Literature DB >> 18573364

Front face fluorescence spectroscopy and visible spectroscopy coupled with chemometrics have the potential to characterise ripening of Cabernet Franc grapes.

Marine Le Moigne1, Eric Dufour, Dominique Bertrand, Chantal Maury, Denis Seraphin, Frédérique Jourjon.   

Abstract

The potential of front-face spectroscopy for grape ripening dates discrimination was investigated on Cabernet Franc grapes from three parcels located on the Loire Valley and for six ripening dates. The 18 batches were analysed by front-face fluorescence spectroscopy and visible spectroscopy. The excitation spectra (250-310nm, emission wavelength=350nm) were characterised by a shoulder at 280nm. Grapes spectra were classified by factorial discriminant analysis (FDA). Ripening dates were well predicted by fluorescence spectra: grapes before veraison were separated from grapes after veraison and almost every ripening date was identified. The common spectroscopic space obtained by CCSWA showed that wavelengths corresponding to anthocyanin absorption in the visible were correlated to fluorescence wavelengths around the starting and ending points of the shoulder (263 and at 292nm). Then, regression models were investigated to predict total soluble solids (TSS), total acidity, malvidin-3G, total anthocyanins and total phenolics content from visible and fluorescence spectra. To predict technological indicators (TSS and total acidity), the PLS model with visible spectra (RMSECV=0.82 degrees Brix or 0.96gL(-1) H(2)SO(4)) was better than those with fluorescence one (RMSECV=1.39 degrees Brix or 2.06gL(-1) H(2)SO(4)). For malvidin-3G and total anthocyanins, all R(c)(2) and R(cv)(2) were superior to 0.90 and RMSECV were low. Visible and fluorescence spectroscopies succeeded in predicting anthocyanin content. Concerning total phenolic, the best prediction was provided by fluorescence spectroscopy.

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Year:  2007        PMID: 18573364     DOI: 10.1016/j.aca.2007.09.054

Source DB:  PubMed          Journal:  Anal Chim Acta        ISSN: 0003-2670            Impact factor:   6.558


  2 in total

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Authors:  Mohsen Kompany-Zareh; Somayyeh Akbarian; Mohammad Mahdi Najafpour
Journal:  Sci Rep       Date:  2022-10-07       Impact factor: 4.996

2.  Application of Hyperspectral Imaging and Deep Learning for Robust Prediction of Sugar and pH Levels in Wine Grape Berries.

Authors:  Véronique Gomes; Ana Mendes-Ferreira; Pedro Melo-Pinto
Journal:  Sensors (Basel)       Date:  2021-05-15       Impact factor: 3.576

  2 in total

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