing grape berries. — Vitis 24: 229–240, 1985.
  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.CrossRef
  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.CrossRef
  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.
  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.CrossRef
  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.CrossRef PubMed
  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.
  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.
  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.CrossRef PubMed
  Tremblay N., Wang Z., Cerovic Z.G.: Sensing crop nitrogen status with fluorescence indicators. A review. — Agron. Sustain. Dev. 32: 451–464, 2012CrossRef
  Valeur B., Berberan-Santos M.: Molecular Fluorescence -Principles and Applications. 2nd ed. Pp. 592. Wiley-VCH, Weinheim, 2012CrossRef
  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.CrossRef
  
• 作者单位：M. Navrátil (1)
  C. Buschmann (2)
  
  1. Faculty of Science, Department of Physics, University of Ostrava, Chittussiho 10, CZ-710 00, Slezská Ostrava, Czech Republic
  2. Botanical Institute, Karlsruhe Institute of Technology (KIT), Kaiserstr. 12, D-76128, Karlsruhe, Germany
  
• 刊物类别：Biomedical and Life Sciences
• 刊物主题：Life Sciences
  Plant Physiology
  
• 出版者：Springer Netherlands
• ISSN：1573-9058

文摘

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